From 66ee132b04dc5ca411e6a7b3708b81c260184e19 Mon Sep 17 00:00:00 2001 From: Omer-alt Date: Thu, 23 Jul 2026 08:33:22 +0100 Subject: [PATCH 1/5] Add reproducible Yaounde boundary and 30 m grid pipeline --- .gitignore | 12 + Makefile | 28 + checksums.txt | 3 + configs/study_area.yaml | 114 +++ data/metadata/adm2_adm3_comparison.json | 9 + data/metadata/boundary_report.json | 83 ++ data/metadata/grid_specification.json | 31 + data/metadata/source_boundary_inspection.json | 53 ++ .../source_boundary_inspection_adm2.json | 48 + .../source_boundary_inspection_adm3.json | 53 ++ .../geoBoundaries-CMR-ADM2.geojson | 66 ++ .../geoBoundaries-CMR-ADM3.geojson | 366 ++++++++ .../geoboundaries_cmr_adm2_metadata.json | 15 + .../geoboundaries_cmr_adm3_metadata.json | 15 + data/raw/boundaries/hdx_cod_ab_cmr/README.md | 51 ++ .../hdx_cod_ab_cmr/cod_ab_cmr_metadata.json | 15 + pyproject.toml | 22 + reports/day1/01_boundary_overview.png | Bin 0 -> 221858 bytes reports/day1/02_adm2_adm3_comparison.png | Bin 0 -> 182630 bytes reports/day1/03_core_context_masks.png | Bin 0 -> 126346 bytes reports/day1/04_grid_alignment_zoom.png | Bin 0 -> 110500 bytes src/analysis/boundaries/build_study_grid.py | 818 ++++++++++++++++++ src/analysis/boundaries/inspect_boundaries.py | 114 +++ .../boundaries/visualize_study_grid.py | 510 +++++++++++ tests/reference/grid_v1.json | 31 + tests/test_study_grid.py | 482 +++++++++++ 26 files changed, 2939 insertions(+) create mode 100644 Makefile create mode 100644 checksums.txt create mode 100644 configs/study_area.yaml create mode 100644 data/metadata/adm2_adm3_comparison.json create mode 100644 data/metadata/boundary_report.json create mode 100644 data/metadata/grid_specification.json create mode 100644 data/metadata/source_boundary_inspection.json create mode 100644 data/metadata/source_boundary_inspection_adm2.json create mode 100644 data/metadata/source_boundary_inspection_adm3.json create mode 100644 data/raw/boundaries/geoboundaries/geoBoundaries-CMR-ADM2.geojson create mode 100644 data/raw/boundaries/geoboundaries/geoBoundaries-CMR-ADM3.geojson create mode 100644 data/raw/boundaries/geoboundaries/geoboundaries_cmr_adm2_metadata.json create mode 100644 data/raw/boundaries/geoboundaries/geoboundaries_cmr_adm3_metadata.json create mode 100644 data/raw/boundaries/hdx_cod_ab_cmr/README.md create mode 100644 data/raw/boundaries/hdx_cod_ab_cmr/cod_ab_cmr_metadata.json create mode 100644 reports/day1/01_boundary_overview.png create mode 100644 reports/day1/02_adm2_adm3_comparison.png create mode 100644 reports/day1/03_core_context_masks.png create mode 100644 reports/day1/04_grid_alignment_zoom.png create mode 100644 src/analysis/boundaries/build_study_grid.py create mode 100644 src/analysis/boundaries/inspect_boundaries.py create mode 100644 src/analysis/boundaries/visualize_study_grid.py create mode 100644 tests/reference/grid_v1.json create mode 100644 tests/test_study_grid.py diff --git a/.gitignore b/.gitignore index 39ede04..1fd9b41 100644 --- a/.gitignore +++ b/.gitignore @@ -61,3 +61,15 @@ ENV/ .cache/ tmp/ temp/ + +# Large files +*.tif +*.parquet +*.gpkg +cmr_admin_boundaries.geojson.zip + +# Virtual environments +.venv/ +.venvt/ +venv/ +env/ \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..f5501bb --- /dev/null +++ b/Makefile @@ -0,0 +1,28 @@ +.PHONY: help install inspect-adm2 inspect-adm3 inspect-boundaries build-grid test-grid day1 + +help: ## Display this help with a description of all available commands + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-20s\033[0m %s\n", $$1, $$2}' + +install: ## Install the package in development mode with dev dependencies + python -m pip install -e ".[dev]" + +inspect-adm2: ## Inspect the ADM2 layer and generate the inspection report + python -m src.analysis.boundaries.inspect_boundaries \ + --input data/raw/boundaries/hdx_cod_ab_cmr/extracted/cmr_admin2.geojson \ + --report data/metadata/source_boundary_inspection_adm2.json + +inspect-adm3: ## Inspect the ADM3 layer and generate the inspection report + python -m src.analysis.boundaries.inspect_boundaries \ + --input data/raw/boundaries/hdx_cod_ab_cmr/extracted/cmr_admin3.geojson \ + --report data/metadata/source_boundary_inspection_adm3.json + +inspect-boundaries: inspect-adm2 inspect-adm3 ## Run both ADM2 and ADM3 inspections + +build-grid: ## Build the study grid from the YAML configuration + python -m src.analysis.boundaries.build_study_grid \ + --config configs/study_area.yaml + +test-grid: ## Run pytest on the study grid module + pytest tests/test_study_grid.py + +day1: inspect-boundaries build-grid test-grid ## Run the complete Day 1 pipeline (inspection, grid build, tests) \ No newline at end of file diff --git a/checksums.txt b/checksums.txt new file mode 100644 index 0000000..8788532 --- /dev/null +++ b/checksums.txt @@ -0,0 +1,3 @@ +f24a75c41ee2e107448ff1d2b8769ee970dad376d20cd7093da0c9106814ab5a data/raw/boundaries/hdx_cod_ab_cmr/cmr_admin_boundaries.geojson.zip +9d0ff998057c3afe7f3a6334d71e9d7c353c64fe6e7c35b01fe14d804c97f04b data/raw/boundaries/geoboundaries/geoBoundaries-CMR-ADM3.geojson +81b6159418f9b8d8341822d7bcfd9d42d31c893492ce336f5f629ca08bf939fb data/raw/boundaries/geoboundaries/geoBoundaries-CMR-ADM2.geojson diff --git a/configs/study_area.yaml b/configs/study_area.yaml new file mode 100644 index 0000000..59db90a --- /dev/null +++ b/configs/study_area.yaml @@ -0,0 +1,114 @@ +study_area: + country: Cameroon + city: Yaounde + iso3: CMR + +boundary: + source_name: HDX_COD_AB_CMR + + # Main source used to build the Yaoundé core + source_file: data/raw/boundaries/hdx_cod_ab_cmr/extracted/cmr_admin3.geojson + + # ADM2 source used to compare the union of the seven districts + # with the Mfoundi department + comparison_file: data/raw/boundaries/hdx_cod_ab_cmr/extracted/cmr_admin2.geojson + + # Metadata and checksum of the raw source file + metadata_file: data/raw/boundaries/hdx_cod_ab_cmr/cod_ab_cmr_metadata.json + checksum_file: checksums.txt + + # GeoJSON: no internal layer to specify + layer: null + + administrative_level: 3 + + # Field containing the French/main name of ADM3 + name_field: adm3_name1 + + # Exact values observed in the source file + selected_names: + - "Yaounde 1er" + - "Yaounde 2e" + - "Yaounde 3e" + - "Yaounde 4e" + - "Yaounde 5e" + - "Yaounde 6e" + - "Yaounde 7e" + + # Field identifying the parent department + parent_field: adm2_name1 + parent_name: Mfoundi + + # Stable field allowing optional selection by P-code + pcode_field: adm3_pcode + selected_pcodes: + - CM002007001 + - CM002007002 + - CM002007003 + - CM002007004 + - CM002007005 + - CM002007006 + - CM002007007 + + parent_pcode_field: adm2_pcode + parent_pcode: CM002007 + + expected_selected_units: 7 + geometry_fix: make_valid + dissolve_selected_units: true + +comparison_boundary: + administrative_level: 2 + name_field: adm2_name1 + selected_name: Mfoundi + pcode_field: adm2_pcode + selected_pcode: CM002007 + +projection: + geographic_crs: EPSG:4326 + analysis_crs: EPSG:32632 + +geometries: + context_buffer_m: 5000 + create_convex_hull: true + +grid: + grid_id: yaounde_epsg32632_30m_anchor0_v1 + version: 1 + + crs: EPSG:32632 + resolution_m: 30 + anchor_x_m: 0 + anchor_y_m: 0 + inclusion_rule: pixel_center + extent_geometry: context_buffer + nodata: 0 + + # Used only for visual zoom. + visual_zoom_half_window_m: 300 + + # To be activated after the grid has been officially approved. + reference_specification: tests/reference/grid_v1.json + +quality_thresholds: + adm2_adm3_max_symmetric_difference_ratio: 0.02 + max_vector_raster_area_difference_ratio: 0.02 + +outputs: + core_boundary: data/processed/boundaries/yaounde_core.gpkg + context_boundary: data/processed/boundaries/yaounde_context_buffer.gpkg + convex_hull: data/processed/boundaries/yaounde_convex_hull.gpkg + + adm2_reference_boundary: data/processed/boundaries/mfoundi_adm2_reference.gpkg + boundary_comparison_metrics: data/metadata/adm2_adm3_comparison.json + visual_report_dir: reports/day1 + + grid_template: data/processed/grid/yaounde_grid_30m_template.tif + core_mask: data/processed/grid/yaounde_core_mask_30m.tif + context_mask: data/processed/grid/yaounde_context_mask_30m.tif + cell_table: data/processed/grid/yaounde_cells_30m.parquet + + adm2_inspection_report: data/metadata/source_boundary_inspection_adm2.json + adm3_inspection_report: data/metadata/source_boundary_inspection_adm3.json + boundary_report: data/metadata/boundary_report.json + grid_specification: data/metadata/grid_specification.json \ No newline at end of file diff --git a/data/metadata/adm2_adm3_comparison.json b/data/metadata/adm2_adm3_comparison.json new file mode 100644 index 0000000..96c9b20 --- /dev/null +++ b/data/metadata/adm2_adm3_comparison.json @@ -0,0 +1,9 @@ +{ + "adm3_union_area_km2": 289.72898706712, + "adm2_mfoundi_area_km2": 289.7289870671199, + "intersection_area_km2": 289.7289870671193, + "symmetric_difference_area_km2": 0.0, + "symmetric_difference_ratio": 0.0, + "adm2_covers_adm3_union": true, + "adm3_union_covers_adm2": true +} \ No newline at end of file diff --git a/data/metadata/boundary_report.json b/data/metadata/boundary_report.json new file mode 100644 index 0000000..21a53fb --- /dev/null +++ b/data/metadata/boundary_report.json @@ -0,0 +1,83 @@ +{ + "source_file": "data/raw/boundaries/hdx_cod_ab_cmr/extracted/cmr_admin3.geojson", + "source_sha256": "9d7844198b9fcc00509cf916dabbbe616c803774f445c4f9d70736eef20a291c", + "input_crs": "EPSG:4326", + "analysis_crs": "EPSG:32632", + "input_feature_count": 360, + "selected_feature_count": 7, + "selected_names": [ + "Yaounde 1er", + "Yaounde 2e", + "Yaounde 3e", + "Yaounde 4e", + "Yaounde 5e", + "Yaounde 6e", + "Yaounde 7e" + ], + "selected_pcodes": [ + "CM002007001", + "CM002007002", + "CM002007003", + "CM002007004", + "CM002007005", + "CM002007006", + "CM002007007" + ], + "parent_name": "Mfoundi", + "missing_geometry_count_before_fix": 0, + "empty_geometry_count_before_fix": 0, + "invalid_geometry_count_before_fix": 0, + "duplicate_geometry_count_before_fix": 0, + "geometry_types_before_fix": { + "Polygon": 357, + "MultiPolygon": 3 + }, + "geometry_fix_operation": "make_valid", + "invalid_core_after_fix": false, + "core_area_vector_km2": 289.72898706712, + "core_area_rasterised_km2": 289.7181, + "relative_vector_raster_area_difference": 3.757672723818094e-05, + "context_area_km2": 791.7075129573457, + "convex_hull_area_km2": 378.9235951778433, + "core_within_context": true, + "adm2_adm3_comparison": { + "adm3_union_area_km2": 289.72898706712, + "adm2_mfoundi_area_km2": 289.7289870671199, + "intersection_area_km2": 289.7289870671193, + "symmetric_difference_area_km2": 0.0, + "symmetric_difference_ratio": 0.0, + "adm2_covers_adm3_union": true, + "adm3_union_covers_adm2": true + }, + "grid_specification": { + "grid_id": "yaounde_epsg32632_30m_anchor0_v1", + "grid_version": 1, + "crs": "EPSG:32632", + "epsg": 32632, + "resolution_m": 30.0, + "anchor_x_m": 0.0, + "anchor_y_m": 0.0, + "extent": { + "xmin": 762570.0, + "ymin": 405810.0, + "xmax": 790950.0, + "ymax": 444150.0 + }, + "width": 946, + "height": 1278, + "transform": [ + 30.0, + 0.0, + 762570.0, + 0.0, + -30.0, + 444150.0 + ], + "inclusion_rule": "pixel_center", + "extent_geometry": "context_buffer", + "context_buffer_m": 5000.0, + "context_cell_count": 879666, + "core_cell_count": 321909, + "cell_id_pattern": "EPSG32632_30m__" + } +} \ No newline at end of file diff --git a/data/metadata/grid_specification.json b/data/metadata/grid_specification.json new file mode 100644 index 0000000..e73f5a4 --- /dev/null +++ b/data/metadata/grid_specification.json @@ -0,0 +1,31 @@ +{ + "grid_id": "yaounde_epsg32632_30m_anchor0_v1", + "grid_version": 1, + "crs": "EPSG:32632", + "epsg": 32632, + "resolution_m": 30.0, + "anchor_x_m": 0.0, + "anchor_y_m": 0.0, + "extent": { + "xmin": 762570.0, + "ymin": 405810.0, + "xmax": 790950.0, + "ymax": 444150.0 + }, + "width": 946, + "height": 1278, + "transform": [ + 30.0, + 0.0, + 762570.0, + 0.0, + -30.0, + 444150.0 + ], + "inclusion_rule": "pixel_center", + "extent_geometry": "context_buffer", + "context_buffer_m": 5000.0, + "context_cell_count": 879666, + "core_cell_count": 321909, + "cell_id_pattern": "EPSG32632_30m__" +} \ No newline at end of file diff --git a/data/metadata/source_boundary_inspection.json b/data/metadata/source_boundary_inspection.json new file mode 100644 index 0000000..0cda3b7 --- /dev/null +++ b/data/metadata/source_boundary_inspection.json @@ -0,0 +1,53 @@ +{ + "file": "data/raw/boundaries/hdx_cod_ab_cmr/extracted/cmr_admin3.geojson", + "feature_count": 360, + "crs": "EPSG:4326", + "columns": [ + "adm3_name", + "adm3_name1", + "adm3_name2", + "adm3_name3", + "adm3_pcode", + "adm2_name", + "adm2_name1", + "adm2_name2", + "adm2_name3", + "adm2_pcode", + "adm1_name", + "adm1_name1", + "adm1_name2", + "adm1_name3", + "adm1_pcode", + "adm0_name", + "adm0_name1", + "adm0_name2", + "adm0_name3", + "adm0_pcode", + "valid_on", + "valid_to", + "area_sqkm", + "version", + "lang", + "lang1", + "lang2", + "lang3", + "adm3_ref_name1", + "center_lat", + "center_lon", + "geometry" + ], + "geometry_types": { + "Polygon": 357, + "MultiPolygon": 3 + }, + "missing_geometry_count": 0, + "empty_geometry_count": 0, + "invalid_geometry_count": 0, + "duplicate_geometry_count": 0, + "bounds": [ + 8.498952800000097, + 1.654665900000111, + 16.19214760000017, + 13.083333000000152 + ] +} \ No newline at end of file diff --git a/data/metadata/source_boundary_inspection_adm2.json b/data/metadata/source_boundary_inspection_adm2.json new file mode 100644 index 0000000..16aecc4 --- /dev/null +++ b/data/metadata/source_boundary_inspection_adm2.json @@ -0,0 +1,48 @@ +{ + "file": 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"2025-08-26T12:50:25", + "metadata_modified": "2026-03-07T09:27:23.798691", + "microdata": false, + "dataset_id": "59c145a6-7ede-4021-96b0-6aff58296d34", + "resource_type": "api", + "name": "geoBoundaries-CMR-ADM2.geojson", + "mimetype": null, + "size": "225.6K" +} \ No newline at end of file diff --git a/data/raw/boundaries/geoboundaries/geoboundaries_cmr_adm3_metadata.json b/data/raw/boundaries/geoboundaries/geoboundaries_cmr_adm3_metadata.json new file mode 100644 index 0000000..96150d2 --- /dev/null +++ b/data/raw/boundaries/geoboundaries/geoboundaries_cmr_adm3_metadata.json @@ -0,0 +1,15 @@ +{ + "created": "2022-01-06T14:04:46.866620", + "description": "GeoJSON ADM3 boundaries for Cameroon", + "format": "GeoJSON", + "download_url": "https://github.com/wmgeolab/geoBoundaries/raw/9469f09/releaseData/gbOpen/CMR/ADM3/geoBoundaries-CMR-ADM3.geojson", + "id": "3104e70e-5085-4bb6-ad26-f1420479aa6c", + "last_modified": "2025-08-26T12:50:26", + "metadata_modified": "2025-08-26T13:08:23.230168", + "microdata": false, + "dataset_id": "59c145a6-7ede-4021-96b0-6aff58296d34", + "resource_type": "api", + "name": "geoBoundaries-CMR-ADM3.geojson", + "mimetype": null, + "size": "4.9M" +} \ No newline at end of file diff --git a/data/raw/boundaries/hdx_cod_ab_cmr/README.md b/data/raw/boundaries/hdx_cod_ab_cmr/README.md new file mode 100644 index 0000000..77fbd2e --- /dev/null +++ b/data/raw/boundaries/hdx_cod_ab_cmr/README.md @@ -0,0 +1,51 @@ +# Boundary source record + +## Dataset + +- **Dataset:** Cameroon - Subnational Administrative Boundaries +- **Dataset identifier:** `cod-ab-cmr` +- **Provider:** OCHA Field Information Services Section +- **Original source:** Institut National de Cartographie, Cameroon +- **Dataset type:** Common Operational Dataset – Administrative Boundaries +- **Version:** `v01` +- **Administrative coverage:** ADM0–ADM3 +- **Units:** 10 ADM1 regions, 58 ADM2 departments and 360 ADM3 arrondissements +- **Boundary valid from:** 2019-01-04 +- **Dataset review date:** 2025-10-30 +- **Resource modification date:** 2026-01-26 +- **Download date:** 2026-07-22 + +## Downloaded resource + +- **Filename:** `cmr_admin_boundaries.geojson.zip` +- **Format:** GeoJSON ZIP archive +- **Resource ID:** `2248ac6d-9675-47de-90d3-1e7ce6e601b1` +- **Approximate size:** 10.5 MB +- **Raw archive modified after download:** No +- **Raw archive SHA-256:** `f24a75c41ee2e107448ff1d2b8769ee970dad376d20cd7093da0c9106814ab5a` + +The extracted `cmr_admin2.geojson` and `cmr_admin3.geojson` files are retained +without manual geometry editing. + +## Licence and attribution + +- **Licence:** Creative Commons Attribution 4.0 International (`CC BY 4.0`) +- **Required attribution:** OCHA Field Information Services Section and + Institut National de Cartographie, Cameroon +- **Source page:** `https://data.humdata.org/dataset/cod-ab-cmr` + +## Project use + +The seven Mfoundi ADM3 units identified by P-codes +`CM002007001`–`CM002007007` are dissolved to construct the Yaoundé +administrative core. Their union is compared with the ADM2 Mfoundi boundary +(`CM002007`). + +geoBoundaries ADM2 and ADM3 files are used only as independent cross-check +sources and do not define the final study boundary. + +## Processing policy + +The raw archive is preserved unchanged. Selection, validation, geometry repair, +dissolution, reprojection, buffering, rasterisation and grid construction are +performed reproducibly in code using `configs/study_area.yaml`. \ No newline at end of file diff --git a/data/raw/boundaries/hdx_cod_ab_cmr/cod_ab_cmr_metadata.json b/data/raw/boundaries/hdx_cod_ab_cmr/cod_ab_cmr_metadata.json new file mode 100644 index 0000000..ea5a34e --- /dev/null +++ b/data/raw/boundaries/hdx_cod_ab_cmr/cod_ab_cmr_metadata.json @@ -0,0 +1,15 @@ +{ + "created": "2025-11-03T16:00:08.712440", + "description": "Cameroon administrative level 0-3 boundaries (COD-AB), GeoJSON", + "format": "GeoJSON", + "download_url": "https://data.humdata.org/dataset/b13f08ef-92ee-4446-9b0a-e219f5c25415/resource/2248ac6d-9675-47de-90d3-1e7ce6e601b1/download/cmr_admin_boundaries.geojson.zip", + "id": "2248ac6d-9675-47de-90d3-1e7ce6e601b1", + "last_modified": "2026-01-26T15:23:52.278273", + "metadata_modified": "2026-03-10T08:40:45.192138", + "microdata": false, + "dataset_id": "b13f08ef-92ee-4446-9b0a-e219f5c25415", + "resource_type": "file.upload", + "name": "cmr_admin_boundaries.geojson.zip", + "mimetype": "application/zip", + "size": "10.5M" +} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 91e58c3..095bfa4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,14 +23,26 @@ dependencies = [ "numpy>=1.26", "pandas>=2.2", "scipy>=1.11", + + # Data structures "xarray>=2024.6.0", + "pyarrow>=25.0", + + # Geospatial "rasterio>=1.3.10", "geopandas>=0.14", "shapely>=2.0", "pyproj>=3.6", + + # Configuration + "PyYAML>=6.0", + + # Machine Learning "scikit-learn>=1.4", "torch>=2.3", "gpytorch>=1.12", + + # Visualization "matplotlib>=3.8", "seaborn>=0.13" ] @@ -42,6 +54,16 @@ dev = [ "black>=24.4" ] +notebooks = [ + "jupyter", + "ipykernel", +] + +docs = [ + "mkdocs", + "mkdocs-material", +] + [tool.ruff] line-length = 100 target-version = "py310" diff --git a/reports/day1/01_boundary_overview.png 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b/reports/day2/day2_landsat_report.md new file mode 100644 index 0000000..8e38f0c --- /dev/null +++ b/reports/day2/day2_landsat_report.md @@ -0,0 +1,7 @@ +# Day 2 — Landsat catalogue report + +- Selected common window: `Jul-Sep_3m` +- Diagnostic scene records: 521 +- Frozen scene records: 7 + +Day 2 freezes exact Earth Engine asset IDs and does not generate composites. diff --git a/src/analysis/landsat/__init__.py b/src/analysis/landsat/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/analysis/landsat/build_catalog.py b/src/analysis/landsat/build_catalog.py new file mode 100644 index 0000000..15a5f6f --- /dev/null +++ b/src/analysis/landsat/build_catalog.py @@ -0,0 +1,654 @@ +"""Build and freeze the Day 2 Landsat scene catalogue without composites.""" + +from __future__ import annotations + +import argparse +import calendar +import json +import random +import time +from dataclasses import dataclass +from datetime import date +from pathlib import Path +from typing import Any + +import ee +import pandas as pd +import yaml + +from .common import ( + find_project_root, + initialize_earth_engine, + load_ee_geometry, + load_grid_specification, + load_json, + load_yaml, + query_rectangle, + resolve_project_path, + sha256_file, +) +from .qa_masks import landsat_valid_mask + + +@dataclass(frozen=True) +class Window: + """Describe one consecutive calendar window.""" + + name: str + months: int + start_month: int + + +def sensor_policy(config: dict[str, Any], epoch: int) -> dict[str, list[str]]: + """Return normalized primary, supplemental and diagnostic sensor lists.""" + raw = config["epoch_sensor_policy"].get(epoch) + if raw is None: + raw = config["epoch_sensor_policy"][str(epoch)] + return {key: list(raw.get(key, [])) for key in ( + "primary", "supplemental", "diagnostic_only" + )} + + +def diagnostic_range(epoch: int, config: dict[str, Any]) -> tuple[str, str]: + """Return the three-year half-open diagnostic date range for one epoch.""" + before = int(config["catalog"]["diagnostic_years_before"]) + after = int(config["catalog"]["diagnostic_years_after"]) + return f"{epoch-before}-01-01", f"{epoch+after+1}-01-01" + + +def safe_number(result: ee.Dictionary, key: str) -> ee.Number: + """Read a reducer result while replacing an absent key with zero.""" + return ee.Number(ee.Algorithms.If(result.contains(key), result.get(key), 0)) + + +def valid_area( + mask: ee.Image, + geometry: ee.Geometry, + grid: dict[str, Any], + statistics: dict[str, Any], +) -> ee.Number: + """Calculate valid area on the exact frozen Day 1 affine grid.""" + result = ee.Image.pixelArea().updateMask(mask).rename("area").reduceRegion( + reducer=ee.Reducer.sum(), + geometry=geometry, + crs=grid["crs"], + crsTransform=grid["transform"], + maxPixels=int(statistics["max_pixels"]), + tileScale=int(statistics["tile_scale"]), + ) + return safe_number(ee.Dictionary(result), "area") + + +def scene_feature( + image: ee.Image, + epoch: int, + sensor: str, + role: str, + config: dict[str, Any], + grid: dict[str, Any], + core: ee.Geometry, + context: ee.Geometry, + core_area_m2: float, + context_area_m2: float, +) -> ee.Feature: + """Convert one scene into a complete manifest row.""" + image = ee.Image(image) + mask = landsat_valid_mask(image, sensor, config["collections"], config["quality_mask"]) + core_valid = valid_area(mask, core, grid, config["statistics"]) + context_valid = valid_area(mask, context, grid, config["statistics"]) + acquired = ee.Date(image.get("system:time_start")) + collection_id = config["collections"][sensor]["id"] + system_index = ee.String(image.get("system:index")) + asset_id = ee.String(collection_id).cat("/").cat(system_index) + slc_off = sensor == "LE07" and True + + return ee.Feature(None, { + "epoch": epoch, + "relative_year": acquired.get("year").subtract(epoch), + "sensor_key": sensor, + "sensor_role": role, + "collection_id": collection_id, + "earth_engine_asset_id": asset_id, + "system_index": system_index, + "landsat_product_id": image.get("LANDSAT_PRODUCT_ID"), + "landsat_scene_id": image.get("LANDSAT_SCENE_ID"), + "acquisition_date": acquired.format("YYYY-MM-dd"), + "acquisition_year": acquired.get("year"), + "acquisition_month": acquired.get("month"), + "day_of_year": acquired.getRelative("day", "year").add(1), + "wrs_path": image.get("WRS_PATH"), + "wrs_row": image.get("WRS_ROW"), + "cloud_cover_scene_pct": image.get("CLOUD_COVER"), + "cloud_cover_land_pct": image.get("CLOUD_COVER_LAND"), + "valid_area_core_m2": core_valid, + "valid_area_context_m2": context_valid, + "valid_fraction_core": core_valid.divide(core_area_m2), + "valid_fraction_context": context_valid.divide(context_area_m2), + "slc_off": ee.Algorithms.If( + slc_off, acquired.millis().gt(ee.Date("2003-05-31").millis()), False + ), + "landsat7_orbit_drift_period": ee.Algorithms.If( + sensor == "LE07", + acquired.millis().gt(ee.Date("2017-02-07").millis()), + False, + ), + "has_positive_core_coverage": core_valid.gt(0), + "candidate_for_composite": core_valid.gt(0).And( + ee.String(role).compareTo("diagnostic_only").neq(0) + ), + }) + + +def is_retryable_earth_engine_error(error: Exception) -> bool: + """Return whether an Earth Engine error is likely to succeed on retry. + + Retries are reserved for temporary quota, service and transport failures. + Invalid arguments and deterministic computation errors are raised + immediately so genuine bugs are not hidden. + """ + message = str(error).casefold() + retryable_fragments = ( + "too many concurrent aggregations", + "quota exceeded", + "http 429", + "rate limit", + "internal error", + "service unavailable", + "deadline exceeded", + "timed out", + ) + return any(fragment in message for fragment in retryable_fragments) + + +def get_info_with_retry( + computed_object: ee.ComputedObject, + *, + description: str, + maximum_attempts: int = 7, + initial_delay_seconds: float = 3.0, +) -> Any: + """Evaluate one small Earth Engine request with exponential backoff. + + The function must be used only after the computation has been split into + small requests. Retrying one very large request would reproduce the same + aggregation fan-out and would not address the root cause. + """ + for attempt in range(1, maximum_attempts + 1): + try: + return computed_object.getInfo() + except Exception as error: + if ( + attempt == maximum_attempts + or not is_retryable_earth_engine_error(error) + ): + raise RuntimeError( + f"Earth Engine failed while {description}." + ) from error + + delay = ( + initial_delay_seconds * (2 ** (attempt - 1)) + + random.uniform(0.0, 1.0) + ) + print( + f"Temporary Earth Engine error while {description}. " + f"Retrying in {delay:.1f} seconds " + f"({attempt}/{maximum_attempts})..." + ) + time.sleep(delay) + + raise AssertionError("Unreachable retry state.") + + +def normalize_manifest(frame: pd.DataFrame) -> pd.DataFrame: + """Normalize manifest data types and apply deterministic row ordering.""" + if frame.empty: + raise RuntimeError( + "The Earth Engine query returned no Landsat scenes." + ) + + result = frame.copy() + result["acquisition_date"] = pd.to_datetime( + result["acquisition_date"], + errors="raise", + ) + + integer_columns = ( + "epoch", + "relative_year", + "acquisition_year", + "acquisition_month", + ) + for column in integer_columns: + result[column] = pd.to_numeric( + result[column], + errors="coerce", + ).astype("Int64") + + numeric_columns = ( + "valid_area_core_m2", + "valid_area_context_m2", + "valid_fraction_core", + "valid_fraction_context", + "cloud_cover_scene_pct", + "cloud_cover_land_pct", + ) + for column in numeric_columns: + if column in result.columns: + result[column] = pd.to_numeric( + result[column], + errors="coerce", + ) + + boolean_columns = ( + "slc_off", + "landsat7_orbit_drift_period", + "has_positive_core_coverage", + "candidate_for_composite", + ) + for column in boolean_columns: + if column in result.columns: + result[column] = result[column].fillna(False).astype(bool) + + key = ( + result["epoch"].astype(str) + + "::" + + result["earth_engine_asset_id"].astype(str) + ) + if not key.is_unique: + raise ValueError( + "Duplicate epoch/asset records were produced in the manifest." + ) + + return result.sort_values( + ["epoch", "acquisition_date", "sensor_key", "system_index"] + ).reset_index(drop=True) + + +def load_manifest_checkpoint(checkpoint_path: Path) -> pd.DataFrame: + """Load a partial manifest so interrupted catalogue runs can resume.""" + if not checkpoint_path.is_file(): + return pd.DataFrame() + + frame = pd.read_csv(checkpoint_path) + if "acquisition_date" in frame.columns: + frame["acquisition_date"] = pd.to_datetime( + frame["acquisition_date"], + errors="coerce", + ) + + boolean_columns = ( + "slc_off", + "landsat7_orbit_drift_period", + "has_positive_core_coverage", + "candidate_for_composite", + ) + for column in boolean_columns: + if column in frame.columns: + frame[column] = ( + frame[column] + .astype(str) + .str.casefold() + .map({"true": True, "false": False}) + .fillna(False) + ) + + return frame + + +def write_manifest_checkpoint( + rows: list[dict[str, Any]], + checkpoint_path: Path, +) -> None: + """Persist completed scene rows after each small Earth Engine request.""" + checkpoint_path.parent.mkdir(parents=True, exist_ok=True) + pd.DataFrame(rows).to_csv( + checkpoint_path, + index=False, + date_format="%Y-%m-%d", + ) + + +def build_manifest( + config: dict[str, Any], + grid: dict[str, Any], + core: ee.Geometry, + context: ee.Geometry, + query_geom: ee.Geometry, + core_area_m2: float, + context_area_m2: float, + checkpoint_path: Path, +) -> pd.DataFrame: + """Build the manifest sequentially to avoid aggregation fan-out. + + Earth Engine raises ``Too many concurrent aggregations`` when a mapped + collection contains a regional reduction for every image and the complete + graph is evaluated at once. This implementation first retrieves scene + indexes, then evaluates one scene feature per request. Each request contains + only the core and context reductions for that single scene. + """ + checkpoint = load_manifest_checkpoint(checkpoint_path) + rows = checkpoint.to_dict(orient="records") + completed = { + (int(row["epoch"]), str(row["earth_engine_asset_id"])) + for row in rows + if pd.notna(row.get("epoch")) + and pd.notna(row.get("earth_engine_asset_id")) + } + + for epoch_value in config["catalog"]["epochs"]: + epoch = int(epoch_value) + policy = sensor_policy(config, epoch) + start_date, end_date = diagnostic_range(epoch, config) + + for role in ("primary", "supplemental", "diagnostic_only"): + for sensor in policy[role]: + collection_id = config["collections"][sensor]["id"] + collection = ( + ee.ImageCollection(collection_id) + .filterDate(start_date, end_date) + .filterBounds(query_geom) + .sort("system:time_start") + ) + system_indexes = get_info_with_retry( + collection.aggregate_array("system:index"), + description=( + f"listing scenes for epoch {epoch}, sensor {sensor}" + ), + ) or [] + + total = len(system_indexes) + print( + f"Epoch {epoch}, {sensor} ({role}): " + f"{total} diagnostic scenes." + ) + + for position, system_index in enumerate( + system_indexes, + start=1, + ): + asset_id = f"{collection_id}/{system_index}" + manifest_key = (epoch, asset_id) + + if manifest_key in completed: + continue + + feature = scene_feature( + ee.Image(asset_id), + epoch, + sensor, + role, + config, + grid, + core, + context, + core_area_m2, + context_area_m2, + ) + properties = get_info_with_retry( + feature.toDictionary(), + description=( + f"computing scene {position}/{total} " + f"for epoch {epoch}, sensor {sensor}" + ), + ) + rows.append(dict(properties)) + completed.add(manifest_key) + write_manifest_checkpoint(rows, checkpoint_path) + + print( + f" Completed {position}/{total}: {system_index}" + ) + + manifest = normalize_manifest(pd.DataFrame(rows)) + manifest.to_csv( + checkpoint_path, + index=False, + date_format="%Y-%m-%d", + ) + return manifest + +def monthly_summary(manifest: pd.DataFrame) -> pd.DataFrame: + """Summarize scene count and scene-level valid coverage by month and sensor.""" + return ( + manifest.groupby(["epoch", "acquisition_month", "sensor_key"], as_index=False) + .agg( + scene_count=("earth_engine_asset_id", "count"), + positive_core_scene_count=("has_positive_core_coverage", "sum"), + median_valid_fraction_core=("valid_fraction_core", "median"), + maximum_valid_fraction_core=("valid_fraction_core", "max"), + median_valid_fraction_context=("valid_fraction_context", "median"), + median_cloud_cover_scene_pct=("cloud_cover_scene_pct", "median"), + ) + ) + + +def candidate_windows(config: dict[str, Any]) -> list[Window]: + """Generate every configured consecutive calendar window.""" + windows: list[Window] = [] + allow_cross = bool(config["candidate_windows"]["allow_cross_year_windows"]) + for length in config["candidate_windows"]["lengths_months"]: + for start_month in range(1, 13): + if not allow_cross and start_month + int(length) - 1 > 12: + continue + end_month = ((start_month + int(length) - 2) % 12) + 1 + windows.append(Window( + name=f"{calendar.month_abbr[start_month]}-{calendar.month_abbr[end_month]}_{length}m", + months=int(length), + start_month=start_month, + )) + return windows + + +def exact_window_dates(epoch: int, window: Window) -> tuple[pd.Timestamp, pd.Timestamp]: + """Convert a calendar window to exact half-open dates for one epoch.""" + crosses = window.start_month + window.months - 1 > 12 + start_year = epoch - 1 if crosses else epoch + start = pd.Timestamp(date(start_year, window.start_month, 1)) + end = start + pd.DateOffset(months=window.months) + return start, end + + +def evaluate_windows(manifest: pd.DataFrame, config: dict[str, Any]) -> pd.DataFrame: + """Evaluate candidate windows using a fast manifest-derived approximation. + + This first implementation ranks windows from scene-level QA coverage and + freezes exact scene IDs. Day 3 can recompute full observation-depth rasters + for the selected window only, avoiding hundreds of expensive reductions. + """ + records: list[dict[str, Any]] = [] + for window in candidate_windows(config): + for epoch in config["catalog"]["epochs"]: + policy = sensor_policy(config, int(epoch)) + modes = [("primary_only", policy["primary"])] + if policy["supplemental"]: + modes.append(("primary_plus_supplemental", policy["primary"] + policy["supplemental"])) + start, end = exact_window_dates(int(epoch), window) + for mode, sensors in modes: + rows = manifest[ + (manifest["epoch"] == int(epoch)) + & manifest["sensor_key"].isin(sensors) + & (manifest["acquisition_date"] >= start) + & (manifest["acquisition_date"] < end) + & manifest["has_positive_core_coverage"].astype(bool) + ] + combined = 0.0 if rows.empty else 1 - float( + (1 - rows["valid_fraction_core"].clip(0, 1)).prod() + ) + depth3 = 0.0 if len(rows) < 3 else float( + rows["valid_fraction_core"].nlargest(3).mean() + ) + records.append({ + "epoch": int(epoch), + "window_name": window.name, + "number_of_months": window.months, + "window_start": start.date().isoformat(), + "window_end": end.date().isoformat(), + "sensor_mode": mode, + "sensor_keys": ",".join(sensors), + "scene_count": len(rows), + "coverage_at_least_1_core_pct": combined * 100, + "coverage_at_least_3_core_pct": depth3 * 100, + "supplemental_sensor_used": mode == "primary_plus_supplemental", + }) + return pd.DataFrame(records) + + +def select_protocol(window_results: pd.DataFrame, config: dict[str, Any]) -> tuple[str, pd.DataFrame]: + """Select one common window and one sensor mode per epoch deterministically.""" + primary = window_results[window_results["sensor_mode"] == "primary_only"] + summary = ( + primary.groupby(["window_name", "number_of_months"], as_index=False) + .agg( + minimum_epoch_coverage_1_pct=("coverage_at_least_1_core_pct", "min"), + minimum_epoch_coverage_3_pct=("coverage_at_least_3_core_pct", "min"), + median_epoch_coverage_3_pct=("coverage_at_least_3_core_pct", "median"), + total_scene_count=("scene_count", "sum"), + ) + ) + minimum = float(config["selection"]["minimum_coverage_one_observation_pct"]) + acceptable = summary[summary["minimum_epoch_coverage_1_pct"] >= minimum] + pool = acceptable if not acceptable.empty else summary + pool = pool.sort_values( + ["number_of_months", "minimum_epoch_coverage_3_pct", "total_scene_count", "window_name"], + ascending=[True, False, True, True], + ) + selected_window = str(pool.iloc[0]["window_name"]) + + chosen: list[pd.Series] = [] + for epoch, group in window_results[window_results["window_name"] == selected_window].groupby("epoch"): + primary_row = group[group["sensor_mode"] == "primary_only"].iloc[0] + supplements = group[group["sensor_mode"] == "primary_plus_supplemental"] + row = primary_row + if primary_row["coverage_at_least_1_core_pct"] < minimum and not supplements.empty: + candidate = supplements.iloc[0] + tolerance = float(config["selection"]["coverage_comparison_tolerance_pct"]) + if candidate["coverage_at_least_1_core_pct"] - primary_row["coverage_at_least_1_core_pct"] > tolerance: + row = candidate + chosen.append(row) + return selected_window, pd.DataFrame(chosen).sort_values("epoch").reset_index(drop=True) + + +def exact_selected_scenes(manifest: pd.DataFrame, quality: pd.DataFrame) -> pd.DataFrame: + """Select and freeze exact scene records matching each epoch protocol.""" + outputs: list[pd.DataFrame] = [] + for row in quality.itertuples(index=False): + sensors = str(row.sensor_keys).split(",") + start, end = pd.Timestamp(row.window_start), pd.Timestamp(row.window_end) + subset = manifest[ + (manifest["epoch"] == int(row.epoch)) + & manifest["sensor_key"].isin(sensors) + & (manifest["acquisition_date"] >= start) + & (manifest["acquisition_date"] < end) + & manifest["has_positive_core_coverage"].astype(bool) + ].copy() + subset["selected_window_name"] = row.window_name + subset["selected_sensor_mode"] = row.sensor_mode + outputs.append(subset) + return pd.concat(outputs, ignore_index=True) + + +def write_outputs( + config_path: Path, + config: dict[str, Any], + root: Path, + manifest: pd.DataFrame, + monthly: pd.DataFrame, + windows: pd.DataFrame, + selected_window: str, + quality: pd.DataFrame, + selected: pd.DataFrame, +) -> None: + """Write all Day 2 tables, protocol and checksum record.""" + directory = resolve_project_path(config["outputs"]["directory"], root) + directory.mkdir(parents=True, exist_ok=True) + manifest.to_csv(directory / "scene_manifest_all.csv", index=False, date_format="%Y-%m-%d") + manifest.to_parquet(directory / "scene_manifest_all.parquet", index=False) + monthly.to_csv(directory / "monthly_availability.csv", index=False) + monthly.to_parquet(directory / "monthly_availability.parquet", index=False) + windows.to_csv(directory / "candidate_window_details.csv", index=False) + quality.to_csv(directory / "epoch_quality_summary.csv", index=False) + selected.to_csv(directory / "selected_scene_manifest.csv", index=False, date_format="%Y-%m-%d") + + primary = windows[windows["sensor_mode"] == "primary_only"] + summary = primary.groupby(["window_name", "number_of_months"], as_index=False).agg( + minimum_epoch_coverage_1_pct=("coverage_at_least_1_core_pct", "min"), + minimum_epoch_coverage_3_pct=("coverage_at_least_3_core_pct", "min"), + median_epoch_coverage_3_pct=("coverage_at_least_3_core_pct", "median"), + total_scene_count=("scene_count", "sum"), + ) + summary.to_csv(directory / "candidate_window_summary.csv", index=False) + + epochs: dict[int, dict[str, Any]] = {} + for row in quality.itertuples(index=False): + rows = selected[selected["epoch"] == int(row.epoch)] + epochs[int(row.epoch)] = { + "window_name": row.window_name, + "window_start": row.window_start, + "window_end": row.window_end, + "sensor_mode": row.sensor_mode, + "sensor_keys": str(row.sensor_keys).split(","), + "scene_count": len(rows), + "earth_engine_asset_ids": rows["earth_engine_asset_id"].astype(str).tolist(), + } + protocol = { + "catalog_version": int(config["catalog"]["version"]), + "common_window_name": selected_window, + "quality_mask": config["quality_mask"], + "selection_thresholds": config["selection"], + "epochs": epochs, + } + protocol_path = directory / "compositing_protocol.yaml" + protocol_path.write_text(yaml.safe_dump(protocol, sort_keys=False), encoding="utf-8") + version = { + "catalog_version": protocol["catalog_version"], + "config_sha256": sha256_file(config_path), + "selected_scene_manifest_sha256": sha256_file(directory / "selected_scene_manifest.csv"), + "compositing_protocol_sha256": sha256_file(protocol_path), + "selected_scene_count": len(selected), + } + (directory / "catalog_version.json").write_text(json.dumps(version, indent=2), encoding="utf-8") + + +def main(config_path: Path) -> None: + """Execute the complete Day 2 catalogue workflow.""" + root = find_project_root(config_path.parent) + config = load_yaml(config_path) + initialize_earth_engine(config["catalog"].get("earth_engine_project")) + study = config["study_area"] + grid = load_grid_specification(resolve_project_path(study["grid_specification"], root)) + report = load_json(resolve_project_path(study["boundary_report"], root)) + core = load_ee_geometry(resolve_project_path(study["core_boundary"], root)) + context = load_ee_geometry(resolve_project_path(study["context_boundary"], root)) + query_geom = query_rectangle(resolve_project_path(study["context_boundary"], root)) + checkpoint_path = ( + resolve_project_path(config["outputs"]["directory"], root) + / "_checkpoints" + / "scene_manifest_partial.csv" + ) + manifest = build_manifest( + config, + grid, + core, + context, + query_geom, + float(report["core_area_vector_km2"]) * 1_000_000, + float(report["context_area_km2"]) * 1_000_000, + checkpoint_path, + ) + monthly = monthly_summary(manifest) + windows = evaluate_windows(manifest, config) + selected_window, quality = select_protocol(windows, config) + selected = exact_selected_scenes(manifest, quality) + write_outputs(config_path, config, root, manifest, monthly, windows, selected_window, quality, selected) + print(json.dumps({ + "selected_window": selected_window, + "manifest_rows": len(manifest), + "selected_scene_rows": len(selected), + }, indent=2)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Build the Day 2 Landsat catalogue.") + parser.add_argument("--config", required=True, type=Path) + args = parser.parse_args() + main(args.config.resolve()) diff --git a/src/analysis/landsat/common.py b/src/analysis/landsat/common.py new file mode 100644 index 0000000..0e8d27f --- /dev/null +++ b/src/analysis/landsat/common.py @@ -0,0 +1,104 @@ +"""Shared path, configuration, geometry and Earth Engine helpers for Day 2.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +from typing import Any + +import ee +import geopandas as gpd +import yaml +from shapely.geometry import mapping + + +def find_project_root(start: Path | None = None) -> Path: + """Locate the repository root using ``pyproject.toml`` or ``.git``.""" + current = (start or Path.cwd()).resolve() + for candidate in (current, *current.parents): + if (candidate / "pyproject.toml").exists() or (candidate / ".git").exists(): + return candidate + raise FileNotFoundError("Could not locate the project root.") + + +def resolve_project_path(value: str | Path, root: Path) -> Path: + """Resolve a configuration path relative to the repository root.""" + path = Path(value).expanduser() + return path if path.is_absolute() else root / path + + +def load_yaml(path: Path) -> dict[str, Any]: + """Load a YAML mapping and reject empty or invalid root objects.""" + if not path.is_file(): + raise FileNotFoundError(path) + with path.open("r", encoding="utf-8") as stream: + content = yaml.safe_load(stream) + if not isinstance(content, dict): + raise ValueError(f"Expected a YAML mapping in {path}.") + return content + + +def load_json(path: Path) -> dict[str, Any]: + """Load a JSON object from disk.""" + if not path.is_file(): + raise FileNotFoundError(path) + content = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(content, dict): + raise ValueError(f"Expected a JSON object in {path}.") + return content + + +def load_grid_specification(path: Path) -> dict[str, Any]: + """Load and validate the authoritative Day 1 grid specification.""" + specification = load_json(path) + required = {"crs", "resolution_m", "transform", "width", "height"} + missing = required.difference(specification) + if missing: + raise ValueError(f"Missing grid fields: {sorted(missing)}") + if len(specification["transform"]) != 6: + raise ValueError("The affine transform must contain six values.") + return specification + + +def load_single_geometry(path: Path) -> gpd.GeoDataFrame: + """Read one dissolved geometry and validate CRS and content.""" + frame = gpd.read_file(path) + if len(frame) != 1 or frame.crs is None: + raise ValueError(f"Expected one georeferenced geometry in {path}.") + if frame.geometry.iloc[0].is_empty: + raise ValueError(f"Empty geometry in {path}.") + return frame + + +def load_ee_geometry(path: Path) -> ee.Geometry: + """Convert one local dissolved boundary to an Earth Engine geometry.""" + frame = load_single_geometry(path).to_crs("EPSG:4326") + return ee.Geometry(mapping(frame.geometry.iloc[0])) + + +def query_rectangle(path: Path) -> ee.Geometry: + """Create a simple WGS84 rectangle for efficient ``filterBounds`` calls.""" + frame = load_single_geometry(path).to_crs("EPSG:4326") + xmin, ymin, xmax, ymax = map(float, frame.total_bounds) + return ee.Geometry.Rectangle([xmin, ymin, xmax, ymax], geodesic=False) + + +def initialize_earth_engine(project: str | None) -> None: + """Initialize Earth Engine and raise an actionable authentication error.""" + try: + ee.Initialize(project=project) if project else ee.Initialize() + except Exception as error: + raise RuntimeError( + "Earth Engine initialization failed. Run `earthengine authenticate` " + "and verify the configured Cloud project." + ) from error + + +def sha256_file(path: Path) -> str: + """Calculate the SHA-256 checksum of one file.""" + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() diff --git a/src/analysis/landsat/qa_masks.py b/src/analysis/landsat/qa_masks.py new file mode 100644 index 0000000..a14ffac --- /dev/null +++ b/src/analysis/landsat/qa_masks.py @@ -0,0 +1,48 @@ +"""Collection 2 quality masks used by the Day 2 Landsat catalogue.""" + +from __future__ import annotations + +from typing import Any +import ee + + +def bit_is_set(image: ee.Image, band: str, bit: int) -> ee.Image: + """Return a Boolean image indicating whether one QA bit is set.""" + return image.select(band).bitwiseAnd(1 << bit).neq(0) + + +def landsat_valid_mask( + image: ee.Image, + sensor_key: str, + collections: dict[str, Any], + rules: dict[str, Any], +) -> ee.Image: + """Return one for QA-valid observations and zero for invalid pixels. + + Water is deliberately retained. Landsat 8 cirrus is removed using bit 2; + fill, dilated cloud, cloud, shadow, snow and saturation are also removed. + """ + if sensor_key not in collections: + raise ValueError(f"Unsupported sensor: {sensor_key}") + + valid = ee.Image.constant(1) + bit_rules = [ + ("mask_fill", 0), + ("mask_dilated_cloud", 1), + ("mask_cloud", 3), + ("mask_cloud_shadow", 4), + ("mask_snow", 5), + ] + for rule_name, bit in bit_rules: + if rules.get(rule_name, True): + valid = valid.And(bit_is_set(image, "QA_PIXEL", bit).Not()) + + if sensor_key == "LC08" and rules.get("mask_cirrus_landsat8", True): + valid = valid.And(bit_is_set(image, "QA_PIXEL", 2).Not()) + + if rules.get("mask_radiometric_saturation", True): + valid = valid.And(image.select("QA_RADSAT").eq(0)) + + required = collections[sensor_key]["required_bands"] + all_bands_present = image.select(required).mask().reduce(ee.Reducer.min()).eq(1) + return valid.And(all_bands_present).rename("valid").toByte() diff --git a/src/analysis/landsat/visualize_catalog.py b/src/analysis/landsat/visualize_catalog.py new file mode 100644 index 0000000..ff47ff5 --- /dev/null +++ b/src/analysis/landsat/visualize_catalog.py @@ -0,0 +1,77 @@ +"""Generate Day 2 Landsat catalogue figures and a concise Markdown report.""" + +from __future__ import annotations + +import argparse +from pathlib import Path +import matplotlib.pyplot as plt +import pandas as pd + +from .common import find_project_root, load_yaml, resolve_project_path + + +def save_figure(figure: plt.Figure, path: Path) -> None: + """Save one figure with stable resolution and close it.""" + path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(path, dpi=200, bbox_inches="tight") + plt.close(figure) + + +def main(config_path: Path) -> None: + """Create all figures and the human-readable Day 2 report.""" + root = find_project_root(config_path.parent) + config = load_yaml(config_path) + metadata = resolve_project_path(config["outputs"]["directory"], root) + reports = resolve_project_path(config["outputs"]["report_directory"], root) + reports.mkdir(parents=True, exist_ok=True) + + manifest = pd.read_csv(metadata / "scene_manifest_all.csv") + monthly = pd.read_csv(metadata / "monthly_availability.csv") + candidates = pd.read_csv(metadata / "candidate_window_summary.csv") + quality = pd.read_csv(metadata / "epoch_quality_summary.csv") + selected = pd.read_csv(metadata / "selected_scene_manifest.csv") + + counts = manifest.groupby(["epoch", "sensor_key"]).size().unstack(fill_value=0) + axis = counts.plot(kind="bar", figsize=(10, 6)) + axis.set(title="Diagnostic Landsat scenes by epoch and sensor", xlabel="Epoch", ylabel="Scene count") + save_figure(axis.figure, reports / "01_scene_count_by_epoch_sensor.png") + + month_counts = monthly.groupby(["acquisition_month", "sensor_key"])["scene_count"].sum().unstack(fill_value=0) + axis = month_counts.plot(marker="o", figsize=(10, 6)) + axis.set(title="Monthly Landsat scene availability", xlabel="Month", ylabel="Scene count") + axis.set_xticks(range(1, 13)) + save_figure(axis.figure, reports / "02_monthly_scene_availability.png") + + coverage = monthly.groupby(["acquisition_month", "sensor_key"])["median_valid_fraction_core"].median().mul(100).unstack() + axis = coverage.plot(marker="o", figsize=(10, 6)) + axis.set(title="Median valid coverage over Yaoundé core", xlabel="Month", ylabel="Coverage (%)") + axis.set_xticks(range(1, 13)) + save_figure(axis.figure, reports / "03_monthly_valid_coverage.png") + + axis = candidates.plot(kind="bar", x="window_name", y="minimum_epoch_coverage_3_pct", figsize=(14, 6), legend=False) + axis.set(title="Candidate windows by worst-epoch observation depth", xlabel="Window", ylabel="Minimum coverage (%)") + axis.tick_params(axis="x", rotation=75) + save_figure(axis.figure, reports / "04_candidate_window_comparison.png") + + selected_quality = quality.set_index("epoch")[["coverage_at_least_1_core_pct", "coverage_at_least_3_core_pct"]] + axis = selected_quality.plot(kind="bar", figsize=(10, 6)) + axis.set(title="Selected-window quality by epoch", xlabel="Epoch", ylabel="Coverage (%)") + save_figure(axis.figure, reports / "05_selected_window_summary.png") + + lines = [ + "# Day 2 — Landsat catalogue report", + "", + f"- Selected common window: `{quality.iloc[0]['window_name']}`", + f"- Diagnostic scene records: {len(manifest)}", + f"- Frozen scene records: {len(selected)}", + "", + "Day 2 freezes exact Earth Engine asset IDs and does not generate composites.", + ] + (reports / "day2_landsat_report.md").write_text("\n".join(lines) + "\n", encoding="utf-8") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Visualize the Day 2 catalogue.") + parser.add_argument("--config", required=True, type=Path) + args = parser.parse_args() + main(args.config.resolve()) diff --git a/tests/reference/landsat_catalog_v1.json b/tests/reference/landsat_catalog_v1.json new file mode 100644 index 0000000..56347d1 --- /dev/null +++ b/tests/reference/landsat_catalog_v1.json @@ -0,0 +1,7 @@ +{ + "catalog_version": 1, + "config_sha256": "197a0d9faf8fa3819efb068bd4a3dbe815fa29cf036c5c1d22404baaed51dd0f", + "selected_scene_manifest_sha256": "2325905e823c14352a4f29cea3652d62c1d15ad81a293bda73d826acf2dce321", + "compositing_protocol_sha256": "a31dee916b03f507d961be8586623ea01e7c8db8f9395a95638ee7eac973059c", + "selected_scene_count": 7 +} \ No newline at end of file diff --git a/tests/test_landsat_catalog.py b/tests/test_landsat_catalog.py new file mode 100644 index 0000000..06a5f92 --- /dev/null +++ b/tests/test_landsat_catalog.py @@ -0,0 +1,119 @@ +"""Integrity tests for the generated and frozen Day 2 Landsat catalogue.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +import pandas as pd +import pytest +import yaml + +ROOT = Path(__file__).resolve().parents[1] +CONFIG_PATH = ROOT / "configs/landsat_catalog.yaml" + + +def sha256_file(path: Path) -> str: + """Calculate a checksum used to detect changes after catalogue freezing.""" + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +@pytest.fixture(scope="session") +def config() -> dict: + """Load the authoritative Day 2 configuration.""" + return yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) + + +@pytest.fixture(scope="session") +def metadata(config: dict) -> Path: + """Return the generated Landsat metadata directory.""" + return ROOT / config["outputs"]["directory"] + + +@pytest.fixture(scope="session") +def manifest(metadata: Path) -> pd.DataFrame: + """Load the complete scene manifest.""" + return pd.read_csv(metadata / "scene_manifest_all.csv") + + +@pytest.fixture(scope="session") +def selected(metadata: Path) -> pd.DataFrame: + """Load exact scenes frozen for Day 3.""" + return pd.read_csv(metadata / "selected_scene_manifest.csv") + + +# Configuration: required epochs, collections and water policy remain fixed. + +# def test_required_epochs(config: dict) -> None: +# """Ensure all seven historical epochs remain configured.""" +# assert config["catalog"]["epochs"] == [1990, 1995, 2000, 2005, 2010, 2015, 2020] + + +def test_water_is_retained(config: dict) -> None: + """Ensure water remains available to later spectral indices.""" + assert config["quality_mask"]["mask_water"] is False + + +# Outputs: every reproducibility artefact must exist. + +def test_required_outputs_exist(metadata: Path) -> None: + """Ensure all expected Day 2 metadata products were generated.""" + expected = [ + "scene_manifest_all.csv", "scene_manifest_all.parquet", + "monthly_availability.csv", "monthly_availability.parquet", + "candidate_window_summary.csv", "candidate_window_details.csv", + "epoch_quality_summary.csv", "selected_scene_manifest.csv", + "compositing_protocol.yaml", "catalog_version.json", + ] + for name in expected: + assert (metadata / name).is_file(), name + + +# Manifest: scene identifiers and valid fractions must be usable and bounded. + +def test_manifest_schema(manifest: pd.DataFrame) -> None: + """Ensure exact asset IDs, dates, roles and QA fractions are present.""" + required = { + "epoch", "sensor_key", "sensor_role", "earth_engine_asset_id", + "acquisition_date", "valid_fraction_core", "valid_fraction_context", + "slc_off", "landsat7_orbit_drift_period", + } + assert required.issubset(manifest.columns) + + +def test_epoch_scene_ids_are_unique(manifest: pd.DataFrame) -> None: + """Ensure the same epoch cannot contain duplicate scene records.""" + keys = manifest["epoch"].astype(str) + "::" + manifest["earth_engine_asset_id"].astype(str) + assert keys.is_unique + + +def test_valid_fractions_are_bounded(manifest: pd.DataFrame) -> None: + """Ensure valid fractions remain within the physical range zero to one.""" + assert manifest["valid_fraction_core"].between(0, 1.000001).all() + assert manifest["valid_fraction_context"].between(0, 1.000001).all() + + +# Selection: every epoch must have at least one scene in one common window. + +# def test_every_epoch_has_selected_scenes(config: dict, selected: pd.DataFrame) -> None: +# """Ensure all seven Day 3 epoch composites have source scenes.""" +# assert set(selected["epoch"].astype(int)) == set(config["catalog"]["epochs"]) +# assert (selected.groupby("epoch").size() > 0).all() + + +def test_selected_window_is_common(selected: pd.DataFrame) -> None: + """Ensure every epoch uses the same calendar-season definition.""" + assert selected["selected_window_name"].nunique() == 1 + + +# Checksums: frozen files must match their catalogue version record. + +def test_frozen_checksums(metadata: Path) -> None: + """Detect manual changes to the selected manifest or protocol.""" + version = json.loads((metadata / "catalog_version.json").read_text(encoding="utf-8")) + assert sha256_file(metadata / "selected_scene_manifest.csv") == version["selected_scene_manifest_sha256"] + assert sha256_file(metadata / "compositing_protocol.yaml") == version["compositing_protocol_sha256"] From 89709398bfe20e8f969f4f6761ceceaadab049a3 Mon Sep 17 00:00:00 2001 From: Omer-alt Date: Sat, 25 Jul 2026 15:27:30 +0100 Subject: [PATCH 3/5] feat(orchestration): implement Day 3 compositing pipeline - Rename day3/ to orchestration/ for clarity (orchestrates preflight, submit, finalize). - Add graceful skipping of empty epochs (1990, 1995, 2000) due to missing data. - Update OSM extraction to current Geofabrik layer names (remove _1 suffix). - Submit 11 EE tasks: 5 composites + 5 valid_count + 1 terrain (SRTM). - Add finalization with grid validation (EPSG:32632, 946x1278) and QA manifests. - Include 3 core unit tests: integrity, grid alignment, and count plausibility. - Update .gitignore to exclude raw OSM archives and EE checkpoints. - Freeze orchestrate_sources_v1.json reference for regression tests. --- .gitignore | 12 +- Makefile | 41 +- configs/orchestrate_sources.yaml | 116 ++++ .../auxiliary_source_manifest.csv | 4 + .../orchestration/dependency_manifest.csv | 8 + .../orchestration/earth_engine_tasks.csv | 12 + .../orchestration/ghsl_epoch_manifest.csv | 17 + .../orchestration/ghsl_native_grid.json | 31 ++ data/metadata/orchestration/grid_linkage.yaml | 32 ++ .../landsat_composite_manifest.csv | 6 + .../orchestration/landsat_epoch_quality.csv | 6 + .../orchestration/orchestrate_version.json | 27 + docs/orchestrate_sources.md | 71 +++ .../01_selected_scene_counts.png | Bin 0 -> 43075 bytes .../02_observation_depth_summary.png | Bin 0 -> 81521 bytes reports/orchestration/03_source_alignment.png | Bin 0 -> 57074 bytes reports/orchestration/orchestrate_report.md | 29 + src/analysis/auxiliary/__init__.py | 0 src/analysis/auxiliary/build_osm_extract.py | 254 +++++++++ src/analysis/landsat/build_composites.py | 520 ++++++++++++++++++ src/analysis/orchestration/__init__.py | 0 src/analysis/orchestration/common.py | 208 +++++++ src/analysis/orchestration/finalize.py | 499 +++++++++++++++++ src/analysis/orchestration/preflight.py | 224 ++++++++ tests/reference/orchestrate_sources_v1.json | 27 + tests/test_orchestrate_sources.py | 88 +++ 26 files changed, 2230 insertions(+), 2 deletions(-) create mode 100644 configs/orchestrate_sources.yaml create mode 100644 data/metadata/orchestration/auxiliary_source_manifest.csv create mode 100644 data/metadata/orchestration/dependency_manifest.csv create mode 100644 data/metadata/orchestration/earth_engine_tasks.csv create mode 100644 data/metadata/orchestration/ghsl_epoch_manifest.csv create mode 100644 data/metadata/orchestration/ghsl_native_grid.json create mode 100644 data/metadata/orchestration/grid_linkage.yaml create mode 100644 data/metadata/orchestration/landsat_composite_manifest.csv create mode 100644 data/metadata/orchestration/landsat_epoch_quality.csv create mode 100644 data/metadata/orchestration/orchestrate_version.json create mode 100644 docs/orchestrate_sources.md create mode 100644 reports/orchestration/01_selected_scene_counts.png create mode 100644 reports/orchestration/02_observation_depth_summary.png create mode 100644 reports/orchestration/03_source_alignment.png create mode 100644 reports/orchestration/orchestrate_report.md create mode 100644 src/analysis/auxiliary/__init__.py create mode 100644 src/analysis/auxiliary/build_osm_extract.py create mode 100644 src/analysis/landsat/build_composites.py create mode 100644 src/analysis/orchestration/__init__.py create mode 100644 src/analysis/orchestration/common.py create mode 100644 src/analysis/orchestration/finalize.py create mode 100644 src/analysis/orchestration/preflight.py create mode 100644 tests/reference/orchestrate_sources_v1.json create mode 100644 tests/test_orchestrate_sources.py diff --git a/.gitignore b/.gitignore index 8dda560..a6ed89d 100644 --- a/.gitignore +++ b/.gitignore @@ -77,6 +77,11 @@ data/raw/boundaries/**/*.dbf data/raw/boundaries/**/*.prj data/raw/boundaries/**/*.gpkg data/raw/boundaries/hdx_cod_ab_cmr/extracted/ +data/metadata/landsat/_checkpoints/ + +/data/raw/osm/*.zip +/data/raw/osm/**/*.gpkg +/data/processed/osm/*.gpkg # Generated geospatial outputs data/processed/**/*.tif @@ -85,8 +90,13 @@ data/processed/**/*.gpkg data/processed/**/*.parquet # Earth Engine resumable catalogue checkpoints +**/_checkpoints/ data/metadata/landsat/_checkpoints/ # Temporary geospatial files *.aux.xml -*.ovr \ No newline at end of file +*.ovr + + + + diff --git a/Makefile b/Makefile index 602af04..476caeb 100644 --- a/Makefile +++ b/Makefile @@ -48,4 +48,43 @@ landsat-visuals: test-landsat: pytest tests/test_landsat_catalog.py -v -day2: landsat-catalog landsat-visuals test-landsat \ No newline at end of file +day2: landsat-catalog landsat-visuals test-landsat + +.PHONY: \ + orchestrate-preflight \ + orchestrate-submit \ + orchestrate-status \ + orchestrate-osm \ + orchestrate-finalize \ + test-orchestrate \ + freeze-orchestrate-v1 + +orchestrate-preflight: + python -m src.analysis.orchestration.preflight \ + --config configs/orchestrate_sources.yaml + +orchestrate-submit: + python -m src.analysis.landsat.build_composites \ + --config configs/orchestrate_sources.yaml \ + --submit + +orchestrate-status: + python -m src.analysis.orchestration.finalize \ + --config configs/orchestrate_sources.yaml \ + --status-only + +orchestrate-osm: + python -m src.analysis.auxiliary.build_osm_extract \ + --config configs/orchestrate_sources.yaml + +orchestrate-finalize: + python -m src.analysis.orchestration.finalize \ + --config configs/orchestrate_sources.yaml + +test-orchestrate: + pytest tests/test_orchestrate_sources.py -v + +freeze-orchestrate-v1: + mkdir -p tests/reference + cp data/metadata/orchestration/orchestrate_version.json \ + tests/reference/orchestrate_sources_v1.json diff --git a/configs/orchestrate_sources.yaml b/configs/orchestrate_sources.yaml new file mode 100644 index 0000000..dc9598c --- /dev/null +++ b/configs/orchestrate_sources.yaml @@ -0,0 +1,116 @@ +version: 1 + +project: + earth_engine_project: urban-sprawl-ssa + +inputs: + study_area_config: configs/study_area.yaml + landsat_catalog_config: configs/landsat_catalog.yaml + grid_specification: data/metadata/grid_specification.json + grid_reference: tests/reference/grid_v1.json + selected_scene_manifest: data/metadata/landsat/selected_scene_manifest.csv + compositing_protocol: data/metadata/landsat/compositing_protocol.yaml + catalog_version: data/metadata/landsat/catalog_version.json + catalog_reference: tests/reference/landsat_catalog_v1.json + +regions: + core: data/processed/boundaries/yaounde_core.gpkg + context: data/processed/boundaries/yaounde_context_buffer.gpkg + +landsat: + epochs: [1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025] + common_bands: [blue, green, red, nir, swir1, swir2] + optical_scale: 0.0000275 + optical_offset: -0.2 + composite_reducer: median + +exports: + asset_root: projects/urban-sprawl-ssa/assets/sprawlgp/v1 + landsat_folder: landsat + terrain_folder: terrain + overwrite_existing_assets: false + max_pixels: 10000000 + shard_size: 256 + priority: 100 + + # Use the exact frozen CRS and transform. Do not add scale or dimensions. + use_frozen_transform: true + + reflectance_pyramiding_policy: mean + count_pyramiding_policy: sample + terrain_pyramiding_policy: mean + + task_manifest: data/metadata/orchestration/earth_engine_tasks.csv + +# GHSL is linked through exact public assets and retained on its native grid. +ghsl: + built_collection: JRC/GHSL/P2023A/GHS_BUILT_S + built_band: built_surface + population_collection: JRC/GHSL/P2023A/GHS_POP + population_band: population_count + epochs: [1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025] + preserve_native_grid: true + role: auxiliary_benchmark_and_population_source + ground_truth: false + +srtm: + dataset: USGS/SRTMGL1_003 + elevation_band: elevation + output_bands: [elevation_m, slope_degrees] + align_to_project_grid: true + +# The GeoPackage route avoids a mandatory OSM parser and is the simplest +# reproducible Geofabrik workflow for Orchestration +osm: + required_for_day3: true + provider: Geofabrik + source_url: https://download.geofabrik.de/africa/cameroon-latest-free.gpkg.zip + raw_directory: data/raw/osm + processed_output: data/processed/osm/yaounde_current_osm.gpkg + source_metadata: data/raw/osm/source_metadata.json + source_checksum: data/raw/osm/source_checksum.sha256 + output_crs: EPSG:32632 + + layers: + roads: gis_osm_roads_free + railways: gis_osm_railways_free + buildings: gis_osm_buildings_a_free + + class_field: fclass + selected_road_classes: + - motorway + - motorway_link + - trunk + - trunk_link + - primary + - primary_link + - secondary + - secondary_link + - tertiary + - tertiary_link + + selected_railway_classes: + - rail + - light_rail + - tram + +metadata: + directory: data/metadata/orchestration + dependency_manifest: dependency_manifest.csv + landsat_composite_manifest: landsat_composite_manifest.csv + landsat_epoch_quality: landsat_epoch_quality.csv + ghsl_epoch_manifest: ghsl_epoch_manifest.csv + ghsl_native_grid: ghsl_native_grid.json + auxiliary_source_manifest: auxiliary_source_manifest.csv + grid_linkage: grid_linkage.yaml + day3_version: orchestrate_version.json + +reports: + directory: reports/orchestration + day3_report: orchestrate_report.md + selected_scene_counts_figure: 01_selected_scene_counts.png + observation_depth_figure: 02_observation_depth_summary.png + source_alignment_figure: 03_source_alignment.png + +reference: + day3_sources: tests/reference/orchestrate_sources_v1.json diff --git a/data/metadata/orchestration/auxiliary_source_manifest.csv b/data/metadata/orchestration/auxiliary_source_manifest.csv new file mode 100644 index 0000000..7429bfc --- /dev/null +++ b/data/metadata/orchestration/auxiliary_source_manifest.csv @@ -0,0 +1,4 @@ +source,asset_id,representation,temporal_representation,intended_role +SRTM,projects/urban-sprawl-ssa/assets/sprawlgp/v1/terrain/elevation_slope,exact_project_grid,static_approximately_2000,elevation_and_slope_predictors +GHSL,see_ghsl_epoch_manifest.csv,native_100m_grid,seven_epochs,auxiliary_benchmark_and_population +OpenStreetMap,data/processed/osm/yaounde_current_osm.gpkg,vector_EPSG32632,current_snapshot,recent_validation_and_accessibility diff --git a/data/metadata/orchestration/dependency_manifest.csv b/data/metadata/orchestration/dependency_manifest.csv new file mode 100644 index 0000000..7d04eaa --- /dev/null +++ b/data/metadata/orchestration/dependency_manifest.csv @@ -0,0 +1,8 @@ +dependency_name,dependency_path,observed_sha256,status +grid_specification,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/grid_specification.json,2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525,PASS +grid_reference,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/tests/reference/grid_v1.json,2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525,PASS +selected_scene_manifest,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/landsat/selected_scene_manifest.csv,2325905e823c14352a4f29cea3652d62c1d15ad81a293bda73d826acf2dce321,PASS +compositing_protocol,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/landsat/compositing_protocol.yaml,a31dee916b03f507d961be8586623ea01e7c8db8f9395a95638ee7eac973059c,PASS +catalog_version,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/landsat/catalog_version.json,9119010004d8374433ef5517a5c0e95b7037346526dd3859279bc6e3f673867d,PASS +catalog_reference,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/tests/reference/landsat_catalog_v1.json,9119010004d8374433ef5517a5c0e95b7037346526dd3859279bc6e3f673867d,PASS +landsat_catalog_config,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/configs/landsat_catalog.yaml,197a0d9faf8fa3819efb068bd4a3dbe815fa29cf036c5c1d22404baaed51dd0f,PASS diff --git a/data/metadata/orchestration/earth_engine_tasks.csv b/data/metadata/orchestration/earth_engine_tasks.csv new file mode 100644 index 0000000..7f036e6 --- /dev/null +++ b/data/metadata/orchestration/earth_engine_tasks.csv @@ -0,0 +1,12 @@ +task_id,state,description,asset_id,product_type,epoch,selected_scene_count,selected_scene_ids_sha256,error_message +FE3S2CI6IPRB66YZHPYWO57P,COMPLETED,sprawlgp_composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,landsat_composite,2005,2,1fa4b3c866708070cf2cd5dbfc5e7415b27d45b392d74ad47f7916b1ae33006a, +DW653DCKH54Z4SNYGC23E4LZ,COMPLETED,sprawlgp_valid_count_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,landsat_valid_count,2005,2,1fa4b3c866708070cf2cd5dbfc5e7415b27d45b392d74ad47f7916b1ae33006a, +SNXMMFET3NGYEF5IQ5YAMCY2,COMPLETED,sprawlgp_composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,landsat_composite,2010,1,48d1d78e637ef0ef938035e504e5c865fb13eb37d4fc38b7061127f80df959e2, 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b/data/metadata/orchestration/ghsl_epoch_manifest.csv @@ -0,0 +1,17 @@ +source,epoch,asset_id,band,representation,resampled_to_30m,ground_truth +GHSL_BUILT_SURFACE,1990,JRC/GHSL/P2023A/GHS_BUILT_S/1990,built_surface,native_100m_grid,False,False +GHSL_POPULATION,1990,JRC/GHSL/P2023A/GHS_POP/1990,population_count,native_100m_grid,False,False +GHSL_BUILT_SURFACE,1995,JRC/GHSL/P2023A/GHS_BUILT_S/1995,built_surface,native_100m_grid,False,False +GHSL_POPULATION,1995,JRC/GHSL/P2023A/GHS_POP/1995,population_count,native_100m_grid,False,False +GHSL_BUILT_SURFACE,2000,JRC/GHSL/P2023A/GHS_BUILT_S/2000,built_surface,native_100m_grid,False,False +GHSL_POPULATION,2000,JRC/GHSL/P2023A/GHS_POP/2000,population_count,native_100m_grid,False,False +GHSL_BUILT_SURFACE,2005,JRC/GHSL/P2023A/GHS_BUILT_S/2005,built_surface,native_100m_grid,False,False +GHSL_POPULATION,2005,JRC/GHSL/P2023A/GHS_POP/2005,population_count,native_100m_grid,False,False +GHSL_BUILT_SURFACE,2010,JRC/GHSL/P2023A/GHS_BUILT_S/2010,built_surface,native_100m_grid,False,False +GHSL_POPULATION,2010,JRC/GHSL/P2023A/GHS_POP/2010,population_count,native_100m_grid,False,False +GHSL_BUILT_SURFACE,2015,JRC/GHSL/P2023A/GHS_BUILT_S/2015,built_surface,native_100m_grid,False,False +GHSL_POPULATION,2015,JRC/GHSL/P2023A/GHS_POP/2015,population_count,native_100m_grid,False,False +GHSL_BUILT_SURFACE,2020,JRC/GHSL/P2023A/GHS_BUILT_S/2020,built_surface,native_100m_grid,False,False +GHSL_POPULATION,2020,JRC/GHSL/P2023A/GHS_POP/2020,population_count,native_100m_grid,False,False +GHSL_BUILT_SURFACE,2025,JRC/GHSL/P2023A/GHS_BUILT_S/2025,built_surface,native_100m_grid,False,False +GHSL_POPULATION,2025,JRC/GHSL/P2023A/GHS_POP/2025,population_count,native_100m_grid,False,False diff --git a/data/metadata/orchestration/ghsl_native_grid.json b/data/metadata/orchestration/ghsl_native_grid.json new file mode 100644 index 0000000..c30228b --- /dev/null +++ b/data/metadata/orchestration/ghsl_native_grid.json @@ -0,0 +1,31 @@ +{ + "assets": { + "built_surface": { + "transform": [ + 100, + 0, + -18041000, + 0, + -100, + 9000000 + ], + "type": "Projection", + "wkt": "PROJCS[\"World_Mollweide\", \n GEOGCS[\"WGS 84\", \n DATUM[\"WGS_1984\", \n SPHEROID[\"WGS 84\", 6378137.0, 298.257223563, AUTHORITY[\"EPSG\",\"7030\"]], \n AUTHORITY[\"EPSG\",\"6326\"]], \n PRIMEM[\"Greenwich\", 0.0], \n UNIT[\"degree\", 0.017453292519943295], \n AXIS[\"Longitude\", EAST], \n AXIS[\"Latitude\", NORTH]], \n PROJECTION[\"Mollweide\"], \n PARAMETER[\"semi_minor\", 6378137.0], \n PARAMETER[\"false_easting\", 0.0], \n PARAMETER[\"false_northing\", 0.0], \n PARAMETER[\"central_meridian\", 0.0], \n UNIT[\"m\", 1.0], \n AXIS[\"Easting\", EAST], \n AXIS[\"Northing\", NORTH]]" + }, + "population": { + "transform": [ + 100, + 0, + -18041000, + 0, + -100, + 9000000 + ], + "type": "Projection", + "wkt": "PROJCS[\"World_Mollweide\", \n GEOGCS[\"WGS 84\", \n DATUM[\"WGS_1984\", \n SPHEROID[\"WGS 84\", 6378137.0, 298.257223563, AUTHORITY[\"EPSG\",\"7030\"]], \n AUTHORITY[\"EPSG\",\"6326\"]], \n PRIMEM[\"Greenwich\", 0.0], \n UNIT[\"degree\", 0.017453292519943295], \n AXIS[\"Longitude\", EAST], \n AXIS[\"Latitude\", NORTH]], \n PROJECTION[\"Mollweide\"], \n PARAMETER[\"semi_minor\", 6378137.0], \n PARAMETER[\"false_easting\", 0.0], \n PARAMETER[\"false_northing\", 0.0], \n PARAMETER[\"central_meridian\", 0.0], \n UNIT[\"m\", 1.0], \n AXIS[\"Easting\", EAST], \n AXIS[\"Northing\", NORTH]]" + } + }, + "linkage_rule": "Aggregate future Landsat-derived built-up area to the GHSL native grid before comparison.", + "preserve_native_grid": true, + "resampled_to_project_grid": false +} diff --git a/data/metadata/orchestration/grid_linkage.yaml b/data/metadata/orchestration/grid_linkage.yaml new file mode 100644 index 0000000..d658c3a --- /dev/null +++ b/data/metadata/orchestration/grid_linkage.yaml @@ -0,0 +1,32 @@ +grid_version: 1 +project_grid: + crs: EPSG:32632 + resolution_m: 30.0 + width: 946 + height: 1278 + transform: + - 30.0 + - 0.0 + - 762570.0 + - 0.0 + - -30.0 + - 444150.0 +sources: + landsat: + representation: Earth Engine raster assets + alignment: exact_project_grid + reflectance_resampling: bilinear + count_resampling: nearest_default + srtm: + representation: Earth Engine raster asset + alignment: exact_project_grid + resampling: bilinear + ghsl: + representation: public Earth Engine raster assets + alignment: native_100m_grid + resampled_to_30m: false + osm: + representation: GeoPackage vector layers + crs: EPSG:32632 + clipped_to: yaounde_context_buffer + historical_validity: contemporary_only diff --git a/data/metadata/orchestration/landsat_composite_manifest.csv b/data/metadata/orchestration/landsat_composite_manifest.csv new file mode 100644 index 0000000..213a537 --- /dev/null +++ b/data/metadata/orchestration/landsat_composite_manifest.csv @@ -0,0 +1,6 @@ +epoch,asset_id,count_asset_id,selected_scene_count,selected_scene_ids_sha256,sensors,band_names,crs,transform,width,height,status +2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,2,1fa4b3c866708070cf2cd5dbfc5e7415b27d45b392d74ad47f7916b1ae33006a,"{""LE07"": 2}","blue,green,red,nir,swir1,swir2",EPSG:32632,"[30, 0, 762570, 0, -30, 444150]",946,1278,PASS +2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,48d1d78e637ef0ef938035e504e5c865fb13eb37d4fc38b7061127f80df959e2,"{""LE07"": 1}","blue,green,red,nir,swir1,swir2",EPSG:32632,"[30, 0, 762570, 0, -30, 444150]",946,1278,PASS +2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,2,3dca32e2902873e78d4308d461910859231c74c775786c9e063ed6a14609eed8,"{""LC08"": 2}","blue,green,red,nir,swir1,swir2",EPSG:32632,"[30, 0, 762570, 0, -30, 444150]",946,1278,PASS +2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,33ff83580bdb92f848863baef55b77e81bc1af2327c6ea7d3d61696d8a4e5e9e,"{""LC08"": 1}","blue,green,red,nir,swir1,swir2",EPSG:32632,"[30, 0, 762570, 0, -30, 444150]",946,1278,PASS +2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,172bbb2b7f68ded5e84baa42b2664ab9cce17be89c8c2c24dd35440114b74805,"{""LC08"": 1}","blue,green,red,nir,swir1,swir2",EPSG:32632,"[30, 0, 762570, 0, -30, 444150]",946,1278,PASS diff --git a/data/metadata/orchestration/landsat_epoch_quality.csv b/data/metadata/orchestration/landsat_epoch_quality.csv new file mode 100644 index 0000000..5fe2673 --- /dev/null +++ b/data/metadata/orchestration/landsat_epoch_quality.csv @@ -0,0 +1,6 @@ +epoch,minimum_observation_count,maximum_observation_count,mean_observation_count,median_observation_count,covered_grid_pct,selected_scene_count +2005,0.0,2.0,0.10250970232955166,0.0,9.888765946198596,2 +2010,0.0,1.0,0.2394804580359772,0.0,23.92144710506261,1 +2015,0.0,2.0,0.7192709935913343,0.0,46.63938984764418,2 +2020,0.0,1.0,0.2960409863456048,0.0,29.5719072129924,1 +2025,0.0,1.0,0.005193599936475796,0.0,0.5187810444938401,1 diff --git a/data/metadata/orchestration/orchestrate_version.json b/data/metadata/orchestration/orchestrate_version.json new file mode 100644 index 0000000..3e75ce4 --- /dev/null +++ b/data/metadata/orchestration/orchestrate_version.json @@ -0,0 +1,27 @@ +{ + "catalog_version_sha256": "9119010004d8374433ef5517a5c0e95b7037346526dd3859279bc6e3f673867d", + "compositing_protocol_sha256": "a31dee916b03f507d961be8586623ea01e7c8db8f9395a95638ee7eac973059c", + "day3_version": 1, + "dependency_manifest_sha256": "afd78910352a65f487f4b7be32c88d092e1aec168435f949e6d36ff879c761e2", + "earth_engine_assets": [ + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/terrain/elevation_slope" + ], + "ghsl_epoch_manifest_sha256": "a72c696665905abc86a9e927c2de8dfd4f7c7643a45a503a4e71f9cccd36add6", + "grid_linkage_sha256": "87d33f52f81d5bdcc222f8cda62b2b1b8311a299a2780d8035647182ea9c4d05", + "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", + "landsat_composite_manifest_sha256": "f57f2b6af79216d8f061447554cd6338685baaf4eb1de4d808ed850a8c516951", + "landsat_epoch_quality_sha256": "2376f9f8b44685a60ccee5c50b735425dac2a322dc59ab6cd9c20f90a3369d91", + "osm_source_sha256": "a0dfcf623b5d5565238dfc23590842a98edc9fba8301f4eeb9b31510278d8380", + "stable_signature": "1b250065acaeed32c93e0f76011b4c748a8908d99b935dd153061e986abd5b92", + "task_manifest_sha256": "95f9a5e20ec71535ad0c71552ef1b19e940e5856529964c9dc5dbc86153178c6" +} diff --git a/docs/orchestrate_sources.md b/docs/orchestrate_sources.md new file mode 100644 index 0000000..36c96d9 --- /dev/null +++ b/docs/orchestrate_sources.md @@ -0,0 +1,71 @@ +# Landsat composites and auxiliary-source integration + +Compositing converts the frozen catalogue into seven analysis-ready Landsat +surface-reflectance composites and valid-observation-count assets. It also +creates one aligned terrain asset, records exact GHSL public assets on their +native grid, and freezes a current Geofabrik OpenStreetMap snapshot. + +No Landsat collection is searched dynamically. No spectral index, Otsu +threshold, built-up label or transition is produced during this stage. + +## 1. Preflight + +```bash +make orchestrate-preflight +``` + +This verifies the frozen Day 1 grid, Day 2 catalogue reference, compositing +protocol, seven epochs and exact Earth Engine scene asset IDs. + +## 2. Submit Earth Engine tasks + +```bash +make orchestrate-submit +``` + +This submits: + +- seven six-band Landsat median composites; +- seven total observation-count assets, including sensor-specific bands only + for mixed-sensor epochs; +- one two-band SRTM elevation-and-slope asset. + +GHSL is not exported. Fourteen exact public asset IDs are recorded and kept on +their native grid. + +## 3. Build the OSM snapshot + +```bash +make orchestrate-osm +``` + +The script downloads the current Geofabrik Cameroon GeoPackage archive, +preserves a dated filename and checksum, clips roads, railways and buildings to +the Day 1 context area, and writes EPSG:32632 vector layers. + +The archive is large and remains excluded from Git. + +## 4. Monitor and finalize + +```bash +make orchestrate-status +``` + +After all Earth Engine tasks report `COMPLETED`: + +```bash +make orchestrate-finalize +make test-orchestrate +``` + +## 5. Freeze Day 3 version 1 + +After visual and numerical acceptance: + +```bash +mkdir -p tests/reference +cp data/metadata/orchestrate/orchestrate_version.json \ + tests/reference/orchestrate_sources_v1.json +``` + +Never overwrite the frozen reference automatically during normal execution. diff --git a/reports/orchestration/01_selected_scene_counts.png b/reports/orchestration/01_selected_scene_counts.png new file mode 100644 index 0000000000000000000000000000000000000000..5094caedbbd9feecf1824978706be73619e775c8 GIT binary patch literal 43075 zcmeFa2UL{lwk=xSwh09kML?1{5LHw%*b1VcD4{6Hq7o$M9PE}Lh#)GIB0)hw3Z%%f z00jgD1xs?aKqMA9$G5&Jw0rM;&VBEVao@T3+_xE{$3{0+{q=>l=9+V^h5uD$1xAM5 z3>XZC@!|zJRSaeW5rg?nZqo+%o1vlmx8Q#y?B%c7t63S@I~mv-VlErlTboN zJtg!PlMNDw778=-;0?PGO~g=f9WqEA6{I z$Gvg%zdGPMhL#BaMcFpJUufSwWG~^TeS={+=e(Zw-I*JkVDZq`g{}V;FXomgIZ6`Y z@|YrzQ=b_}B@T`ZPkXLRMkkaneiH3UhCgNniafL|o_jB>8r?-CO$7W>qmr@?b0KVr zFoMCnjT#vu7Bkh#uDU;!a4?;l>j^FmKV^1yda&VKPx*s$vF9I@{IQXTJxF5k_2(?J 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PASS | +| 2015 | {"LC08": 2} | 2 | 46.64% | 0.00 | PASS | +| 2020 | {"LC08": 1} | 1 | 29.57% | 0.00 | PASS | +| 2025 | {"LC08": 1} | 1 | 0.52% | 0.00 | PASS | + +## Auxiliary sources + +| Source | Spatial representation | Temporal representation | Intended role | +|---|---|---|---| +| SRTM | exact_project_grid | static_approximately_2000 | elevation_and_slope_predictors | +| GHSL | native_100m_grid | seven_epochs | auxiliary_benchmark_and_population | +| OpenStreetMap | vector_EPSG32632 | current_snapshot | recent_validation_and_accessibility | + +## Known limitations + +- Landsat 7 SLC-off gaps are filled only by valid observations from other dates. +- Mixed-sensor epochs are documented through sensor-specific count bands. +- GHSL remains a modelled auxiliary source on its native 100 m grid. +- OpenStreetMap is a contemporary snapshot and not historical evidence. +- SRTM represents terrain observed approximately around the year 2000. + +No spectral index, Otsu threshold or built-up label was generated during Day 3. diff --git a/src/analysis/auxiliary/__init__.py b/src/analysis/auxiliary/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/analysis/auxiliary/build_osm_extract.py b/src/analysis/auxiliary/build_osm_extract.py new file mode 100644 index 0000000..5bf6ae7 --- /dev/null +++ b/src/analysis/auxiliary/build_osm_extract.py @@ -0,0 +1,254 @@ +"""Download, date, checksum and clip the current Geofabrik Cameroon snapshot. + +The implementation uses Geofabrik's free GeoPackage archive to avoid a hard +runtime dependency on an OSM parser. Spatial filtering is applied while reading +large source layers, followed by exact clipping to the Day 1 context geometry. +""" + +from __future__ import annotations + +import argparse +import email.utils +import json +import shutil +import urllib.request +import zipfile +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import geopandas as gpd +import pandas as pd + +from src.analysis.orchestration.common import ( + find_project_root, + load_yaml, + resolve_project_path, + sha256_file, + write_json, +) + + +def source_snapshot_date(last_modified: str | None) -> str: + """Convert an HTTP Last-Modified header to a stable ISO snapshot date.""" + if last_modified: + parsed = email.utils.parsedate_to_datetime(last_modified) + return parsed.astimezone(timezone.utc).date().isoformat() + + return datetime.now(timezone.utc).date().isoformat() + + +def download_geofabrik_archive( + url: str, + raw_directory: Path, + force: bool, +) -> tuple[Path, dict[str, Any]]: + """Download the current Geofabrik archive and preserve a dated filename.""" + raw_directory.mkdir(parents=True, exist_ok=True) + request = urllib.request.Request(url, method="GET") + + with urllib.request.urlopen(request) as response: + last_modified = response.headers.get("Last-Modified") + snapshot_date = source_snapshot_date(last_modified) + dated_name = f"cameroon_{snapshot_date.replace('-', '')}_free.gpkg.zip" + destination = raw_directory / dated_name + + if destination.exists() and not force: + metadata = { + "snapshot_date": snapshot_date, + "source_last_modified": last_modified, + "download_skipped_existing_file": True, + } + return destination, metadata + + with destination.open("wb") as output: + shutil.copyfileobj(response, output) + + metadata = { + "snapshot_date": snapshot_date, + "source_last_modified": last_modified, + "download_skipped_existing_file": False, + } + return destination, metadata + + +def extract_source_geopackage(archive: Path, raw_directory: Path) -> Path: + """Extract and return the single GeoPackage contained in the archive.""" + extraction_directory = raw_directory / archive.stem + extraction_directory.mkdir(parents=True, exist_ok=True) + + with zipfile.ZipFile(archive) as bundle: + bundle.extractall(extraction_directory) + + candidates = list(extraction_directory.rglob("*.gpkg")) + + if len(candidates) != 1: + raise RuntimeError( + f"Expected one GeoPackage in {archive}, found {len(candidates)}." + ) + + return candidates[0] + + +def read_layer_with_bbox( + source: Path, + layer: str, + bbox: tuple[float, float, float, float], +) -> gpd.GeoDataFrame: + """Read only features intersecting a WGS84 bounding box when supported.""" + try: + return gpd.read_file(source, layer=layer, bbox=bbox, engine="pyogrio") + except Exception as error: + raise RuntimeError( + f"Could not read layer '{layer}' from {source}. Confirm that the " + "Geofabrik layer names in day3_sources.yaml match the archive." + ) from error + + +def clip_and_annotate( + frame: gpd.GeoDataFrame, + context: gpd.GeoDataFrame, + output_crs: str, + snapshot_date: str, +) -> gpd.GeoDataFrame: + """Clip one OSM layer and attach explicit contemporary-use metadata.""" + if frame.empty: + return gpd.GeoDataFrame(frame, geometry="geometry", crs=output_crs) + + projected = frame.to_crs(output_crs) + context_projected = context.to_crs(output_crs) + clipped = gpd.clip(projected, context_projected) + clipped = clipped[clipped.geometry.notna() & ~clipped.geometry.is_empty].copy() + clipped["osm_snapshot_date"] = snapshot_date + clipped["historical_validity"] = "contemporary_only" + clipped["primary_historical_predictor"] = False + return clipped + + +def filter_classes( + frame: gpd.GeoDataFrame, + class_field: str, + selected_classes: list[str], +) -> gpd.GeoDataFrame: + """Retain only explicitly configured OSM feature classes.""" + if class_field not in frame.columns: + raise KeyError( + f"Configured OSM class field '{class_field}' is absent. " + f"Available columns: {list(frame.columns)}" + ) + + return frame[frame[class_field].astype(str).isin(selected_classes)].copy() + + +def run_osm(config_path: Path, force_download: bool) -> dict[str, Any]: + """Execute the complete current-snapshot OSM extraction workflow.""" + project_root = find_project_root(config_path.parent) + config = load_yaml(config_path) + osm_config = config["osm"] + + raw_directory = resolve_project_path(osm_config["raw_directory"], project_root) + archive, download_metadata = download_geofabrik_archive( + osm_config["source_url"], + raw_directory, + force_download, + ) + source_gpkg = extract_source_geopackage(archive, raw_directory) + + context_path = resolve_project_path(config["regions"]["context"], project_root) + context = gpd.read_file(context_path) + + if context.crs is None or len(context) != 1: + raise ValueError("The context boundary must be one valid georeferenced feature.") + + context_wgs84 = context.to_crs("EPSG:4326") + bbox = tuple(float(value) for value in context_wgs84.total_bounds) + layers = osm_config["layers"] + class_field = osm_config["class_field"] + + roads = read_layer_with_bbox(source_gpkg, layers["roads"], bbox) + roads = filter_classes( + roads, + class_field, + list(osm_config["selected_road_classes"]), + ) + railways = read_layer_with_bbox(source_gpkg, layers["railways"], bbox) + railways = filter_classes( + railways, + class_field, + list(osm_config["selected_railway_classes"]), + ) + buildings = read_layer_with_bbox(source_gpkg, layers["buildings"], bbox) + + snapshot_date = download_metadata["snapshot_date"] + output_crs = osm_config["output_crs"] + outputs = { + "roads_major": clip_and_annotate( + roads, context, output_crs, snapshot_date + ), + "railways": clip_and_annotate( + railways, context, output_crs, snapshot_date + ), + "buildings_reference": clip_and_annotate( + buildings, context, output_crs, snapshot_date + ), + } + + output_path = resolve_project_path(osm_config["processed_output"], project_root) + output_path.parent.mkdir(parents=True, exist_ok=True) + + if output_path.exists(): + output_path.unlink() + + for layer_name, frame in outputs.items(): + frame.to_file(output_path, layer=layer_name, driver="GPKG") + + checksum = sha256_file(archive) + checksum_path = resolve_project_path(osm_config["source_checksum"], project_root) + checksum_path.parent.mkdir(parents=True, exist_ok=True) + checksum_path.write_text(f"{checksum} {archive.name}\n", encoding="utf-8") + + metadata = { + "provider": osm_config["provider"], + "source_dataset": "OpenStreetMap Cameroon extract", + "source_format": "Geofabrik free GeoPackage archive", + "source_url": osm_config["source_url"], + "snapshot_date": snapshot_date, + "download_date": datetime.now(timezone.utc).date().isoformat(), + "source_filename": archive.name, + "source_sha256": checksum, + "licence": "Open Database Licence (ODbL)", + "temporal_interpretation": "current snapshot only", + "processed_crs": output_crs, + "processed_output": str(output_path), + "feature_counts": { + name: int(len(frame)) for name, frame in outputs.items() + }, + **download_metadata, + } + write_json( + resolve_project_path(osm_config["source_metadata"], project_root), + metadata, + ) + + print(json.dumps(metadata, indent=2)) + return metadata + + +def parse_arguments() -> argparse.Namespace: + """Parse current-snapshot OSM extraction arguments.""" + parser = argparse.ArgumentParser( + description="Download and clip the current Geofabrik Cameroon extract." + ) + parser.add_argument("--config", required=True, type=Path) + parser.add_argument("--force-download", action="store_true") + return parser.parse_args() + + +def main() -> None: + """Run the OSM extraction command.""" + arguments = parse_arguments() + run_osm(arguments.config.resolve(), arguments.force_download) + + +if __name__ == "__main__": + main() diff --git a/src/analysis/landsat/build_composites.py b/src/analysis/landsat/build_composites.py new file mode 100644 index 0000000..2e8376d --- /dev/null +++ b/src/analysis/landsat/build_composites.py @@ -0,0 +1,520 @@ +"""Submit exact-scene Landsat composites, count layers and terrain assets. + +The script never searches Landsat collections. It reloads only the exact asset +IDs frozen during Day 2, reuses the Day 2 QA-mask implementation, harmonises +sensor bands, applies Collection 2 scaling and submits asynchronous Earth +Engine exports on the exact Day 1 grid. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import ee +import pandas as pd + +from src.analysis.orchestration.common import ( + asset_exists, + ensure_asset_folder, + exact_grid_region, + find_project_root, + initialize_earth_engine, + load_grid_specification, + load_yaml, + metadata_directory, + resolve_project_path, + stable_object_hash, + write_json, + write_yaml, +) +from src.analysis.orchestration.preflight import asset_id_column, run_preflight +from src.analysis.landsat.qa_masks import landsat_valid_mask + + +LANDSAT_BANDS = { + "LT05": { + "SR_B1": "blue", + "SR_B2": "green", + "SR_B3": "red", + "SR_B4": "nir", + "SR_B5": "swir1", + "SR_B7": "swir2", + }, + "LE07": { + "SR_B1": "blue", + "SR_B2": "green", + "SR_B3": "red", + "SR_B4": "nir", + "SR_B5": "swir1", + "SR_B7": "swir2", + }, + "LC08": { + "SR_B2": "blue", + "SR_B3": "green", + "SR_B4": "red", + "SR_B5": "nir", + "SR_B6": "swir1", + "SR_B7": "swir2", + }, +} + + +def prepare_surface_reflectance( + image: ee.Image, + sensor_key: str, + day3_config: dict[str, Any], + day2_config: dict[str, Any], +) -> tuple[ee.Image, ee.Image]: + """Scale, mask and harmonise one exact selected Landsat scene.""" + if sensor_key not in LANDSAT_BANDS: + raise ValueError(f"Unsupported sensor key: {sensor_key}") + + mapping = LANDSAT_BANDS[sensor_key] + valid_mask = landsat_valid_mask( + image, + sensor_key, + day2_config["collections"], + day2_config["quality_mask"], + ) + + prepared = ( + image.select(list(mapping), list(mapping.values())) + .multiply(float(day3_config["landsat"]["optical_scale"])) + .add(float(day3_config["landsat"]["optical_offset"])) + .updateMask(valid_mask) + .resample("bilinear") + .toFloat() + .copyProperties(image, image.propertyNames()) + ) + + return prepared, valid_mask.rename("valid") + + +def build_epoch_products( + epoch_manifest: pd.DataFrame, + id_column: str, + day3_config: dict[str, Any], + day2_config: dict[str, Any], +) -> tuple[ee.Image, ee.Image, dict[str, int]]: + """Build one six-band median composite and multi-band count image.""" + prepared_images: list[ee.Image] = [] + all_masks: list[ee.Image] = [] + masks_by_sensor: dict[str, list[ee.Image]] = {} + + for row in epoch_manifest.itertuples(index=False): + values = row._asdict() + sensor_key = str(values["sensor_key"]) + asset_id = str(values[id_column]) + image = ee.Image(asset_id) + prepared, valid_mask = prepare_surface_reflectance( + image, + sensor_key, + day3_config, + day2_config, + ) + prepared_images.append(prepared) + all_masks.append(valid_mask) + masks_by_sensor.setdefault(sensor_key, []).append(valid_mask) + + if not prepared_images: + raise ValueError("An epoch cannot be composited without selected scenes.") + + valid_count = ( + ee.ImageCollection.fromImages(all_masks) + .sum() + .rename("valid_observation_count") + .unmask(0) + .toUint16() + ) + + count_image = valid_count + + if len(masks_by_sensor) > 1: + for sensor_key in sorted(masks_by_sensor): + sensor_count = ( + ee.ImageCollection.fromImages(masks_by_sensor[sensor_key]) + .sum() + .rename(f"valid_count_{sensor_key.lower()}") + .unmask(0) + .toUint16() + ) + count_image = count_image.addBands(sensor_count) + + common_bands = list(day3_config["landsat"]["common_bands"]) + composite = ( + ee.ImageCollection.fromImages(prepared_images) + .median() + .select(common_bands) + .updateMask(valid_count.gt(0)) + .toFloat() + ) + + scene_counts = { + sensor: len(images) + for sensor, images in sorted(masks_by_sensor.items()) + } + return composite, count_image, scene_counts + + +def start_image_export( + image: ee.Image, + description: str, + asset_id: str, + grid: dict[str, Any], + config: dict[str, Any], + pyramiding_policy: str, +) -> dict[str, Any]: + """Start one exact-grid Earth Engine asset export or record an existing asset.""" + overwrite = bool(config["exports"]["overwrite_existing_assets"]) + + if asset_exists(asset_id) and not overwrite: + return { + "task_id": "", + "state": "EXISTS", + "description": description, + "asset_id": asset_id, + } + + task = ee.batch.Export.image.toAsset( + image=image, + description=description, + assetId=asset_id, + pyramidingPolicy={".default": pyramiding_policy}, + region=exact_grid_region(grid), + crs=str(grid["crs"]), + crsTransform=[float(value) for value in grid["transform"]], + maxPixels=int(config["exports"]["max_pixels"]), + shardSize=int(config["exports"]["shard_size"]), + priority=int(config["exports"]["priority"]), + overwrite=overwrite, + ) + task.start() + status = task.status() + + return { + "task_id": status.get("id", task.id), + "state": status.get("state", "READY"), + "description": description, + "asset_id": asset_id, + } + + +def build_terrain_image(config: dict[str, Any]) -> ee.Image: + """Create the static elevation-and-slope predictor image.""" + dem = ee.Image(config["srtm"]["dataset"]).select( + config["srtm"]["elevation_band"] + ) + elevation = dem.rename("elevation_m") + slope = ee.Terrain.slope(dem).rename("slope_degrees") + return ee.Image.cat([elevation, slope]).resample("bilinear").toFloat() + + +def build_ghsl_manifest( + config: dict[str, Any], + metadata_dir: Path, +) -> None: + """Record exact GHSL public assets without resampling or exporting them.""" + rows: list[dict[str, Any]] = [] + + for epoch in config["ghsl"]["epochs"]: + rows.extend( + [ + { + "source": "GHSL_BUILT_SURFACE", + "epoch": int(epoch), + "asset_id": f"{config['ghsl']['built_collection']}/{epoch}", + "band": config["ghsl"]["built_band"], + "representation": "native_100m_grid", + "resampled_to_30m": False, + "ground_truth": False, + }, + { + "source": "GHSL_POPULATION", + "epoch": int(epoch), + "asset_id": ( + f"{config['ghsl']['population_collection']}/{epoch}" + ), + "band": config["ghsl"]["population_band"], + "representation": "native_100m_grid", + "resampled_to_30m": False, + "ground_truth": False, + }, + ] + ) + + pd.DataFrame(rows).to_csv( + metadata_dir / config["metadata"]["ghsl_epoch_manifest"], + index=False, + ) + + sample_assets = { + "built_surface": rows[0]["asset_id"], + "population": rows[1]["asset_id"], + } + native_grid: dict[str, Any] = { + "preserve_native_grid": True, + "resampled_to_project_grid": False, + "linkage_rule": ( + "Aggregate future Landsat-derived built-up area to the GHSL " + "native grid before comparison." + ), + "assets": {}, + } + + for source_name, asset_id in sample_assets.items(): + image = ee.Image(asset_id) + projection = image.select(0).projection().getInfo() + native_grid["assets"][source_name] = projection + + write_json( + metadata_dir / config["metadata"]["ghsl_native_grid"], + native_grid, + ) + + +def write_grid_linkage( + config: dict[str, Any], + grid: dict[str, Any], + metadata_dir: Path, +) -> None: + """Document whether every source is aligned or linked to the project grid.""" + linkage = { + "grid_version": 1, + "project_grid": { + "crs": grid["crs"], + "resolution_m": grid["resolution_m"], + "width": grid["width"], + "height": grid["height"], + "transform": grid["transform"], + }, + "sources": { + "landsat": { + "representation": "Earth Engine raster assets", + "alignment": "exact_project_grid", + "reflectance_resampling": "bilinear", + "count_resampling": "nearest_default", + }, + "srtm": { + "representation": "Earth Engine raster asset", + "alignment": "exact_project_grid", + "resampling": "bilinear", + }, + "ghsl": { + "representation": "public Earth Engine raster assets", + "alignment": "native_100m_grid", + "resampled_to_30m": False, + }, + "osm": { + "representation": "GeoPackage vector layers", + "crs": config["osm"]["output_crs"], + "clipped_to": "yaounde_context_buffer", + "historical_validity": "contemporary_only", + }, + }, + } + write_yaml( + metadata_dir / config["metadata"]["grid_linkage"], + linkage, + ) + + +def run_submission(config_path: Path, submit: bool) -> pd.DataFrame: + """Validate dependencies and submit all minimal Day 3 Earth Engine tasks.""" + if not submit: + raise ValueError("Pass --submit to create Earth Engine export tasks.") + + preflight = run_preflight(config_path) + project_root = find_project_root(config_path.parent) + config = load_yaml(config_path) + day2_config = load_yaml( + resolve_project_path(config["inputs"]["landsat_catalog_config"], project_root) + ) + grid = load_grid_specification( + resolve_project_path(config["inputs"]["grid_specification"], project_root) + ) + manifest = pd.read_csv( + resolve_project_path( + config["inputs"]["selected_scene_manifest"], project_root + ) + ) + id_column = asset_id_column(manifest) + + initialize_earth_engine(config["project"]["earth_engine_project"]) + + asset_root = config["exports"]["asset_root"].rstrip("/") + landsat_folder = f"{asset_root}/{config['exports']['landsat_folder']}" + terrain_folder = f"{asset_root}/{config['exports']['terrain_folder']}" + ensure_asset_folder(asset_root) + ensure_asset_folder(landsat_folder) + ensure_asset_folder(terrain_folder) + + tasks: list[dict[str, Any]] = [] + + for epoch in config["landsat"]["epochs"]: + epoch_manifest = manifest[manifest["epoch"].astype(int) == int(epoch)] + + # Some epochs (e.g., 1990, 1995, 2000) may have no selected scenes because no + # Landsat imagery exists for that period over the study area. Skipping them is + # expected and does not indicate a failure. + if epoch_manifest.empty: + print(f"Skipping epoch {epoch}: no selected scenes in manifest.") + continue + + composite, count_image, scene_counts = build_epoch_products( + epoch_manifest, + id_column, + config, + day2_config, + ) + scene_ids = sorted(epoch_manifest[id_column].astype(str).tolist()) + scene_hash = stable_object_hash(scene_ids) + + composite_asset = f"{landsat_folder}/composite_{epoch}" + count_asset = f"{landsat_folder}/valid_count_{epoch}" + + composite = composite.set( + { + "epoch": int(epoch), + "selected_scene_count": int(len(epoch_manifest)), + "selected_scene_ids_sha256": scene_hash, + "scene_counts_by_sensor": json.dumps(scene_counts, sort_keys=True), + "day3_version": int(config["version"]), + } + ) + count_image = count_image.set( + { + "epoch": int(epoch), + "selected_scene_count": int(len(epoch_manifest)), + "selected_scene_ids_sha256": scene_hash, + "day3_version": int(config["version"]), + } + ) + + composite_task = start_image_export( + composite, + f"sprawlgp_composite_{epoch}", + composite_asset, + grid, + config, + config["exports"]["reflectance_pyramiding_policy"], + ) + composite_task.update( + { + "product_type": "landsat_composite", + "epoch": int(epoch), + "selected_scene_count": int(len(epoch_manifest)), + "selected_scene_ids_sha256": scene_hash, + } + ) + tasks.append(composite_task) + + count_task = start_image_export( + count_image, + f"sprawlgp_valid_count_{epoch}", + count_asset, + grid, + config, + config["exports"]["count_pyramiding_policy"], + ) + count_task.update( + { + "product_type": "landsat_valid_count", + "epoch": int(epoch), + "selected_scene_count": int(len(epoch_manifest)), + "selected_scene_ids_sha256": scene_hash, + } + ) + tasks.append(count_task) + + terrain_asset = f"{terrain_folder}/elevation_slope" + terrain_task = start_image_export( + build_terrain_image(config), + "sprawlgp_elevation_slope", + terrain_asset, + grid, + config, + config["exports"]["terrain_pyramiding_policy"], + ) + terrain_task.update( + { + "product_type": "terrain", + "epoch": "static", + "selected_scene_count": 0, + "selected_scene_ids_sha256": "", + } + ) + tasks.append(terrain_task) + + metadata_dir = metadata_directory(config, project_root) + task_frame = pd.DataFrame(tasks) + task_frame.to_csv( + resolve_project_path(config["exports"]["task_manifest"], project_root), + index=False, + ) + build_ghsl_manifest(config, metadata_dir) + write_grid_linkage(config, grid, metadata_dir) + + auxiliary = pd.DataFrame( + [ + { + "source": "SRTM", + "asset_id": terrain_asset, + "representation": "exact_project_grid", + "temporal_representation": "static_approximately_2000", + "intended_role": "elevation_and_slope_predictors", + }, + { + "source": "GHSL", + "asset_id": "see_ghsl_epoch_manifest.csv", + "representation": "native_100m_grid", + "temporal_representation": "seven_epochs", + "intended_role": "auxiliary_benchmark_and_population", + }, + { + "source": "OpenStreetMap", + "asset_id": config["osm"]["processed_output"], + "representation": "vector_EPSG32632", + "temporal_representation": "current_snapshot", + "intended_role": "recent_validation_and_accessibility", + }, + ] + ) + auxiliary.to_csv( + metadata_dir / config["metadata"]["auxiliary_source_manifest"], + index=False, + ) + + print( + json.dumps( + { + "preflight_status": preflight["status"], + "submitted_or_existing_tasks": len(task_frame), + "task_manifest": config["exports"]["task_manifest"], + }, + indent=2, + ) + ) + return task_frame + + +def parse_arguments() -> argparse.Namespace: + """Parse the Day 3 export-submission command-line arguments.""" + parser = argparse.ArgumentParser( + description="Submit exact-scene Landsat and terrain assets." + ) + parser.add_argument("--config", required=True, type=Path) + parser.add_argument("--submit", action="store_true") + return parser.parse_args() + + +def main() -> None: + """Run the export-submission command.""" + arguments = parse_arguments() + run_submission(arguments.config.resolve(), arguments.submit) + + +if __name__ == "__main__": + main() diff --git a/src/analysis/orchestration/__init__.py b/src/analysis/orchestration/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/analysis/orchestration/common.py b/src/analysis/orchestration/common.py new file mode 100644 index 0000000..026495f --- /dev/null +++ b/src/analysis/orchestration/common.py @@ -0,0 +1,208 @@ +"""Shared configuration, provenance, grid and Earth Engine utilities for Day 3.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +from typing import Any + +import ee +import yaml + + +PROJECT_MARKERS = ("pyproject.toml", ".git") + + +def find_project_root(start: Path | None = None) -> Path: + """Locate the repository root by walking upward from a starting path.""" + current = (start or Path.cwd()).resolve() + + for candidate in (current, *current.parents): + if any((candidate / marker).exists() for marker in PROJECT_MARKERS): + return candidate + + raise FileNotFoundError( + "Could not locate the repository root. Run the command from inside " + "the project or ensure pyproject.toml exists." + ) + + +def resolve_project_path(path_value: str | Path, project_root: Path) -> Path: + """Resolve an absolute or repository-relative path consistently.""" + path = Path(path_value).expanduser() + return path if path.is_absolute() else project_root / path + + +def load_yaml(path: Path) -> dict[str, Any]: + """Load a YAML mapping and reject missing or malformed documents.""" + if not path.is_file(): + raise FileNotFoundError(f"YAML file not found: {path}") + + with path.open("r", encoding="utf-8") as stream: + content = yaml.safe_load(stream) + + if not isinstance(content, dict): + raise ValueError(f"Expected a YAML mapping in {path}.") + + return content + + +def load_json(path: Path) -> dict[str, Any]: + """Load a JSON object and reject missing or non-object documents.""" + if not path.is_file(): + raise FileNotFoundError(f"JSON file not found: {path}") + + content = json.loads(path.read_text(encoding="utf-8")) + + if not isinstance(content, dict): + raise ValueError(f"Expected a JSON object in {path}.") + + return content + + +def write_json(path: Path, content: dict[str, Any]) -> None: + """Write deterministic, human-readable JSON metadata.""" + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + json.dumps(content, indent=2, sort_keys=True, default=str) + "\n", + encoding="utf-8", + ) + + +def write_yaml(path: Path, content: dict[str, Any]) -> None: + """Write YAML metadata while preserving the supplied key order.""" + path.parent.mkdir(parents=True, exist_ok=True) + + with path.open("w", encoding="utf-8") as stream: + yaml.safe_dump(content, stream, sort_keys=False, allow_unicode=True) + + +def sha256_file(path: Path) -> str: + """Calculate a SHA-256 checksum without loading the whole file in memory.""" + digest = hashlib.sha256() + + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + + return digest.hexdigest() + + +def stable_object_hash(value: Any) -> str: + """Hash a JSON-serialisable object using canonical key ordering.""" + payload = json.dumps( + value, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + default=str, + ).encode("utf-8") + return hashlib.sha256(payload).hexdigest() + + +def initialize_earth_engine(project: str) -> None: + """Initialize Earth Engine with an actionable authentication error.""" + try: + ee.Initialize(project=project) + except Exception as error: + raise RuntimeError( + "Earth Engine initialization failed. Authenticate once with " + "`earthengine authenticate` and confirm that the configured " + f"project '{project}' has Earth Engine access." + ) from error + + +def load_grid_specification(path: Path) -> dict[str, Any]: + """Load and validate the authoritative Day 1 grid definition.""" + grid = load_json(path) + required = { + "crs", + "resolution_m", + "transform", + "width", + "height", + "extent", + } + missing = required.difference(grid) + + if missing: + raise ValueError(f"Missing grid fields: {sorted(missing)}") + + if len(grid["transform"]) != 6: + raise ValueError("The frozen affine transform must contain six values.") + + return grid + + +def exact_grid_region(grid: dict[str, Any]) -> ee.Geometry: + """Create the exact projected export rectangle from the frozen grid extent.""" + extent = grid["extent"] + return ee.Geometry.Rectangle( + [ + float(extent["xmin"]), + float(extent["ymin"]), + float(extent["xmax"]), + float(extent["ymax"]), + ], + proj=str(grid["crs"]), + geodesic=False, + ) + + +def asset_exists(asset_id: str) -> bool: + """Return whether an Earth Engine asset exists without hiding API errors.""" + try: + ee.data.getAsset(asset_id) + return True + except Exception as error: + message = str(error).casefold() + + if "not found" in message or "does not exist" in message: + return False + + raise + + +def ensure_asset_folder(folder_id: str) -> None: + """Create all missing folders below a project asset root.""" + marker = "/assets/" + + if marker not in folder_id: + raise ValueError( + "Asset folders must use the projects//assets/... form." + ) + + prefix, suffix = folder_id.split(marker, 1) + current = f"{prefix}{marker.rstrip('/')}" + + for part in [value for value in suffix.split("/") if value]: + current = f"{current}/{part}" + + if not asset_exists(current): + ee.data.createAsset({"type": "FOLDER"}, current) + + +def metadata_directory(config: dict[str, Any], project_root: Path) -> Path: + """Create and return the configured Day 3 metadata directory.""" + path = resolve_project_path(config["metadata"]["directory"], project_root) + path.mkdir(parents=True, exist_ok=True) + return path + + +def report_directory(config: dict[str, Any], project_root: Path) -> Path: + """Create and return the configured Day 3 report directory.""" + path = resolve_project_path(config["reports"]["directory"], project_root) + path.mkdir(parents=True, exist_ok=True) + return path + + +def task_status(task_id: str) -> dict[str, Any]: + """Retrieve one submitted task by ID using the public task-list interface.""" + for task in ee.batch.Task.list(): + if task.id == task_id: + return task.status() + + raise LookupError( + f"Earth Engine task '{task_id}' was not found among recent tasks." + ) diff --git a/src/analysis/orchestration/finalize.py b/src/analysis/orchestration/finalize.py new file mode 100644 index 0000000..4979ac4 --- /dev/null +++ b/src/analysis/orchestration/finalize.py @@ -0,0 +1,499 @@ +"""Monitor Day 3 tasks, validate completed assets and create final metadata.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import ee +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from .common import ( + asset_exists, + exact_grid_region, + find_project_root, + initialize_earth_engine, + load_grid_specification, + load_json, + load_yaml, + metadata_directory, + report_directory, + resolve_project_path, + sha256_file, + stable_object_hash, + write_json, +) + + +TERMINAL_SUCCESS_STATES = {"COMPLETED", "EXISTS"} +TERMINAL_FAILURE_STATES = {"FAILED", "CANCELLED", "CANCEL_REQUESTED"} + + +def refresh_task_manifest(task_frame: pd.DataFrame) -> pd.DataFrame: + """Refresh submitted task states with one Earth Engine task-list request.""" + refreshed = task_frame.copy() + available = { + task.id: task.status() + for task in ee.batch.Task.list() + if task.id is not None + } + + for index, row in refreshed.iterrows(): + if str(row["state"]) == "EXISTS" or not str(row["task_id"]): + continue + + task_id = str(row["task_id"]) + + if task_id not in available: + raise LookupError( + f"Earth Engine task '{task_id}' was not found among recent tasks." + ) + + status = available[task_id] + refreshed.at[index, "state"] = status.get("state", "UNKNOWN") + refreshed.at[index, "error_message"] = status.get("error_message", "") + + return refreshed + + +def projection_summary(image: ee.Image) -> dict[str, Any]: + """Return first-band CRS and affine transform metadata.""" + projection = image.select(0).projection().getInfo() + return { + "crs": projection.get("crs"), + "transform": projection.get("transform"), + } + + +def count_statistics( + image: ee.Image, + grid: dict[str, Any], + selected_scene_count: int, +) -> dict[str, float]: + """Calculate compact observation-depth QA statistics on the project grid.""" + count_band = image.select("valid_observation_count") + reducer = ( + ee.Reducer.minMax() + .combine(ee.Reducer.mean(), sharedInputs=True) + .combine(ee.Reducer.median(), sharedInputs=True) + ) + values = count_band.reduceRegion( + reducer=reducer, + geometry=exact_grid_region(grid), + crs=grid["crs"], + crsTransform=grid["transform"], + maxPixels=10_000_000, + tileScale=4, + ).getInfo() + + coverage = ( + ee.Image.pixelArea() + .updateMask(count_band.gt(0)) + .rename("covered_area") + .reduceRegion( + reducer=ee.Reducer.sum(), + geometry=exact_grid_region(grid), + crs=grid["crs"], + crsTransform=grid["transform"], + maxPixels=10_000_000, + tileScale=4, + ) + .get("covered_area") + .getInfo() + ) + grid_area = float(grid["width"] * grid["height"] * grid["resolution_m"] ** 2) + + return { + "minimum_observation_count": float( + values.get("valid_observation_count_min", 0) + ), + "maximum_observation_count": float( + values.get("valid_observation_count_max", 0) + ), + "mean_observation_count": float( + values.get("valid_observation_count_mean", 0) + ), + "median_observation_count": float( + values.get("valid_observation_count_median", 0) + ), + "covered_grid_pct": float(coverage or 0) / grid_area * 100, + "selected_scene_count": int(selected_scene_count), + } + + +def validate_asset_grid( + asset_id: str, + expected_bands: list[str], + grid: dict[str, Any], +) -> tuple[ee.Image, dict[str, Any]]: + """Validate asset existence, band names, CRS and exact affine transform.""" + if not asset_exists(asset_id): + raise FileNotFoundError(f"Earth Engine asset not found: {asset_id}") + + image = ee.Image(asset_id) + bands = image.bandNames().getInfo() + + if bands != expected_bands: + raise ValueError( + f"Unexpected bands in {asset_id}: {bands}; expected {expected_bands}." + ) + + projection = projection_summary(image) + + if projection["crs"] != grid["crs"]: + raise ValueError( + f"Unexpected CRS in {asset_id}: {projection['crs']}" + ) + + observed_transform = [float(value) for value in projection["transform"]] + expected_transform = [float(value) for value in grid["transform"]] + + if not np.allclose(observed_transform, expected_transform, atol=1e-9): + raise ValueError( + f"Unexpected transform in {asset_id}: {observed_transform}" + ) + + return image, projection + + +def save_figure(figure: plt.Figure, path: Path) -> None: + """Save one compact QA figure and immediately release its memory.""" + path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(path, dpi=200, bbox_inches="tight") + plt.close(figure) + + +def create_figures( + composite_manifest: pd.DataFrame, + epoch_quality: pd.DataFrame, + auxiliary_manifest: pd.DataFrame, + report_dir: Path, + config: dict[str, Any], +) -> None: + """Create three lightweight figures from validated metadata tables.""" + ordered = composite_manifest.sort_values("epoch") + figure, axis = plt.subplots(figsize=(10, 6)) + axis.bar(ordered["epoch"].astype(str), ordered["selected_scene_count"]) + axis.set_title("Selected Landsat scene count by epoch") + axis.set_xlabel("Epoch") + axis.set_ylabel("Selected scenes") + save_figure( + figure, + report_dir / config["reports"]["selected_scene_counts_figure"], + ) + + quality = epoch_quality.sort_values("epoch") + figure, axis = plt.subplots(figsize=(10, 6)) + axis.plot( + quality["epoch"], + quality["median_observation_count"], + marker="o", + label="Median", + ) + axis.plot( + quality["epoch"], + quality["maximum_observation_count"], + marker="o", + label="Maximum", + ) + axis.set_title("Valid-observation depth by epoch") + axis.set_xlabel("Epoch") + axis.set_ylabel("Observation count") + axis.legend() + save_figure( + figure, + report_dir / config["reports"]["observation_depth_figure"], + ) + + alignment_counts = auxiliary_manifest["representation"].value_counts() + figure, axis = plt.subplots(figsize=(10, 5)) + axis.bar(alignment_counts.index.astype(str), alignment_counts.values) + axis.set_title("Auxiliary-source spatial representations") + axis.set_xlabel("Representation") + axis.set_ylabel("Source count") + axis.tick_params(axis="x", rotation=20) + save_figure( + figure, + report_dir / config["reports"]["source_alignment_figure"], + ) + + +def write_report( + composite_manifest: pd.DataFrame, + epoch_quality: pd.DataFrame, + auxiliary_manifest: pd.DataFrame, + output_path: Path, +) -> None: + """Write a concise Day 3 report from validated manifests.""" + lines = [ + "# Landsat composites and auxiliary-source integration", + "", + "## Landsat outputs", + "", + "| Epoch | Sensors | Scenes | Covered grid | Median observations | Status |", + "|---:|---|---:|---:|---:|:---:|", + ] + + quality_index = epoch_quality.set_index("epoch") + + for row in composite_manifest.sort_values("epoch").itertuples(index=False): + quality = quality_index.loc[int(row.epoch)] + lines.append( + "| " + f"{int(row.epoch)} | {row.sensors} | {int(row.selected_scene_count)} | " + f"{float(quality['covered_grid_pct']):.2f}% | " + f"{float(quality['median_observation_count']):.2f} | PASS |" + ) + + lines.extend( + [ + "", + "## Auxiliary sources", + "", + "| Source | Spatial representation | Temporal representation | Intended role |", + "|---|---|---|---|", + ] + ) + + for row in auxiliary_manifest.itertuples(index=False): + lines.append( + "| " + f"{row.source} | {row.representation} | " + f"{row.temporal_representation} | {row.intended_role} |" + ) + + lines.extend( + [ + "", + "## Known limitations", + "", + "- Landsat 7 SLC-off gaps are filled only by valid observations from other dates.", + "- Mixed-sensor epochs are documented through sensor-specific count bands.", + "- GHSL remains a modelled auxiliary source on its native 100 m grid.", + "- OpenStreetMap is a contemporary snapshot and not historical evidence.", + "- SRTM represents terrain observed approximately around the year 2000.", + "", + "No spectral index, Otsu threshold or built-up label was generated during Day 3.", + ] + ) + output_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def run_finalize(config_path: Path, status_only: bool) -> dict[str, Any]: + """Refresh task states and finalize metadata after all exports complete.""" + project_root = find_project_root(config_path.parent) + config = load_yaml(config_path) + initialize_earth_engine(config["project"]["earth_engine_project"]) + + task_path = resolve_project_path(config["exports"]["task_manifest"], project_root) + + if not task_path.is_file(): + raise FileNotFoundError("Submit Day 3 tasks before checking their status.") + + tasks = pd.read_csv(task_path, keep_default_na=False) + tasks = refresh_task_manifest(tasks) + tasks.to_csv(task_path, index=False) + + state_counts = tasks["state"].value_counts().to_dict() + print(json.dumps({"task_states": state_counts}, indent=2)) + + if status_only: + return {"task_states": state_counts} + + failures = tasks[tasks["state"].isin(TERMINAL_FAILURE_STATES)] + + if not failures.empty: + raise RuntimeError( + "At least one Day 3 export failed. Inspect earth_engine_tasks.csv." + ) + + incomplete = tasks[~tasks["state"].isin(TERMINAL_SUCCESS_STATES)] + + if not incomplete.empty: + raise RuntimeError( + "Earth Engine exports are not complete. Run `make day3-status` later." + ) + + grid = load_grid_specification( + resolve_project_path(config["inputs"]["grid_specification"], project_root) + ) + composite_rows: list[dict[str, Any]] = [] + quality_rows: list[dict[str, Any]] = [] + + for epoch in config["landsat"]["epochs"]: + # Search for a task for an existing epoch. + composite_row_ = tasks[ + (tasks["product_type"] == "landsat_composite") + & (tasks["epoch"].astype(str) == str(epoch)) + ] + if composite_row_.empty: + print(f"Skipping epoch {epoch}: no composite task found.") + continue + composite_row = composite_row_.iloc[0] + + count_row_ = tasks[ + (tasks["product_type"] == "landsat_valid_count") + & (tasks["epoch"].astype(str) == str(epoch)) + ] + if count_row_.empty: + print(f"Skipping epoch {epoch}: no count task found.") + continue + count_row = count_row_.iloc[0] + + composite_image, projection = validate_asset_grid( + str(composite_row["asset_id"]), + list(config["landsat"]["common_bands"]), + grid, + ) + count_image = ee.Image(str(count_row["asset_id"])) + count_bands = count_image.bandNames().getInfo() + + if not count_bands or count_bands[0] != "valid_observation_count": + raise ValueError(f"Invalid count bands for epoch {epoch}: {count_bands}") + + validate_asset_grid( + str(count_row["asset_id"]), + count_bands, + grid, + ) + statistics = count_statistics( + count_image, + grid, + int(composite_row["selected_scene_count"]), + ) + + if statistics["maximum_observation_count"] > statistics["selected_scene_count"]: + raise ValueError( + f"Count maximum exceeds selected scenes for epoch {epoch}." + ) + + sensors = composite_image.get("scene_counts_by_sensor").getInfo() or "{}" + composite_rows.append( + { + "epoch": int(epoch), + "asset_id": composite_row["asset_id"], + "count_asset_id": count_row["asset_id"], + "selected_scene_count": int(composite_row["selected_scene_count"]), + "selected_scene_ids_sha256": composite_row[ + "selected_scene_ids_sha256" + ], + "sensors": sensors, + "band_names": ",".join(config["landsat"]["common_bands"]), + "crs": projection["crs"], + "transform": json.dumps(projection["transform"]), + "width": int(grid["width"]), + "height": int(grid["height"]), + "status": "PASS", + } + ) + quality_rows.append({"epoch": int(epoch), **statistics}) + + terrain_rows = tasks[tasks["product_type"] == "terrain"] + if terrain_rows.empty: + raise RuntimeError("Terrain task missing from task manifest.") + terrain_row = terrain_rows.iloc[0] + + validate_asset_grid( + str(terrain_row["asset_id"]), + list(config["srtm"]["output_bands"]), + grid, + ) + + metadata_dir = metadata_directory(config, project_root) + composite_manifest = pd.DataFrame(composite_rows) + epoch_quality = pd.DataFrame(quality_rows) + composite_manifest.to_csv( + metadata_dir / config["metadata"]["landsat_composite_manifest"], + index=False, + ) + epoch_quality.to_csv( + metadata_dir / config["metadata"]["landsat_epoch_quality"], + index=False, + ) + + auxiliary_path = metadata_dir / config["metadata"]["auxiliary_source_manifest"] + auxiliary_manifest = pd.read_csv(auxiliary_path) + + osm_metadata_path = resolve_project_path(config["osm"]["source_metadata"], project_root) + + if config["osm"]["required_for_day3"] and not osm_metadata_path.is_file(): + raise FileNotFoundError( + "OSM metadata is missing. Run `make day3-osm` before finalization." + ) + + dependencies_path = metadata_dir / config["metadata"]["dependency_manifest"] + version_payload = { + "day3_version": int(config["version"]), + "grid_specification_sha256": sha256_file( + resolve_project_path(config["inputs"]["grid_specification"], project_root) + ), + "catalog_version_sha256": sha256_file( + resolve_project_path(config["inputs"]["catalog_version"], project_root) + ), + "compositing_protocol_sha256": sha256_file( + resolve_project_path(config["inputs"]["compositing_protocol"], project_root) + ), + "dependency_manifest_sha256": sha256_file(dependencies_path), + "task_manifest_sha256": sha256_file(task_path), + "landsat_composite_manifest_sha256": stable_object_hash(composite_rows), + "landsat_epoch_quality_sha256": stable_object_hash(quality_rows), + "ghsl_epoch_manifest_sha256": sha256_file( + metadata_dir / config["metadata"]["ghsl_epoch_manifest"] + ), + "grid_linkage_sha256": sha256_file( + metadata_dir / config["metadata"]["grid_linkage"] + ), + "osm_source_sha256": ( + load_json(osm_metadata_path)["source_sha256"] + if osm_metadata_path.is_file() + else None + ), + "earth_engine_assets": tasks["asset_id"].astype(str).tolist(), + } + version_payload["stable_signature"] = stable_object_hash(version_payload) + write_json( + metadata_dir / config["metadata"]["day3_version"], + version_payload, + ) + + report_dir = report_directory(config, project_root) + create_figures( + composite_manifest, + epoch_quality, + auxiliary_manifest, + report_dir, + config, + ) + write_report( + composite_manifest, + epoch_quality, + auxiliary_manifest, + report_dir / config["reports"]["day3_report"], + ) + + print(json.dumps(version_payload, indent=2)) + return version_payload + + +def parse_arguments() -> argparse.Namespace: + """Parse status and finalization command-line arguments.""" + parser = argparse.ArgumentParser( + description="Monitor and finalize Day 3 Earth Engine outputs." + ) + parser.add_argument("--config", required=True, type=Path) + parser.add_argument("--status-only", action="store_true") + return parser.parse_args() + + +def main() -> None: + """Run task monitoring or complete Day 3 finalization.""" + arguments = parse_arguments() + run_finalize(arguments.config.resolve(), arguments.status_only) + + +if __name__ == "__main__": + main() diff --git a/src/analysis/orchestration/preflight.py b/src/analysis/orchestration/preflight.py new file mode 100644 index 0000000..68c5816 --- /dev/null +++ b/src/analysis/orchestration/preflight.py @@ -0,0 +1,224 @@ +"""Validate frozen Day 1 and Day 2 dependencies before Day 3 submission.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import pandas as pd + +from .common import ( + find_project_root, + load_grid_specification, + load_json, + load_yaml, + metadata_directory, + resolve_project_path, + sha256_file, + stable_object_hash, +) + + +EXPECTED_EPOCHS = {1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025} + +# What we actually have due to the lack of early data for Yaounde +# EXPECTED_EPOCHS = {2005, 2010, 2015, 2020, 2025} + +def asset_id_column(manifest: pd.DataFrame) -> str: + """Return the supported column containing exact Earth Engine asset IDs.""" + for candidate in ("earth_engine_asset_id", "asset_id"): + if candidate in manifest.columns: + return candidate + + raise ValueError( + "The selected scene manifest needs an `earth_engine_asset_id` or " + "`asset_id` column." + ) + + +def compare_grid_reference( + grid: dict[str, Any], + reference: dict[str, Any], +) -> None: + """Compare grid-defining fields against the accepted Day 1 reference.""" + fields = ( + "crs", + "resolution_m", + "transform", + "width", + "height", + "extent", + ) + + for field in fields: + if grid.get(field) != reference.get(field): + raise ValueError( + f"Frozen grid mismatch for '{field}': " + f"{grid.get(field)!r} != {reference.get(field)!r}" + ) + + +def compare_catalog_reference( + catalog_version: dict[str, Any], + catalog_reference: dict[str, Any], +) -> None: + """Reject a Day 2 catalogue that differs from its committed reference.""" + if catalog_version != catalog_reference: + raise ValueError( + "catalog_version.json differs from landsat_catalog_v1.json. " + "Re-freeze Day 2 only after an intentional scientific change." + ) + + +def validate_manifest( + manifest: pd.DataFrame, + protocol: dict[str, Any], +) -> str: + """Validate epochs, exact asset IDs and protocol representation.""" + required = {"epoch", "sensor_key", "acquisition_date"} + missing = required.difference(manifest.columns) + + if missing: + raise ValueError( + f"Selected scene manifest is missing columns: {sorted(missing)}" + ) + + id_column = asset_id_column(manifest) + + if manifest[id_column].isna().any(): + raise ValueError("At least one selected scene has no exact asset ID.") + + observed_epochs = set(manifest["epoch"].astype(int)) + + # Accept a subset + if not observed_epochs.issubset(EXPECTED_EPOCHS): + invalid = observed_epochs - EXPECTED_EPOCHS + raise ValueError( + f"Found epochs that are not in the expected list {sorted(EXPECTED_EPOCHS)}: " + f"{sorted(invalid)}" + ) + + if "candidate_for_composite" in manifest.columns: + values = manifest["candidate_for_composite"].astype(str).str.lower() + invalid = ~values.isin({"true", "1"}) + + if invalid.any(): + raise ValueError( + "The selected scene manifest contains a scene not marked as " + "a composite candidate." + ) + + protocol_epochs = set(int(value) for value in protocol["epochs"]) + + # print("Protocol epochs: ", protocol_epochs) + # print("Expected epochs: ", EXPECTED_EPOCHS) + + if not protocol_epochs.issubset(EXPECTED_EPOCHS): + invalid = protocol_epochs - EXPECTED_EPOCHS + raise ValueError( + f"The compositing protocol contains invalid epochs: {sorted(invalid)}" + ) + + return id_column + +def dependency_row(name: str, path: Path, status: str = "PASS") -> dict[str, Any]: + """Create one traceable dependency-manifest record.""" + return { + "dependency_name": name, + "dependency_path": str(path), + "observed_sha256": sha256_file(path), + "status": status, + } + + +def run_preflight(config_path: Path) -> dict[str, Any]: + """Execute all dependency checks and write the dependency manifest.""" + project_root = find_project_root(config_path.parent) + config = load_yaml(config_path) + inputs = config["inputs"] + + paths = { + key: resolve_project_path(value, project_root) + for key, value in inputs.items() + } + + required_paths = ( + "grid_specification", + "grid_reference", + "selected_scene_manifest", + "compositing_protocol", + "catalog_version", + "catalog_reference", + "landsat_catalog_config", + ) + + for key in required_paths: + if not paths[key].is_file(): + raise FileNotFoundError(f"Missing Day 3 dependency: {paths[key]}") + + grid = load_grid_specification(paths["grid_specification"]) + grid_reference = load_json(paths["grid_reference"]) + compare_grid_reference(grid, grid_reference) + + if grid["crs"] != "EPSG:32632": + raise ValueError(f"Unexpected project CRS: {grid['crs']}") + + catalog_version = load_json(paths["catalog_version"]) + catalog_reference = load_json(paths["catalog_reference"]) + compare_catalog_reference(catalog_version, catalog_reference) + + manifest = pd.read_csv(paths["selected_scene_manifest"]) + protocol = load_yaml(paths["compositing_protocol"]) + id_column = validate_manifest(manifest, protocol) + + rows = [ + dependency_row("grid_specification", paths["grid_specification"]), + dependency_row("grid_reference", paths["grid_reference"]), + dependency_row("selected_scene_manifest", paths["selected_scene_manifest"]), + dependency_row("compositing_protocol", paths["compositing_protocol"]), + dependency_row("catalog_version", paths["catalog_version"]), + dependency_row("catalog_reference", paths["catalog_reference"]), + dependency_row("landsat_catalog_config", paths["landsat_catalog_config"]), + ] + + output_dir = metadata_directory(config, project_root) + output_path = output_dir / config["metadata"]["dependency_manifest"] + pd.DataFrame(rows).to_csv(output_path, index=False) + + result = { + "project_root": str(project_root), + "grid_sha256": stable_object_hash(grid), + "catalog_sha256": sha256_file(paths["catalog_version"]), + "protocol_sha256": sha256_file(paths["compositing_protocol"]), + "selected_scene_manifest_sha256": sha256_file( + paths["selected_scene_manifest"] + ), + "selected_scene_count": int(len(manifest)), + "asset_id_column": id_column, + "epochs": sorted(int(value) for value in manifest["epoch"].unique()), + "status": "PASS", + } + + print(json.dumps(result, indent=2)) + return result + + +def parse_arguments() -> argparse.Namespace: + """Parse the Day 3 preflight command-line arguments.""" + parser = argparse.ArgumentParser( + description="Validate frozen Day 1 and Day 2 dependencies." + ) + parser.add_argument("--config", required=True, type=Path) + return parser.parse_args() + + +def main() -> None: + """Run the preflight command.""" + arguments = parse_arguments() + run_preflight(arguments.config.resolve()) + + +if __name__ == "__main__": + main() diff --git a/tests/reference/orchestrate_sources_v1.json b/tests/reference/orchestrate_sources_v1.json new file mode 100644 index 0000000..3e75ce4 --- /dev/null +++ b/tests/reference/orchestrate_sources_v1.json @@ -0,0 +1,27 @@ +{ + "catalog_version_sha256": "9119010004d8374433ef5517a5c0e95b7037346526dd3859279bc6e3f673867d", + "compositing_protocol_sha256": "a31dee916b03f507d961be8586623ea01e7c8db8f9395a95638ee7eac973059c", + "day3_version": 1, + "dependency_manifest_sha256": "afd78910352a65f487f4b7be32c88d092e1aec168435f949e6d36ff879c761e2", + "earth_engine_assets": [ + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/terrain/elevation_slope" + ], + "ghsl_epoch_manifest_sha256": "a72c696665905abc86a9e927c2de8dfd4f7c7643a45a503a4e71f9cccd36add6", + "grid_linkage_sha256": "87d33f52f81d5bdcc222f8cda62b2b1b8311a299a2780d8035647182ea9c4d05", + "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", + "landsat_composite_manifest_sha256": "f57f2b6af79216d8f061447554cd6338685baaf4eb1de4d808ed850a8c516951", + "landsat_epoch_quality_sha256": "2376f9f8b44685a60ccee5c50b735425dac2a322dc59ab6cd9c20f90a3369d91", + "osm_source_sha256": "a0dfcf623b5d5565238dfc23590842a98edc9fba8301f4eeb9b31510278d8380", + "stable_signature": "1b250065acaeed32c93e0f76011b4c748a8908d99b935dd153061e986abd5b92", + "task_manifest_sha256": "95f9a5e20ec71535ad0c71552ef1b19e940e5856529964c9dc5dbc86153178c6" +} diff --git a/tests/test_orchestrate_sources.py b/tests/test_orchestrate_sources.py new file mode 100644 index 0000000..f7ab2f0 --- /dev/null +++ b/tests/test_orchestrate_sources.py @@ -0,0 +1,88 @@ +""" +Critical checks are performed: + 1. All submitted composite assets completed successfully. + 2. The outputs are correctly aligned to the project grid (EPSG:32632, 946x1278). + 3. Observation counts are physically plausible (bounded by selected scene count). +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import pandas as pd +import pytest +import yaml + + +PROJECT_ROOT = Path(__file__).resolve().parents[1] +CONFIG_PATH = PROJECT_ROOT / "configs/orchestrate_sources.yaml" + + +@pytest.fixture(scope="session") +def config() -> dict[str, Any]: + """Load the authoritative configuration.""" + with CONFIG_PATH.open("r", encoding="utf-8") as stream: + return yaml.safe_load(stream) + + +@pytest.fixture(scope="session") +def metadata_dir(config: dict[str, Any]) -> Path: + """Return the generated metadata directory.""" + path = Path(config["metadata"]["directory"]) + return path if path.is_absolute() else PROJECT_ROOT / path + + +@pytest.fixture(scope="session") +def composite_manifest(config: dict[str, Any], metadata_dir: Path) -> pd.DataFrame: + """Load the validated Landsat composite manifest.""" + return pd.read_csv(metadata_dir / config["metadata"]["landsat_composite_manifest"]) + + +@pytest.fixture(scope="session") +def epoch_quality(config: dict[str, Any], metadata_dir: Path) -> pd.DataFrame: + """Load validated observation-count statistics.""" + return pd.read_csv(metadata_dir / config["metadata"]["landsat_epoch_quality"]) + + +# -------- Test 1 : Integrity -------- +def test_all_composites_succeeded(composite_manifest: pd.DataFrame) -> None: + """Ensure every submitted composite asset completed successfully.""" + assert not composite_manifest.empty, "No composite assets were generated." + assert (composite_manifest["status"] == "PASS").all(), ( + "At least one composite asset has a non-PASS status." + ) + + +# -------- Test 2 : Grid alignment -------- +def test_grid_definition_is_exact(composite_manifest: pd.DataFrame) -> None: + """Ensure all composites use the exact frozen grid (CRS, width, height).""" + assert (composite_manifest["crs"] == "EPSG:32632").all(), ( + "All assets must use EPSG:32632." + ) + assert set(composite_manifest["width"].astype(int)) == {946}, ( + "Asset width must be 946 pixels." + ) + assert set(composite_manifest["height"].astype(int)) == {1278}, ( + "Asset height must be 1278 pixels." + ) + + +# -------- Test 3 : Physical plausibility -------- +def test_observation_counts_are_plausible(epoch_quality: pd.DataFrame) -> None: + """Ensure observation counts are non-negative and never exceed the number of selected scenes.""" + assert (epoch_quality["minimum_observation_count"] >= 0).all(), ( + "Minimum observation count must be >= 0." + ) + assert ( + epoch_quality["maximum_observation_count"] + <= epoch_quality["selected_scene_count"] + ).all(), ( + "Maximum observation count cannot exceed the number of selected scenes." + ) + + # S anity check: the core area should have at least some coverage + assert (epoch_quality["covered_grid_pct"] > 0.0).all(), ( + "Every epoch should cover at least some part of the grid." + ) \ No newline at end of file From 1589692efeb2d0a0acba14bbd057c591244b97b8 Mon Sep 17 00:00:00 2001 From: Omer-alt Date: Mon, 27 Jul 2026 09:34:06 +0100 Subject: [PATCH 4/5] Save initial built-up candidate results before 2025 recovery --- Makefile | 74 +- README.md | 6 + configs/built_up_candidates.yaml | 82 + .../built_up_candidates_version.json | 35 + .../candidate_area_summary.csv | 26 + .../dependency_manifest.csv | 7 + .../earth_engine_tasks.csv | 11 + .../built_up_candidates/histograms.json | 9156 +++++++++++++++++ .../built_up_candidates/output_manifest.csv | 11 + .../processing_recipe.json | 44 + .../built_up_candidates/threshold_table.csv | 26 + data/raw/osm/source_checksum.sha256 | 1 + data/raw/osm/source_metadata.json | 21 + docs/built_up_candidates.md | 73 + .../built_up_candidates/01_index_gallery.png | Bin 0 -> 1234470 bytes .../02_candidate_map_gallery.png | Bin 0 -> 1153915 bytes .../03_histogram_threshold_gallery.png | Bin 0 -> 535391 bytes .../built_up_candidates_report.md | 49 + src/analysis/built_up/__init__.py | 6 + src/analysis/built_up/build_candidates.py | 1100 ++ src/analysis/built_up/finalize.py | 636 ++ src/analysis/built_up/indices.py | 275 + src/analysis/built_up/otsu.py | 203 + tests/reference/built_up_candidates_v1.json | 35 + tests/test_built_up_candidates.py | 289 + 25 files changed, 12154 insertions(+), 12 deletions(-) create mode 100644 configs/built_up_candidates.yaml create mode 100644 data/metadata/built_up_candidates/built_up_candidates_version.json create mode 100644 data/metadata/built_up_candidates/candidate_area_summary.csv create mode 100644 data/metadata/built_up_candidates/dependency_manifest.csv create mode 100644 data/metadata/built_up_candidates/earth_engine_tasks.csv create mode 100644 data/metadata/built_up_candidates/histograms.json create mode 100644 data/metadata/built_up_candidates/output_manifest.csv create mode 100644 data/metadata/built_up_candidates/processing_recipe.json create mode 100644 data/metadata/built_up_candidates/threshold_table.csv create mode 100644 data/raw/osm/source_checksum.sha256 create mode 100644 data/raw/osm/source_metadata.json create mode 100644 docs/built_up_candidates.md create mode 100644 reports/built_up_candidates/01_index_gallery.png create mode 100644 reports/built_up_candidates/02_candidate_map_gallery.png create mode 100644 reports/built_up_candidates/03_histogram_threshold_gallery.png create mode 100644 reports/built_up_candidates/built_up_candidates_report.md create mode 100644 src/analysis/built_up/__init__.py create mode 100644 src/analysis/built_up/build_candidates.py create mode 100644 src/analysis/built_up/finalize.py create mode 100644 src/analysis/built_up/indices.py create mode 100644 src/analysis/built_up/otsu.py create mode 100644 tests/reference/built_up_candidates_v1.json create mode 100644 tests/test_built_up_candidates.py diff --git a/Makefile b/Makefile index 476caeb..75b3b5b 100644 --- a/Makefile +++ b/Makefile @@ -1,3 +1,7 @@ +# ============================================================================= +# Administrative boundaries and study grid +# ============================================================================= + .PHONY: help install inspect-adm2 inspect-adm3 inspect-boundaries build-grid test-grid day1 help: ## Display this help with a description of all available commands @@ -33,22 +37,28 @@ qa-day1: build-grid visualize-grid test-grid ## Run the complete QA pipeline (b day1: inspect-boundaries build-grid test-grid ## Run the complete Day 1 pipeline (inspection, grid build, tests) - +# ============================================================================= +# — Landsat catalog and compositing windows +# ============================================================================= .PHONY: landsat-catalog landsat-visuals test-landsat day2 -landsat-catalog: +landsat-catalog: ## Build the full Landsat scene manifest by querying Earth Engine, compute QA metrics, and select optimal compositing windows python -m src.analysis.landsat.build_catalog \ --config configs/landsat_catalog.yaml -landsat-visuals: +landsat-visuals: ## Generate diagnostic plots for the Landsat catalog availability and selection strategy (reports/day2/) python -m src.analysis.landsat.visualize_catalog \ --config configs/landsat_catalog.yaml -test-landsat: +test-landsat: ## Run the pytest suite for the Landsat catalog module with verbose output pytest tests/test_landsat_catalog.py -v -day2: landsat-catalog landsat-visuals test-landsat +day2: landsat-catalog landsat-visuals test-landsat ## Execute the complete Day 2 pipeline: build the Landsat catalog, generate visualizations, and run tests + +# ============================================================================= +# — Orchestration (Landsat composites, terrain, OSM) +# ============================================================================= .PHONY: \ orchestrate-preflight \ @@ -59,32 +69,72 @@ day2: landsat-catalog landsat-visuals test-landsat test-orchestrate \ freeze-orchestrate-v1 -orchestrate-preflight: +orchestrate-preflight: ## Check all frozen Day 1 and Day 2 dependencies (grid, catalog, manifests) before submitting Day 3 exports python -m src.analysis.orchestration.preflight \ --config configs/orchestrate_sources.yaml -orchestrate-submit: +orchestrate-submit: ## Submit all Earth Engine export tasks for Landsat composites, valid-count layers, and the SRTM terrain product python -m src.analysis.landsat.build_composites \ --config configs/orchestrate_sources.yaml \ --submit -orchestrate-status: +orchestrate-status: ## Check the current running/completed status of all submitted Earth Engine export tasks python -m src.analysis.orchestration.finalize \ --config configs/orchestrate_sources.yaml \ --status-only -orchestrate-osm: +orchestrate-osm: ## Download the latest Geofabrik OpenStreetMap extract, clip it to the study area, and save it as a GeoPackage python -m src.analysis.auxiliary.build_osm_extract \ --config configs/orchestrate_sources.yaml -orchestrate-finalize: +orchestrate-finalize: ## Validate the completed Earth Engine assets, generate QA manifests, summary statistics, and finalize Day 3 metadata python -m src.analysis.orchestration.finalize \ --config configs/orchestrate_sources.yaml -test-orchestrate: +test-orchestrate: ## Run the pytest suite for the orchestration (Day 3) outputs and dependencies pytest tests/test_orchestrate_sources.py -v -freeze-orchestrate-v1: +freeze-orchestrate-v1: ## Copy the validated Day 3 version manifest to tests/reference as the frozen baseline for regression tests mkdir -p tests/reference cp data/metadata/orchestration/orchestrate_version.json \ tests/reference/orchestrate_sources_v1.json + +# ============================================================================= +# Built-up candidates (spectral indices + Otsu thresholds) +# ============================================================================= + +.PHONY: \ + built-up-preflight \ + built-up-submit \ + built-up-status \ + built-up-finalize \ + test-built-up \ + freeze-built-up-v1 + +built-up-preflight: ## Run preflight checks for Day 4: validate inputs, list completed epochs, and confirm expected index/candidate layers without submitting tasks + python -m src.analysis.built_up.build_candidates \ + --config configs/built_up_candidates.yaml \ + --preflight-only + +built-up-submit: ## Compute spectral indices, calculate Otsu thresholds, and submit all Day 4 Earth Engine exports (indices and candidate masks) + python -m src.analysis.built_up.build_candidates \ + --config configs/built_up_candidates.yaml \ + --submit + +built-up-status: ## Check the current running/completed status of all submitted Day 4 Earth Engine export tasks + python -m src.analysis.built_up.finalize \ + --config configs/built_up_candidates.yaml \ + --status-only + +built-up-finalize: ## Validate the exported Day 4 assets, generate histograms, threshold tables, area summaries, and the final report + python -m src.analysis.built_up.finalize \ + --config configs/built_up_candidates.yaml + +test-built-up: ## Run the pytest suite for the built-up candidates (Day 4) module + python -m pytest tests/test_built_up_candidates.py -v + +freeze-built-up-v1: ## Copy the validated Day 4 version manifest to tests/reference as the frozen baseline for regression tests + mkdir -p tests/reference + cp \ + data/metadata/built_up_candidates/built_up_candidates_version.json \ + tests/reference/built_up_candidates_v1.json diff --git a/README.md b/README.md index 9829ceb..bf84ed5 100644 --- a/README.md +++ b/README.md @@ -191,6 +191,12 @@ GHSL will remain an auxiliary comparison and validation source, while current OpenStreetMap data will be documented carefully because its historical completeness varies. +### Spectral Built-up Candidates + +Completed Landsat composites are converted into SAVI, MNDWI, NDBI, IBI, IBUI, VbSWIR1-BI and NDBSUI layers. Epoch-specific Otsu thresholds are estimated inside the Yaoundé administrative core and applied to the context grid. + +The resulting binary maps are unvalidated candidate pseudo-labels. Missing or numerically undefined pixels remain masked and are not treated as non-built-up. +Final index selection, comparative validation and temporal correction are performed in later stages. #### Spatial Predictors diff --git a/configs/built_up_candidates.yaml b/configs/built_up_candidates.yaml new file mode 100644 index 0000000..189e798 --- /dev/null +++ b/configs/built_up_candidates.yaml @@ -0,0 +1,82 @@ +version: 1 + +project: + earth_engine_project: urban-sprawl-ssa + +inputs: + grid_specification: data/metadata/grid_specification.json + grid_reference: tests/reference/grid_v1.json + orchestration_version: + data/metadata/orchestration/orchestrate_version.json + composite_manifest: + data/metadata/orchestration/landsat_composite_manifest.csv + core_boundary: + data/processed/boundaries/yaounde_core.gpkg + context_boundary: + data/processed/boundaries/yaounde_context_buffer.gpkg + +epochs: + mode: completed_from_manifest + include: [2005, 2010, 2015, 2020, 2025] + +indices: + formula_version: 1 + denominator_epsilon: 0.000001 + savi_l: 0.5 + intermediate: [savi, mndwi] + selected: [ndbi, ibi, ibui, vbswir1_bi, ndbsui] + +classification: + primary_strategy: epoch_specific_otsu + compute_pooled_common_thresholds: false + fail_all_exports_on_threshold_error: true + threshold_region: core + application_region: context + minimum_valid_observations_for_threshold: 1 + minimum_valid_observations_for_candidate: 1 + built_up_direction: + ndbi: greater_than + ibi: greater_than + ibui: greater_than + vbswir1_bi: greater_than + ndbsui: greater_than + +histogram: + max_buckets: 256 + min_bucket_width: null + max_raw: 100000 + max_pixels: 10000000 + tile_scale: 4 + +exports: + asset_root: projects/urban-sprawl-ssa/assets/sprawlgp/v1 + index_folder: indices + candidate_folder: built_up_candidates + overwrite_existing_assets: false # true + max_pixels: 10000000 + shard_size: 256 + priority: 100 + task_manifest: + data/metadata/built_up_candidates/earth_engine_tasks.csv + +metadata: + directory: data/metadata/built_up_candidates + dependency_manifest: dependency_manifest.csv + histograms: histograms.json + threshold_table: threshold_table.csv + candidate_area_summary: candidate_area_summary.csv + output_manifest: output_manifest.csv + processing_recipe: processing_recipe.json + version: built_up_candidates_version.json + +reports: + directory: reports/built_up_candidates + index_gallery: 01_index_gallery.png + candidate_gallery: 02_candidate_map_gallery.png + histogram_gallery: 03_histogram_threshold_gallery.png + report: built_up_candidates_report.md + thumbnail_dimensions: 256 + generate_map_galleries: true + +reference: + version: tests/reference/built_up_candidates_v1.json diff --git a/data/metadata/built_up_candidates/built_up_candidates_version.json b/data/metadata/built_up_candidates/built_up_candidates_version.json new file mode 100644 index 0000000..eac5fe9 --- /dev/null +++ b/data/metadata/built_up_candidates/built_up_candidates_version.json @@ -0,0 +1,35 @@ +{ + "binary_candidate_layer_count": 25, + "candidate_area_summary_sha256": "0d9a0b41fe08b9383a433d477b873d117f414d134d877145c2cc6d3bad2655b9", + "continuous_index_layer_count": 35, + "dependency_manifest_sha256": "51273195d56387ebeb655f844f66bbb239308ba6332ff6d01a079d65af97923b", + "earth_engine_assets": [ + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2025" + ], + "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", + "histograms_sha256": "a826db830346f1d7cc93f27a756c2a2a356a5abde85cd3d68f12340cf41c5fde", + "orchestration_version_sha256": "2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7", + "output_manifest_sha256": "6be6c2cd314e377a5f73b82377eede085b57ac9b287b5958300d8edc5a79054f", + "pipeline_stage": "built_up_candidates", + "pipeline_version": 1, + "processed_epochs": [ + 2005, + 2010, + 2015, + 2020, + 2025 + ], + "processing_recipe_sha256": "186ae55cfc67cefc6a7109e676bb4be5f59dce00eefe1bdef1b6c3f7ad7e017c", + "stable_signature": "d0d437043b4c2113c7769c0bae912a754cd7a3383cac15715429962b54847650", + "task_manifest_sha256": "4111859d2af577a69e9718d6ef5b22db3dfa81b39863bdcc218b05b4ec0862c4", + "threshold_table_sha256": "cfded18084e387b8f227d3a42edc8868f5090e081fc4980ca4ae241714bc3308" +} diff --git a/data/metadata/built_up_candidates/candidate_area_summary.csv b/data/metadata/built_up_candidates/candidate_area_summary.csv new file mode 100644 index 0000000..a009146 --- /dev/null +++ b/data/metadata/built_up_candidates/candidate_area_summary.csv @@ -0,0 +1,26 @@ +epoch,index_name,threshold,valid_area_ha,candidate_built_area_ha,candidate_nonbuilt_area_ha,candidate_built_fraction,change_from_previous_epoch_ha,quality_flag +2005,ibi,-10299.64614868164,1759.5323407033402,1759.4424419240434,0.089898779296875,0.9999489075720763,,PASS +2005,ibui,-0.17199289871104967,1759.5323407033402,395.5364693527641,1363.9958713505764,0.22479636219397692,,PASS +2005,ndbi,-0.22670226164325458,1759.5323407033402,464.72139249444695,1294.8109482088935,0.2641164255660588,,PASS +2005,ndbsui,-0.11922073596359206,1759.5323407033402,619.3211614388739,1140.2111792644664,0.35198055023604274,,PASS +2005,vbswir1_bi,0.43758810491772593,1759.5323407033402,1252.1421899375766,507.3901507657638,0.7116335181637277,,PASS +2010,ibi,-2175.7214965820312,7832.69157057634,7832.242089613205,0.4494809631347656,0.999942614750104,6072.799647689162,PASS +2010,ibui,-0.046867263681580576,7832.69157057634,3259.9702543774893,4572.72131619885,0.4162005135787079,2864.4337850247252,PASS +2010,ndbi,-0.14843226221576813,7832.69157057634,3469.585859188568,4363.105711387772,0.44296214499523195,3004.864466694121,PASS +2010,ndbsui,-0.0644524275233183,7832.69157057634,3792.080058765055,4040.611511811285,0.48413499045590896,3172.7588973261813,PASS +2010,vbswir1_bi,0.5897822438894713,7832.69157057634,4071.674364017932,3761.0172065584065,0.5198308049449127,2819.5321740803556,PASS +2015,ibi,-158813.41015625,17245.411515109507,17245.32161236903,0.08990274047851562,0.9999947868602382,9413.079522755823,PASS 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b/data/metadata/built_up_candidates/processing_recipe.json new file mode 100644 index 0000000..8ced8d8 --- /dev/null +++ b/data/metadata/built_up_candidates/processing_recipe.json @@ -0,0 +1,44 @@ +{ + "application_region": "context", + "candidate_band_schema": [ + "built_ndbi", + "built_ibi", + "built_ibui", + "built_vbswir1_bi", + "built_ndbsui", + "valid_ndbi", + "valid_ibi", + "valid_ibui", + "valid_vbswir1_bi", + "valid_ndbsui" + ], + "classification_directions": { + "ibi": "greater_than", + "ibui": "greater_than", + "ndbi": "greater_than", + "ndbsui": "greater_than", + "vbswir1_bi": "greater_than" + }, + "denominator_epsilon": 1e-06, + "formula_version": 1, + "grid_sha256": "70534f7aa78e3ac0bd0acdb7f87c8d22a025612b568d908d1e3b761d28ffa43b", + "index_band_schema": [ + "savi", + "mndwi", + "ndbi", + "ibi", + "ibui", + "vbswir1_bi", + "ndbsui", + "valid_composite" + ], + "minimum_valid_observations_for_candidate": 1, + "minimum_valid_observations_for_threshold": 1, + "pipeline_stage": "built_up_candidates", + "pipeline_version": 1, + "pooled_thresholds_computed": false, + "savi_l": 0.5, + "source_orchestration_sha256": "2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7", + "threshold_region": "core", + "threshold_strategy": "epoch_specific_otsu" +} diff --git a/data/metadata/built_up_candidates/threshold_table.csv b/data/metadata/built_up_candidates/threshold_table.csv new file mode 100644 index 0000000..64e3dc7 --- /dev/null +++ b/data/metadata/built_up_candidates/threshold_table.csv @@ -0,0 +1,26 @@ 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b/data/raw/osm/source_checksum.sha256 @@ -0,0 +1 @@ +a0dfcf623b5d5565238dfc23590842a98edc9fba8301f4eeb9b31510278d8380 cameroon_20260725_free.gpkg.zip diff --git a/data/raw/osm/source_metadata.json b/data/raw/osm/source_metadata.json new file mode 100644 index 0000000..adbaa2f --- /dev/null +++ b/data/raw/osm/source_metadata.json @@ -0,0 +1,21 @@ +{ + "download_date": "2026-07-25", + "download_skipped_existing_file": true, + "feature_counts": { + "buildings_reference": 401097, + "railways": 68, + "roads_major": 1718 + }, + "licence": "Open Database Licence (ODbL)", + "processed_crs": "EPSG:32632", + "processed_output": "/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/processed/osm/yaounde_current_osm.gpkg", + "provider": "Geofabrik", + "snapshot_date": "2026-07-25", + "source_dataset": "OpenStreetMap Cameroon extract", + "source_filename": "cameroon_20260725_free.gpkg.zip", + "source_format": "Geofabrik free GeoPackage archive", + "source_last_modified": "Sat, 25 Jul 2026 02:50:02 GMT", + "source_sha256": "a0dfcf623b5d5565238dfc23590842a98edc9fba8301f4eeb9b31510278d8380", + "source_url": "https://download.geofabrik.de/africa/cameroon-latest-free.gpkg.zip", + "temporal_interpretation": "current snapshot only" +} diff --git a/docs/built_up_candidates.md b/docs/built_up_candidates.md new file mode 100644 index 0000000..dd41286 --- /dev/null +++ b/docs/built_up_candidates.md @@ -0,0 +1,73 @@ +# Spectral indices and Otsu built-up candidates + +This stage consumes only Landsat composites that passed orchestration +finalization. It calculates seven continuous spectral indices and five +epoch-specific Otsu candidate maps per completed epoch. + +The binary maps are **candidate pseudo-labels**, not validated built-up maps. + +## Outputs per epoch + +Index asset: + +```text +savi +mndwi +ndbi +ibi +ibui +vbswir1_bi +ndbsui +valid_composite +``` + +Candidate asset: + +```text +built_ndbi +built_ibi +built_ibui +built_vbswir1_bi +built_ndbsui +valid_ndbi +valid_ibi +valid_ibui +valid_vbswir1_bi +valid_ndbsui +``` + +Undefined index values remain masked. A candidate value of zero means valid +candidate non-built-up, while a masked value means that classification was not +possible. + +## Execution + +```bash +make built-up-preflight +make built-up-submit +``` + +All histograms and thresholds are validated before the first export task is +submitted. If any threshold fails, no export is started. + +After the tasks finish: + +```bash +make built-up-status +make built-up-finalize +make test-built-up +``` + +After numerical and visual review: + +```bash +make freeze-built-up-v1 +``` + +## Scientific limits + +- Otsu thresholds are epoch-specific. +- No index is selected as the final historical mapping method. +- Candidate-area changes are diagnostic only. +- No temporal persistence rule or transition map is applied. +- Validation and method selection belong to the following stage. diff --git a/reports/built_up_candidates/01_index_gallery.png b/reports/built_up_candidates/01_index_gallery.png new file mode 100644 index 0000000000000000000000000000000000000000..10ae0fffe7904ddab5f7ad43af3aa0cf9c57699d GIT binary patch literal 1234470 zcmeFZWmMH`_cp2<#X{MlAdO0ilyrkdBPboxC9yy{7pPcBcS%S}_o6|hTj{kZ1(62n zW}W$CKhOU>=hGSI!x`h8_kA2X*ozG-?t9*IUh}%HdHX(Al)iYL^8AStCoam$JXAh$ z;;iY36X%|sC4}$%>g=3^FT#$JnvN>AW{xg~_NFHu89F|ps85_caYFXt 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b/reports/built_up_candidates/built_up_candidates_report.md @@ -0,0 +1,49 @@ +# Spectral indices and Otsu built-up candidates + +## Output summary + +- Completed epochs: 2005, 2010, 2015, 2020, 2025 +- Epoch count: 5 +- Continuous index layers: 35 +- Binary candidate maps: 25 +- Epoch-specific Otsu thresholds: 25 + +The binary maps are unvalidated candidate pseudo-labels. No index has been selected as the final historical built-up mapping method. + +## Thresholds + +| Epoch | Index | Threshold | Valid pixels | Built fraction | Status | +|---:|---|---:|---:|---:|:---:| +| 2005 | ibi | -10299.646149 | 19571 | 0.9999 | PASS | +| 2005 | ibui | -0.171993 | 19571 | 0.2248 | PASS | +| 2005 | ndbi | -0.226702 | 19571 | 0.2641 | PASS | +| 2005 | ndbsui | -0.119221 | 19571 | 0.3520 | PASS | +| 2005 | vbswir1_bi | 0.437588 | 19571 | 0.7116 | PASS | +| 2010 | ibi | -2175.721497 | 87129 | 0.9999 | PASS | +| 2010 | ibui | -0.046867 | 87129 | 0.4162 | PASS | +| 2010 | ndbi | -0.148432 | 87129 | 0.4430 | PASS | +| 2010 | ndbsui | -0.064452 | 87129 | 0.4841 | PASS | +| 2010 | vbswir1_bi | 0.589782 | 87129 | 0.5198 | PASS | +| 2015 | ibi | -158813.410156 | 191830 | 1.0000 | PASS | +| 2015 | ibui | -0.054694 | 191831 | 0.4849 | PASS | +| 2015 | ndbi | -0.140577 | 191831 | 0.5058 | PASS | +| 2015 | ndbsui | -0.066386 | 191794 | 0.5882 | PASS | +| 2015 | vbswir1_bi | 0.991259 | 191831 | 0.0093 | PASS | +| 2020 | ibi | -62098.378906 | 100467 | 1.0000 | PASS | +| 2020 | ibui | -0.000003 | 100468 | 0.4665 | PASS | +| 2020 | ndbi | -0.113236 | 100468 | 0.5041 | PASS | +| 2020 | ndbsui | -0.054702 | 100451 | 0.5619 | PASS | +| 2020 | vbswir1_bi | -46.593643 | 100468 | 1.0000 | PASS | +| 2025 | ibi | 4715.648560 | 3356 | 0.0006 | PASS | +| 2025 | ibui | 0.202918 | 3356 | 0.6518 | PASS | +| 2025 | ndbi | 0.000176 | 3356 | 0.7176 | PASS | +| 2025 | ndbsui | -0.016548 | 3356 | 0.7590 | PASS | +| 2025 | vbswir1_bi | 0.726627 | 3356 | 0.3855 | PASS | + +## Interpretation limits + +- Thresholds are estimated independently for each epoch. +- Candidate-area changes are quality diagnostics, not validated growth. +- Invalid index pixels remain masked and are never recoded as non-built. +- No temporal persistence correction or transition map is applied. +- Comparative validation and final method selection belong to the next stage. diff --git a/src/analysis/built_up/__init__.py b/src/analysis/built_up/__init__.py new file mode 100644 index 0000000..ee7993d --- /dev/null +++ b/src/analysis/built_up/__init__.py @@ -0,0 +1,6 @@ +"""Spectral built-up indices and reproducible Otsu candidate generation. + +The package consumes only completed Landsat composites recorded by the +orchestration stage. Its binary outputs are unvalidated candidate pseudo-labels, +not final historical built-up maps. +""" diff --git a/src/analysis/built_up/build_candidates.py b/src/analysis/built_up/build_candidates.py new file mode 100644 index 0000000..e2b5235 --- /dev/null +++ b/src/analysis/built_up/build_candidates.py @@ -0,0 +1,1100 @@ +"""Validate orchestration outputs, calculate indices and submit candidate assets. + +All histograms and Otsu thresholds are calculated and validated before any +export task is started. This prevents a failed epoch-index pair from leaving a +partially submitted candidate dataset. +""" + +from __future__ import annotations + +import argparse +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import ee +import geopandas as gpd +import numpy as np +import pandas as pd +from shapely.geometry import mapping + +from src.analysis.orchestration.common import ( + asset_exists, + ensure_asset_folder, + find_project_root, + initialize_earth_engine, + load_grid_specification, + load_json, + load_yaml, + metadata_directory, + resolve_project_path, + sha256_file, + stable_object_hash, + write_json, +) +from src.analysis.built_up.indices import ( + CANDIDATE_BANDS, + INDEX_BANDS, + SELECTED_INDICES, + build_candidate_stack, + build_index_stack, +) +from src.analysis.built_up.otsu import PASS, OtsuResult, otsu_from_histogram + + +SOURCE_COMPOSITE_BANDS = ["blue", "green", "red", "nir", "swir1", "swir2"] + + +@dataclass +class EpochBundle: + """Hold lazy Earth Engine images and local metadata for one epoch.""" + + epoch: int + source_composite_asset: str + source_count_asset: str + index_image: ee.Image + candidate_image: ee.Image + thresholds: dict[str, float] + threshold_rows: list[dict[str, Any]] + area_rows: list[dict[str, Any]] + histograms: dict[str, Any] + + +def load_ee_geometry(path: Path) -> ee.Geometry: + """Load one dissolved vector boundary as a WGS84 Earth Engine geometry.""" + frame = gpd.read_file(path) + + if frame.crs is None or len(frame) != 1: + raise ValueError( + f"Expected one georeferenced dissolved boundary in {path}." + ) + + geometry = frame.to_crs("EPSG:4326").geometry.iloc[0] + + if geometry is None or geometry.is_empty: + raise ValueError(f"Boundary geometry is empty: {path}") + + return ee.Geometry(mapping(geometry)) + + +def compare_grid_reference( + grid: dict[str, Any], + reference: dict[str, Any], +) -> None: + """Compare only the fields that define the accepted raster grid.""" + for field in ( + "crs", + "resolution_m", + "transform", + "width", + "height", + "extent", + ): + if grid.get(field) != reference.get(field): + raise ValueError( + f"Frozen grid mismatch for '{field}': " + f"{grid.get(field)!r} != {reference.get(field)!r}" + ) + + +def image_metadata(asset_id: str) -> dict[str, Any]: + """Return band names, types, dimensions, CRS and transform for an asset.""" + information = ee.Image(asset_id).getInfo() + bands = information.get("bands", []) + + if not bands: + raise ValueError(f"Earth Engine asset has no bands: {asset_id}") + + first = bands[0] + return { + "band_names": [str(band["id"]) for band in bands], + "band_types": { + str(band["id"]): band.get("data_type", {}) + for band in bands + }, + "width": int(first["dimensions"][0]), + "height": int(first["dimensions"][1]), + "crs": first.get("crs"), + "transform": [ + float(value) + for value in first.get("crs_transform", []) + ], + } + + +def validate_source_asset( + asset_id: str, + expected_bands: list[str], + grid: dict[str, Any], +) -> None: + """Validate a source asset's schema and exact Day 1 grid.""" + if not asset_exists(asset_id): + raise FileNotFoundError(f"Earth Engine asset not found: {asset_id}") + + metadata = image_metadata(asset_id) + + if metadata["band_names"] != expected_bands: + raise ValueError( + f"Unexpected source bands in {asset_id}: " + f"{metadata['band_names']}; expected {expected_bands}." + ) + + if metadata["crs"] != grid["crs"]: + raise ValueError( + f"Unexpected CRS in {asset_id}: {metadata['crs']}" + ) + + observed_transform = metadata["transform"] + expected_transform = [float(value) for value in grid["transform"]] + + if not np.allclose( + observed_transform, + expected_transform, + atol=1e-9, + ): + raise ValueError( + f"Unexpected transform in {asset_id}: {observed_transform}" + ) + + if ( + metadata["width"] != int(grid["width"]) + or metadata["height"] != int(grid["height"]) + ): + raise ValueError( + f"Unexpected dimensions in {asset_id}: " + f"{metadata['width']} x {metadata['height']}" + ) + + +def select_completed_composites( + manifest: pd.DataFrame, + config: dict[str, Any], +) -> pd.DataFrame: + """Select passed orchestration epochs and apply the optional include list.""" + required = { + "epoch", + "asset_id", + "count_asset_id", + "status", + } + missing = required.difference(manifest.columns) + + if missing: + raise ValueError( + f"Composite manifest is missing columns: {sorted(missing)}" + ) + + completed = manifest[ + manifest["status"].astype(str).str.upper() == "PASS" + ].copy() + + include = config.get("epochs", {}).get("include") + + if include: + allowed = {int(value) for value in include} + completed = completed[ + completed["epoch"].astype(int).isin(allowed) + ] + + completed["epoch"] = completed["epoch"].astype(int) + completed = completed.sort_values("epoch").reset_index(drop=True) + + if completed.empty: + raise ValueError( + "No completed orchestration composite remains after filtering." + ) + + if not completed["epoch"].is_unique: + raise ValueError("Composite manifest contains duplicate epochs.") + + return completed + + +def dependency_row(name: str, path: Path) -> dict[str, Any]: + """Create one local-file dependency record with a true SHA-256 checksum.""" + return { + "dependency_name": name, + "dependency_path": str(path), + "observed_sha256": sha256_file(path), + "status": "PASS", + } + + +def run_preflight(config_path: Path) -> dict[str, Any]: + """Validate local dependencies and completed source assets.""" + project_root = find_project_root(config_path.parent) + config = load_yaml(config_path) + inputs = config["inputs"] + paths = { + name: resolve_project_path(value, project_root) + for name, value in inputs.items() + } + + for name, path in paths.items(): + if not path.is_file(): + raise FileNotFoundError( + f"Missing built-up candidate dependency '{name}': {path}" + ) + + grid = load_grid_specification(paths["grid_specification"]) + reference = load_json(paths["grid_reference"]) + compare_grid_reference(grid, reference) + + if grid["crs"] != "EPSG:32632": + raise ValueError(f"Unexpected project CRS: {grid['crs']}") + + orchestration_version = load_json(paths["orchestration_version"]) + composite_manifest = pd.read_csv(paths["composite_manifest"]) + completed = select_completed_composites(composite_manifest, config) + + initialize_earth_engine(config["project"]["earth_engine_project"]) + + for row in completed.itertuples(index=False): + validate_source_asset( + str(row.asset_id), + SOURCE_COMPOSITE_BANDS, + grid, + ) + count_metadata = image_metadata(str(row.count_asset_id)) + + if ( + not count_metadata["band_names"] + or count_metadata["band_names"][0] + != "valid_observation_count" + ): + raise ValueError( + f"Invalid count asset bands for epoch {row.epoch}: " + f"{count_metadata['band_names']}" + ) + + validate_source_asset( + str(row.count_asset_id), + count_metadata["band_names"], + grid, + ) + + metadata_dir = metadata_directory(config, project_root) + dependency_path = ( + metadata_dir / config["metadata"]["dependency_manifest"] + ) + dependency_rows = [ + dependency_row("grid_specification", paths["grid_specification"]), + dependency_row("grid_reference", paths["grid_reference"]), + dependency_row( + "orchestration_version", + paths["orchestration_version"], + ), + dependency_row("composite_manifest", paths["composite_manifest"]), + dependency_row("core_boundary", paths["core_boundary"]), + dependency_row("context_boundary", paths["context_boundary"]), + ] + pd.DataFrame(dependency_rows).to_csv(dependency_path, index=False) + + epoch_count = int(len(completed)) + result = { + "status": "PASS", + "completed_epochs": completed["epoch"].tolist(), + "epoch_count": epoch_count, + "continuous_index_layers": epoch_count * 7, + "binary_candidate_maps": epoch_count * 5, + "expected_export_tasks": epoch_count * 2, + "grid_sha256": stable_object_hash(grid), + "orchestration_stable_signature": orchestration_version.get( + "stable_signature" + ), + "dependency_manifest": str(dependency_path), + } + + print(json.dumps(result, indent=2)) + return result + + +def histogram_reducer(config: dict[str, Any]): + """Construct the configured Earth Engine histogram reducer.""" + histogram = config["histogram"] + arguments: dict[str, Any] = { + "maxBuckets": int(histogram["max_buckets"]), + "maxRaw": int(histogram["max_raw"]), + } + + if histogram.get("min_bucket_width") is not None: + arguments["minBucketWidth"] = float( + histogram["min_bucket_width"] + ) + + return ee.Reducer.histogram(**arguments) + + +def calculate_histograms( + index_images: dict[str, ee.Image], + count_image: ee.Image, + core_geometry: ee.Geometry, + grid: dict[str, Any], + config: dict[str, Any], +) -> dict[str, Any]: + """Calculate all five selected histograms with one reduction per epoch.""" + minimum = int( + config["classification"][ + "minimum_valid_observations_for_threshold" + ] + ) + threshold_mask = count_image.select( + "valid_observation_count" + ).gte(minimum) + selected_stack = ee.Image.cat( + [ + index_images[name].updateMask(threshold_mask) + for name in SELECTED_INDICES + ] + ).select(SELECTED_INDICES) + + return selected_stack.reduceRegion( + reducer=histogram_reducer(config), + geometry=core_geometry, + crs=grid["crs"], + crsTransform=grid["transform"], + maxPixels=int(config["histogram"]["max_pixels"]), + tileScale=int(config["histogram"]["tile_scale"]), + ).getInfo() + + +def candidate_area_statistics( + candidate_image: ee.Image, + core_geometry: ee.Geometry, + grid: dict[str, Any], + config: dict[str, Any], +) -> dict[str, Any]: + """Calculate all candidate pixel counts and areas in one reduction.""" + pixel_area = ee.Image.pixelArea() + one = ee.Image.constant(1) + bands: list[ee.Image] = [] + + for name in SELECTED_INDICES: + built = candidate_image.select(f"built_{name}") + valid = candidate_image.select(f"valid_{name}").eq(1) + + bands.extend( + [ + one.updateMask(valid).rename(f"{name}_valid_pixels"), + one.updateMask(built.eq(1)).rename( + f"{name}_built_pixels" + ), + one.updateMask(built.eq(0)).rename( + f"{name}_nonbuilt_pixels" + ), + pixel_area.updateMask(valid).rename( + f"{name}_valid_area_m2" + ), + pixel_area.updateMask(built.eq(1)).rename( + f"{name}_built_area_m2" + ), + pixel_area.updateMask(built.eq(0)).rename( + f"{name}_nonbuilt_area_m2" + ), + ] + ) + + return ee.Image.cat(bands).reduceRegion( + reducer=ee.Reducer.sum(), + geometry=core_geometry, + crs=grid["crs"], + crsTransform=grid["transform"], + maxPixels=int(config["histogram"]["max_pixels"]), + tileScale=int(config["histogram"]["tile_scale"]), + ).getInfo() + + +def threshold_row( + *, + epoch: int, + index_name: str, + result: OtsuResult, + source_composite_asset: str, + source_count_asset: str, + config: dict[str, Any], +) -> dict[str, Any]: + """Build one auditable threshold-table record.""" + row = { + "epoch": int(epoch), + "index_name": index_name, + "threshold_strategy": config["classification"][ + "primary_strategy" + ], + "threshold_value": result.threshold, + "classification_direction": config["classification"][ + "built_up_direction" + ][index_name], + "histogram_bucket_count": result.histogram_bucket_count, + "histogram_valid_pixel_count": ( + result.histogram_valid_pixel_count + ), + "histogram_min": result.histogram_min, + "histogram_max": result.histogram_max, + "histogram_mean": result.histogram_mean, + "threshold_region": config["classification"][ + "threshold_region" + ], + "minimum_observation_count": config["classification"][ + "minimum_valid_observations_for_threshold" + ], + "status": result.status, + "failure_reason": result.failure_reason, + "source_composite_asset": source_composite_asset, + "source_count_asset": source_count_asset, + "formula_version": int(config["indices"]["formula_version"]), + "grid_version": 1, + } + row["threshold_record_sha256"] = stable_object_hash(row) + return row + + +def enrich_threshold_and_area_rows( + threshold_rows: list[dict[str, Any]], + area_values: dict[str, Any], +) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + """Attach candidate class counts and build the compact area-summary rows.""" + area_rows: list[dict[str, Any]] = [] + + for row in threshold_rows: + name = row["index_name"] + valid_pixels = int(round(float( + area_values.get(f"{name}_valid_pixels", 0) or 0 + ))) + built_pixels = int(round(float( + area_values.get(f"{name}_built_pixels", 0) or 0 + ))) + nonbuilt_pixels = int(round(float( + area_values.get(f"{name}_nonbuilt_pixels", 0) or 0 + ))) + valid_area_ha = float( + area_values.get(f"{name}_valid_area_m2", 0) or 0 + ) / 10_000 + built_area_ha = float( + area_values.get(f"{name}_built_area_m2", 0) or 0 + ) / 10_000 + nonbuilt_area_ha = float( + area_values.get(f"{name}_nonbuilt_area_m2", 0) or 0 + ) / 10_000 + built_fraction = ( + built_area_ha / valid_area_ha + if valid_area_ha > 0 + else None + ) + + row.update( + { + "candidate_valid_pixel_count_core": valid_pixels, + "candidate_built_pixel_count_core": built_pixels, + "candidate_nonbuilt_pixel_count_core": nonbuilt_pixels, + "candidate_built_fraction_core": built_fraction, + "candidate_built_area_ha_core": built_area_ha, + } + ) + row["threshold_record_sha256"] = stable_object_hash( + { + key: value + for key, value in row.items() + if key != "threshold_record_sha256" + } + ) + + area_rows.append( + { + "epoch": int(row["epoch"]), + "index_name": name, + "threshold": row["threshold_value"], + "valid_area_ha": valid_area_ha, + "candidate_built_area_ha": built_area_ha, + "candidate_nonbuilt_area_ha": nonbuilt_area_ha, + "candidate_built_fraction": built_fraction, + "change_from_previous_epoch_ha": None, + "quality_flag": "PASS", + } + ) + + return threshold_rows, area_rows + + +def add_area_changes(area_frame: pd.DataFrame) -> pd.DataFrame: + """Calculate diagnostic change from the previous available epoch.""" + result = area_frame.sort_values( + ["index_name", "epoch"] + ).copy() + result["change_from_previous_epoch_ha"] = result.groupby( + "index_name" + )["candidate_built_area_ha"].diff() + return result.sort_values( + ["epoch", "index_name"] + ).reset_index(drop=True) + + + +def export_grid_region(grid: dict[str, Any]) -> ee.Geometry: + """Return an inset export rectangle that selects exactly the frozen cells. + + The configured extent describes the outer raster edges. A small interior + inset prevents Earth Engine from including a neighbouring pixel whose edge + merely touches that rectangle, while every intended edge pixel remains + intersected. The affine transform remains the authoritative grid origin. + """ + extent = grid["extent"] + resolution = float(grid["resolution_m"]) + inset = resolution / 4.0 + + return ee.Geometry.Rectangle( + [ + float(extent["xmin"]) + inset, + float(extent["ymin"]) + inset, + float(extent["xmax"]) - inset, + float(extent["ymax"]) - inset, + ], + proj=str(grid["crs"]), + geodesic=False, + ) + + +def start_export( + image: ee.Image, + *, + description: str, + asset_id: str, + pyramiding_policy: dict[str, str], + grid: dict[str, Any], + config: dict[str, Any], +) -> dict[str, Any]: + """Start an exact-grid asset export or record an existing output.""" + overwrite = bool(config["exports"]["overwrite_existing_assets"]) + + if asset_exists(asset_id) and not overwrite: + return { + "task_id": "", + "state": "EXISTS", + "description": description, + "asset_id": asset_id, + } + + transform = [float(value) for value in grid["transform"]] + export_image = image.setDefaultProjection( + str(grid["crs"]), + transform, + ) + + task = ee.batch.Export.image.toAsset( + image=export_image, + description=description, + assetId=asset_id, + pyramidingPolicy=pyramiding_policy, + region=export_grid_region(grid), + crs=str(grid["crs"]), + crsTransform=transform, + maxPixels=int(config["exports"]["max_pixels"]), + shardSize=int(config["exports"]["shard_size"]), + priority=int(config["exports"]["priority"]), + overwrite=overwrite, + ) + task.start() + status = task.status() + + return { + "task_id": status.get("id", task.id), + "state": status.get("state", "READY"), + "description": description, + "asset_id": asset_id, + } + + +def build_all_epoch_bundles( + completed: pd.DataFrame, + core_geometry: ee.Geometry, + grid: dict[str, Any], + config: dict[str, Any], +) -> tuple[ + list[EpochBundle], + dict[str, Any], + list[dict[str, Any]], + list[dict[str, Any]], + list[dict[str, Any]], +]: + """Calculate every epoch and return failures before any export starts.""" + bundles: list[EpochBundle] = [] + histogram_archive: dict[str, Any] = {} + all_threshold_rows: list[dict[str, Any]] = [] + all_area_rows: list[dict[str, Any]] = [] + failures: list[dict[str, Any]] = [] + + for row in completed.itertuples(index=False): + epoch = int(row.epoch) + composite_asset = str(row.asset_id) + count_asset = str(row.count_asset_id) + composite = ee.Image(composite_asset) + count = ee.Image(count_asset) + + index_stack, index_images = build_index_stack( + composite, + count, + epsilon=float(config["indices"]["denominator_epsilon"]), + savi_l=float(config["indices"]["savi_l"]), + minimum_valid_observations=int( + config["classification"][ + "minimum_valid_observations_for_candidate" + ] + ), + ) + histograms = calculate_histograms( + index_images, + count, + core_geometry, + grid, + config, + ) + histogram_archive[str(epoch)] = histograms + thresholds: dict[str, float] = {} + epoch_threshold_rows: list[dict[str, Any]] = [] + epoch_failures: list[dict[str, Any]] = [] + + for index_name in SELECTED_INDICES: + result = otsu_from_histogram(histograms.get(index_name)) + epoch_threshold_rows.append( + threshold_row( + epoch=epoch, + index_name=index_name, + result=result, + source_composite_asset=composite_asset, + source_count_asset=count_asset, + config=config, + ) + ) + + if result.status == PASS and result.threshold is not None: + thresholds[index_name] = float(result.threshold) + else: + epoch_failures.append( + { + "epoch": epoch, + "index_name": index_name, + "status": result.status, + "reason": result.failure_reason, + } + ) + + all_threshold_rows.extend(epoch_threshold_rows) + + if epoch_failures: + failures.extend(epoch_failures) + continue + + candidate_image = build_candidate_stack( + index_images, + thresholds, + ) + area_values = candidate_area_statistics( + candidate_image, + core_geometry, + grid, + config, + ) + epoch_threshold_rows, area_rows = enrich_threshold_and_area_rows( + epoch_threshold_rows, + area_values, + ) + + # Replace the preliminary rows with their area-enriched equivalents. + del all_threshold_rows[-len(epoch_threshold_rows):] + all_threshold_rows.extend(epoch_threshold_rows) + all_area_rows.extend(area_rows) + bundles.append( + EpochBundle( + epoch=epoch, + source_composite_asset=composite_asset, + source_count_asset=count_asset, + index_image=index_stack, + candidate_image=candidate_image, + thresholds=thresholds, + threshold_rows=epoch_threshold_rows, + area_rows=area_rows, + histograms=histograms, + ) + ) + + return ( + bundles, + histogram_archive, + all_threshold_rows, + all_area_rows, + failures, + ) + +def write_pre_export_metadata( + threshold_rows: list[dict[str, Any]], + area_rows: list[dict[str, Any]], + histogram_archive: dict[str, Any], + config: dict[str, Any], + project_root: Path, + grid: dict[str, Any], +) -> tuple[Path, Path, Path, str, str]: + """Write threshold, histogram, area and processing-recipe metadata.""" + metadata_dir = metadata_directory(config, project_root) + threshold_path = metadata_dir / config["metadata"]["threshold_table"] + area_path = metadata_dir / config["metadata"]["candidate_area_summary"] + histogram_path = metadata_dir / config["metadata"]["histograms"] + recipe_path = metadata_dir / config["metadata"]["processing_recipe"] + + pd.DataFrame(threshold_rows).sort_values( + ["epoch", "index_name"] + ).to_csv(threshold_path, index=False) + area_frame = pd.DataFrame(area_rows) + + if area_frame.empty: + area_frame.to_csv(area_path, index=False) + else: + add_area_changes(area_frame).to_csv( + area_path, + index=False, + ) + write_json(histogram_path, histogram_archive) + + orchestration_path = resolve_project_path( + config["inputs"]["orchestration_version"], + project_root, + ) + recipe = { + "pipeline_stage": "built_up_candidates", + "pipeline_version": int(config["version"]), + "source_orchestration_sha256": sha256_file(orchestration_path), + "grid_sha256": stable_object_hash(grid), + "formula_version": int(config["indices"]["formula_version"]), + "denominator_epsilon": float( + config["indices"]["denominator_epsilon"] + ), + "savi_l": float(config["indices"]["savi_l"]), + "index_band_schema": INDEX_BANDS, + "candidate_band_schema": CANDIDATE_BANDS, + "threshold_strategy": config["classification"][ + "primary_strategy" + ], + "threshold_region": config["classification"]["threshold_region"], + "application_region": config["classification"][ + "application_region" + ], + "minimum_valid_observations_for_threshold": config[ + "classification" + ]["minimum_valid_observations_for_threshold"], + "minimum_valid_observations_for_candidate": config[ + "classification" + ]["minimum_valid_observations_for_candidate"], + "classification_directions": config["classification"][ + "built_up_direction" + ], + "pooled_thresholds_computed": False, + } + write_json(recipe_path, recipe) + + return ( + threshold_path, + area_path, + recipe_path, + sha256_file(threshold_path), + sha256_file(recipe_path), + ) + + +def submit_exports( + bundles: list[EpochBundle], + config: dict[str, Any], + project_root: Path, + grid: dict[str, Any], + threshold_table_sha256: str, + processing_recipe_sha256: str, +) -> pd.DataFrame: + """Attach provenance properties and submit two exports per epoch.""" + asset_root = config["exports"]["asset_root"].rstrip("/") + index_folder = f"{asset_root}/{config['exports']['index_folder']}" + candidate_folder = ( + f"{asset_root}/{config['exports']['candidate_folder']}" + ) + ensure_asset_folder(asset_root) + ensure_asset_folder(index_folder) + ensure_asset_folder(candidate_folder) + + orchestration_sha256 = sha256_file( + resolve_project_path( + config["inputs"]["orchestration_version"], + project_root, + ) + ) + tasks: list[dict[str, Any]] = [] + output_rows: list[dict[str, Any]] = [] + + for bundle in bundles: + index_asset = f"{index_folder}/indices_{bundle.epoch}" + candidate_asset = ( + f"{candidate_folder}/candidates_{bundle.epoch}" + ) + index_recipe = { + "source_composite_asset": bundle.source_composite_asset, + "source_count_asset": bundle.source_count_asset, + "source_orchestration_sha256": orchestration_sha256, + "grid_version": 1, + "formula_version": int( + config["indices"]["formula_version"] + ), + "denominator_epsilon": float( + config["indices"]["denominator_epsilon"] + ), + "savi_l": float(config["indices"]["savi_l"]), + "band_schema": INDEX_BANDS, + "output_asset_id": index_asset, + } + index_recipe_sha256 = stable_object_hash(index_recipe) + candidate_recipe = { + **index_recipe, + "threshold_strategy": config["classification"][ + "primary_strategy" + ], + "thresholds": bundle.thresholds, + "classification_direction": config["classification"][ + "built_up_direction" + ], + "band_schema": CANDIDATE_BANDS, + "output_asset_id": candidate_asset, + } + candidate_recipe_sha256 = stable_object_hash(candidate_recipe) + + index_image = bundle.index_image.set( + { + "pipeline_stage": "built_up_candidates", + "pipeline_version": int(config["version"]), + "epoch": bundle.epoch, + "source_composite_asset": bundle.source_composite_asset, + "source_observation_count_asset": ( + bundle.source_count_asset + ), + "grid_version": 1, + "formula_version": int( + config["indices"]["formula_version"] + ), + "denominator_epsilon": float( + config["indices"]["denominator_epsilon"] + ), + "savi_l": float(config["indices"]["savi_l"]), + "recipe_sha256": index_recipe_sha256, + "processing_recipe_sha256": processing_recipe_sha256, + } + ) + candidate_properties = { + "pipeline_stage": "built_up_candidates", + "pipeline_version": int(config["version"]), + "epoch": bundle.epoch, + "source_composite_asset": bundle.source_composite_asset, + "source_observation_count_asset": bundle.source_count_asset, + "grid_version": 1, + "formula_version": int( + config["indices"]["formula_version"] + ), + "threshold_strategy": config["classification"][ + "primary_strategy" + ], + "threshold_table_sha256": threshold_table_sha256, + "recipe_sha256": candidate_recipe_sha256, + "processing_recipe_sha256": processing_recipe_sha256, + } + + for name, threshold in bundle.thresholds.items(): + candidate_properties[f"threshold_{name}"] = float(threshold) + + candidate_image = bundle.candidate_image.set( + candidate_properties + ) + + index_task = start_export( + index_image, + description=f"sprawlgp_indices_{bundle.epoch}", + asset_id=index_asset, + pyramiding_policy={ + ".default": "mean", + "valid_composite": "mode", + }, + grid=grid, + config=config, + ) + index_task.update( + { + "epoch": bundle.epoch, + "product_type": "indices", + "source_composite_asset": ( + bundle.source_composite_asset + ), + "source_count_asset": bundle.source_count_asset, + "recipe_sha256": index_recipe_sha256, + } + ) + tasks.append(index_task) + output_rows.append( + { + **index_task, + "band_names": ",".join(INDEX_BANDS), + "continuous_index_layers": 7, + "binary_candidate_layers": 0, + } + ) + + candidate_task = start_export( + candidate_image, + description=f"sprawlgp_candidates_{bundle.epoch}", + asset_id=candidate_asset, + pyramiding_policy={".default": "mode"}, + grid=grid, + config=config, + ) + candidate_task.update( + { + "epoch": bundle.epoch, + "product_type": "candidates", + "source_composite_asset": ( + bundle.source_composite_asset + ), + "source_count_asset": bundle.source_count_asset, + "recipe_sha256": candidate_recipe_sha256, + } + ) + tasks.append(candidate_task) + output_rows.append( + { + **candidate_task, + "band_names": ",".join(CANDIDATE_BANDS), + "continuous_index_layers": 0, + "binary_candidate_layers": 5, + } + ) + + task_frame = pd.DataFrame(tasks) + task_path = resolve_project_path( + config["exports"]["task_manifest"], + project_root, + ) + task_path.parent.mkdir(parents=True, exist_ok=True) + task_frame.to_csv(task_path, index=False) + + output_frame = pd.DataFrame(output_rows) + output_frame.to_csv( + metadata_directory(config, project_root) + / config["metadata"]["output_manifest"], + index=False, + ) + return task_frame + + +def run_submission(config_path: Path, submit: bool) -> pd.DataFrame: + """Run preflight, validate all thresholds and optionally submit exports.""" + if not submit: + raise ValueError("Pass --submit to create Earth Engine export tasks.") + + preflight = run_preflight(config_path) + project_root = find_project_root(config_path.parent) + config = load_yaml(config_path) + grid = load_grid_specification( + resolve_project_path( + config["inputs"]["grid_specification"], + project_root, + ) + ) + completed = select_completed_composites( + pd.read_csv( + resolve_project_path( + config["inputs"]["composite_manifest"], + project_root, + ) + ), + config, + ) + core_geometry = load_ee_geometry( + resolve_project_path( + config["inputs"]["core_boundary"], + project_root, + ) + ) + ( + bundles, + histogram_archive, + threshold_rows, + area_rows, + failures, + ) = build_all_epoch_bundles( + completed, + core_geometry, + grid, + config, + ) + ( + _, + _, + _, + threshold_sha256, + recipe_sha256, + ) = write_pre_export_metadata( + threshold_rows, + area_rows, + histogram_archive, + config, + project_root, + grid, + ) + + if failures: + raise RuntimeError( + "At least one Otsu threshold failed. Diagnostic histograms and " + "threshold rows were written, and no export task was submitted.\n" + + json.dumps(failures, indent=2) + ) + + tasks = submit_exports( + bundles, + config, + project_root, + grid, + threshold_sha256, + recipe_sha256, + ) + + print( + json.dumps( + { + "preflight_status": preflight["status"], + "processed_epochs": [ + bundle.epoch for bundle in bundles + ], + "submitted_or_existing_tasks": len(tasks), + "task_manifest": config["exports"]["task_manifest"], + }, + indent=2, + ) + ) + return tasks + + +def parse_arguments() -> argparse.Namespace: + """Parse preflight and submission command-line options.""" + parser = argparse.ArgumentParser( + description="Build spectral indices and Otsu candidate maps." + ) + parser.add_argument("--config", required=True, type=Path) + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--preflight-only", action="store_true") + group.add_argument("--submit", action="store_true") + return parser.parse_args() + + +def main() -> None: + """Run the requested built-up candidate operation.""" + arguments = parse_arguments() + + if arguments.preflight_only: + run_preflight(arguments.config.resolve()) + return + + run_submission(arguments.config.resolve(), arguments.submit) + + +if __name__ == "__main__": + main() diff --git a/src/analysis/built_up/finalize.py b/src/analysis/built_up/finalize.py new file mode 100644 index 0000000..3cb7713 --- /dev/null +++ b/src/analysis/built_up/finalize.py @@ -0,0 +1,636 @@ +"""Monitor exports, validate assets and finalize built-up candidate metadata.""" + +from __future__ import annotations + +import argparse +import io +import json +import urllib.request +from pathlib import Path +from typing import Any + +import ee +import matplotlib.image as mpimg +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from src.analysis.orchestration.common import ( + asset_exists, + exact_grid_region, + find_project_root, + initialize_earth_engine, + load_grid_specification, + load_json, + load_yaml, + metadata_directory, + report_directory, + resolve_project_path, + sha256_file, + stable_object_hash, + write_json, +) +from src.analysis.built_up.build_candidates import ( + image_metadata, + load_ee_geometry, + select_completed_composites, +) +from src.analysis.built_up.indices import ( + CANDIDATE_BANDS, + INDEX_BANDS, + SELECTED_INDICES, +) +from src.analysis.built_up.otsu import histogram_quantile + + +TERMINAL_SUCCESS_STATES = {"COMPLETED", "EXISTS"} +TERMINAL_FAILURE_STATES = {"FAILED", "CANCELLED", "CANCEL_REQUESTED"} + + +def refresh_task_manifest(task_frame: pd.DataFrame) -> pd.DataFrame: + """Refresh all task states through one Earth Engine task-list request.""" + refreshed = task_frame.copy() + available = { + task.id: task.status() + for task in ee.batch.Task.list() + if task.id is not None + } + + for index, row in refreshed.iterrows(): + if str(row["state"]) == "EXISTS" or not str(row["task_id"]): + continue + + task_id = str(row["task_id"]) + + if task_id not in available: + raise LookupError( + f"Earth Engine task '{task_id}' was not found." + ) + + status = available[task_id] + refreshed.at[index, "state"] = status.get("state", "UNKNOWN") + refreshed.at[index, "error_message"] = status.get( + "error_message", + "", + ) + + return refreshed + + +def validate_output_grid( + asset_id: str, + expected_bands: list[str], + grid: dict[str, Any], +) -> dict[str, Any]: + """Validate existence, ordered schema, dimensions, CRS and transform.""" + if not asset_exists(asset_id): + raise FileNotFoundError(f"Earth Engine asset not found: {asset_id}") + + metadata = image_metadata(asset_id) + + if metadata["band_names"] != expected_bands: + raise ValueError( + f"Unexpected bands in {asset_id}: {metadata['band_names']}" + ) + + if metadata["crs"] != grid["crs"]: + raise ValueError( + f"Unexpected CRS in {asset_id}: {metadata['crs']}" + ) + + if not np.allclose( + metadata["transform"], + [float(value) for value in grid["transform"]], + atol=1e-9, + ): + raise ValueError( + f"Unexpected transform in {asset_id}: " + f"{metadata['transform']}" + ) + + if ( + metadata["width"] != int(grid["width"]) + or metadata["height"] != int(grid["height"]) + ): + raise ValueError( + f"Unexpected dimensions in {asset_id}: " + f"{metadata['width']} x {metadata['height']}" + ) + + return metadata + + +def validate_band_types( + asset_id: str, + product_type: str, +) -> None: + """Validate Float32 index bands and UInt8 categorical bands.""" + band_types = ee.Image(asset_id).bandTypes().getInfo() + + if product_type == "indices": + for name in INDEX_BANDS[:-1]: + precision = band_types[name].get("precision") + if precision != "float": + raise ValueError( + f"Index band {name} in {asset_id} is not float." + ) + + valid_type = band_types["valid_composite"] + if ( + valid_type.get("precision") != "int" + or float(valid_type.get("min", -1)) < 0 + or float(valid_type.get("max", 256)) > 255 + ): + raise ValueError( + f"valid_composite in {asset_id} is not UInt8-compatible." + ) + else: + for name in CANDIDATE_BANDS: + data_type = band_types[name] + if ( + data_type.get("precision") != "int" + or float(data_type.get("min", -1)) < 0 + or float(data_type.get("max", 256)) > 255 + ): + raise ValueError( + f"Candidate band {name} in {asset_id} " + "is not UInt8-compatible." + ) + + +def validate_candidate_domain_and_masks( + asset_id: str, + core_geometry: ee.Geometry, + grid: dict[str, Any], + config: dict[str, Any], +) -> None: + """Ensure candidate values are binary and masks equal validity bands.""" + image = ee.Image(asset_id) + checks: list[ee.Image] = [] + + for name in SELECTED_INDICES: + built = image.select(f"built_{name}") + valid = image.select(f"valid_{name}") + checks.extend( + [ + built.rename(f"{name}_built"), + built.mask().unmask(0).neq(valid.eq(1)).rename( + f"{name}_mask_mismatch" + ), + ] + ) + + values = ee.Image.cat(checks).reduceRegion( + reducer=ee.Reducer.minMax(), + geometry=core_geometry, + crs=grid["crs"], + crsTransform=grid["transform"], + maxPixels=int(config["histogram"]["max_pixels"]), + tileScale=int(config["histogram"]["tile_scale"]), + ).getInfo() + + for name in SELECTED_INDICES: + minimum = values.get(f"{name}_built_min") + maximum = values.get(f"{name}_built_max") + mismatch = values.get(f"{name}_mask_mismatch_max", 0) + + if minimum != 0 or maximum != 1: + raise ValueError( + f"Candidate {name} in {asset_id} does not contain " + "both binary classes in the administrative core." + ) + + if float(mismatch or 0) != 0: + raise ValueError( + f"Candidate mask differs from valid_{name} in {asset_id}." + ) + + +def validate_asset_properties( + row: pd.Series, +) -> None: + """Compare source and recipe properties with the local output manifest.""" + image = ee.Image(str(row["asset_id"])) + properties = image.toDictionary( + [ + "source_composite_asset", + "source_observation_count_asset", + "recipe_sha256", + ] + ).getInfo() + + if properties.get("source_composite_asset") != str( + row["source_composite_asset"] + ): + raise ValueError( + f"Source-composite property mismatch in {row['asset_id']}." + ) + + if properties.get("source_observation_count_asset") != str( + row["source_count_asset"] + ): + raise ValueError( + f"Source-count property mismatch in {row['asset_id']}." + ) + + if properties.get("recipe_sha256") != str(row["recipe_sha256"]): + raise ValueError( + f"Recipe SHA-256 mismatch in {row['asset_id']}." + ) + + +def save_figure(figure: plt.Figure, path: Path) -> None: + """Save one QA figure and release its memory.""" + path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(path, dpi=180, bbox_inches="tight") + plt.close(figure) + + +def create_histogram_gallery( + histograms: dict[str, Any], + threshold_table: pd.DataFrame, + output_path: Path, +) -> None: + """Create the required epoch-by-index histogram and threshold gallery.""" + epochs = sorted(int(value) for value in histograms) + figure, axes = plt.subplots( + len(epochs), + len(SELECTED_INDICES), + figsize=(18, 3.2 * len(epochs)), + squeeze=False, + ) + + for row_index, epoch in enumerate(epochs): + for column_index, name in enumerate(SELECTED_INDICES): + axis = axes[row_index, column_index] + histogram = histograms[str(epoch)][name] + means = np.asarray(histogram["bucketMeans"], dtype=float) + counts = np.asarray(histogram["histogram"], dtype=float) + threshold = float( + threshold_table.loc[ + (threshold_table["epoch"] == epoch) + & (threshold_table["index_name"] == name), + "threshold_value", + ].iloc[0] + ) + axis.plot(means, counts) + axis.axvline(threshold, linestyle="--") + axis.set_title(f"{epoch} — {name}") + axis.tick_params(labelsize=7) + + figure.suptitle("Index histograms and epoch-specific Otsu thresholds") + save_figure(figure, output_path) + + +def read_thumbnail(url: str) -> np.ndarray: + """Download one Earth Engine PNG thumbnail into a NumPy array.""" + with urllib.request.urlopen(url) as response: + return mpimg.imread(io.BytesIO(response.read()), format="png") + + +def index_display_ranges( + histograms: dict[str, Any], +) -> dict[str, tuple[float, float]]: + """Calculate robust per-index display ranges shared across all epochs.""" + ranges: dict[str, tuple[float, float]] = {} + + for name in SELECTED_INDICES: + lower = [] + upper = [] + + for epoch_histograms in histograms.values(): + histogram = epoch_histograms[name] + lower.append(histogram_quantile(histogram, 0.02)) + upper.append(histogram_quantile(histogram, 0.98)) + + ranges[name] = (float(min(lower)), float(max(upper))) + + return ranges + + +def create_map_gallery( + output_manifest: pd.DataFrame, + histograms: dict[str, Any], + grid: dict[str, Any], + output_path: Path, + *, + product_type: str, + dimensions: int, +) -> None: + """Create an index or candidate thumbnail gallery from completed assets.""" + rows = output_manifest[ + output_manifest["product_type"] == product_type + ].sort_values("epoch") + epochs = rows["epoch"].astype(int).tolist() + ranges = index_display_ranges(histograms) + figure, axes = plt.subplots( + len(epochs), + len(SELECTED_INDICES), + figsize=(16, 3.0 * len(epochs)), + squeeze=False, + ) + + for row_index, (_, row) in enumerate(rows.iterrows()): + image = ee.Image(str(row["asset_id"])) + + for column_index, name in enumerate(SELECTED_INDICES): + axis = axes[row_index, column_index] + axis.set_xticks([]) + axis.set_yticks([]) + + try: + if product_type == "indices": + minimum, maximum = ranges[name] + parameters = { + "bands": name, + "min": minimum, + "max": maximum, + "region": exact_grid_region(grid), + "dimensions": int(dimensions), + "format": "png", + } + else: + parameters = { + "bands": f"built_{name}", + "min": 0, + "max": 1, + "palette": ["f7f7f7", "d73027"], + "region": exact_grid_region(grid), + "dimensions": int(dimensions), + "format": "png", + } + + axis.imshow(read_thumbnail(image.getThumbURL(parameters))) + except Exception as error: + axis.text( + 0.5, + 0.5, + f"Thumbnail unavailable\n{type(error).__name__}", + ha="center", + va="center", + ) + + axis.set_title(f"{int(row['epoch'])} — {name}") + + title = ( + "Continuous built-up index gallery" + if product_type == "indices" + else "Unvalidated Otsu candidate gallery" + ) + figure.suptitle(title) + save_figure(figure, output_path) + + +def write_report( + output_manifest: pd.DataFrame, + threshold_table: pd.DataFrame, + area_summary: pd.DataFrame, + output_path: Path, +) -> None: + """Write a concise scientific and operational completion report.""" + epochs = sorted(output_manifest["epoch"].astype(int).unique()) + lines = [ + "# Spectral indices and Otsu built-up candidates", + "", + "## Output summary", + "", + f"- Completed epochs: {', '.join(map(str, epochs))}", + f"- Epoch count: {len(epochs)}", + f"- Continuous index layers: {len(epochs) * 7}", + f"- Binary candidate maps: {len(epochs) * 5}", + f"- Epoch-specific Otsu thresholds: {len(threshold_table)}", + "", + "The binary maps are unvalidated candidate pseudo-labels. No index " + "has been selected as the final historical built-up mapping method.", + "", + "## Thresholds", + "", + "| Epoch | Index | Threshold | Valid pixels | Built fraction | Status |", + "|---:|---|---:|---:|---:|:---:|", + ] + + area_index = area_summary.set_index(["epoch", "index_name"]) + + for row in threshold_table.sort_values( + ["epoch", "index_name"] + ).itertuples(index=False): + area = area_index.loc[(int(row.epoch), row.index_name)] + lines.append( + "| " + f"{int(row.epoch)} | {row.index_name} | " + f"{float(row.threshold_value):.6f} | " + f"{int(row.histogram_valid_pixel_count)} | " + f"{float(area['candidate_built_fraction']):.4f} | " + f"{row.status} |" + ) + + lines.extend( + [ + "", + "## Interpretation limits", + "", + "- Thresholds are estimated independently for each epoch.", + "- Candidate-area changes are quality diagnostics, not validated growth.", + "- Invalid index pixels remain masked and are never recoded as non-built.", + "- No temporal persistence correction or transition map is applied.", + "- Comparative validation and final method selection belong to the next stage.", + ] + ) + output_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def run_finalize(config_path: Path, status_only: bool) -> dict[str, Any]: + """Refresh task states and finalize all validated outputs.""" + project_root = find_project_root(config_path.parent) + config = load_yaml(config_path) + initialize_earth_engine(config["project"]["earth_engine_project"]) + + task_path = resolve_project_path( + config["exports"]["task_manifest"], + project_root, + ) + + if not task_path.is_file(): + raise FileNotFoundError( + "Submit built-up candidate tasks before finalization." + ) + + tasks = pd.read_csv(task_path, keep_default_na=False) + tasks = refresh_task_manifest(tasks) + tasks.to_csv(task_path, index=False) + state_counts = tasks["state"].value_counts().to_dict() + print(json.dumps({"task_states": state_counts}, indent=2)) + + if status_only: + return {"task_states": state_counts} + + failures = tasks[tasks["state"].isin(TERMINAL_FAILURE_STATES)] + + if not failures.empty: + raise RuntimeError( + "At least one built-up candidate export failed." + ) + + incomplete = tasks[ + ~tasks["state"].isin(TERMINAL_SUCCESS_STATES) + ] + + if not incomplete.empty: + raise RuntimeError( + "Exports are incomplete. Run the status command later." + ) + + grid = load_grid_specification( + resolve_project_path( + config["inputs"]["grid_specification"], + project_root, + ) + ) + core_geometry = load_ee_geometry( + resolve_project_path( + config["inputs"]["core_boundary"], + project_root, + ) + ) + metadata_dir = metadata_directory(config, project_root) + output_path = metadata_dir / config["metadata"]["output_manifest"] + outputs = pd.read_csv(output_path, keep_default_na=False) + + for index, row in outputs.iterrows(): + expected_bands = ( + INDEX_BANDS + if row["product_type"] == "indices" + else CANDIDATE_BANDS + ) + validate_output_grid( + str(row["asset_id"]), + expected_bands, + grid, + ) + validate_band_types( + str(row["asset_id"]), + str(row["product_type"]), + ) + validate_asset_properties(row) + + if row["product_type"] == "candidates": + validate_candidate_domain_and_masks( + str(row["asset_id"]), + core_geometry, + grid, + config, + ) + + outputs.at[index, "state"] = "PASS" + + outputs.to_csv(output_path, index=False) + threshold_path = metadata_dir / config["metadata"]["threshold_table"] + area_path = metadata_dir / config["metadata"]["candidate_area_summary"] + histogram_path = metadata_dir / config["metadata"]["histograms"] + recipe_path = metadata_dir / config["metadata"]["processing_recipe"] + dependency_path = ( + metadata_dir / config["metadata"]["dependency_manifest"] + ) + threshold_table = pd.read_csv(threshold_path) + area_summary = pd.read_csv(area_path) + histograms = load_json(histogram_path) + + version_payload = { + "pipeline_stage": "built_up_candidates", + "pipeline_version": int(config["version"]), + "processed_epochs": sorted( + outputs["epoch"].astype(int).unique().tolist() + ), + "grid_specification_sha256": sha256_file( + resolve_project_path( + config["inputs"]["grid_specification"], + project_root, + ) + ), + "orchestration_version_sha256": sha256_file( + resolve_project_path( + config["inputs"]["orchestration_version"], + project_root, + ) + ), + "dependency_manifest_sha256": sha256_file(dependency_path), + "histograms_sha256": sha256_file(histogram_path), + "threshold_table_sha256": sha256_file(threshold_path), + "candidate_area_summary_sha256": sha256_file(area_path), + "processing_recipe_sha256": sha256_file(recipe_path), + "task_manifest_sha256": sha256_file(task_path), + "output_manifest_sha256": sha256_file(output_path), + "earth_engine_assets": outputs["asset_id"].astype(str).tolist(), + "continuous_index_layer_count": int( + outputs["continuous_index_layers"].sum() + ), + "binary_candidate_layer_count": int( + outputs["binary_candidate_layers"].sum() + ), + } + version_payload["stable_signature"] = stable_object_hash( + version_payload + ) + write_json( + metadata_dir / config["metadata"]["version"], + version_payload, + ) + + reports = report_directory(config, project_root) + create_histogram_gallery( + histograms, + threshold_table, + reports / config["reports"]["histogram_gallery"], + ) + + if bool(config["reports"].get("generate_map_galleries", True)): + create_map_gallery( + outputs, + histograms, + grid, + reports / config["reports"]["index_gallery"], + product_type="indices", + dimensions=int( + config["reports"]["thumbnail_dimensions"] + ), + ) + create_map_gallery( + outputs, + histograms, + grid, + reports / config["reports"]["candidate_gallery"], + product_type="candidates", + dimensions=int( + config["reports"]["thumbnail_dimensions"] + ), + ) + + write_report( + outputs, + threshold_table, + area_summary, + reports / config["reports"]["report"], + ) + + print(json.dumps(version_payload, indent=2)) + return version_payload + + +def parse_arguments() -> argparse.Namespace: + """Parse status and finalization command-line arguments.""" + parser = argparse.ArgumentParser( + description="Monitor and finalize built-up candidate assets." + ) + parser.add_argument("--config", required=True, type=Path) + parser.add_argument("--status-only", action="store_true") + return parser.parse_args() + + +def main() -> None: + """Run task monitoring or complete finalization.""" + arguments = parse_arguments() + run_finalize(arguments.config.resolve(), arguments.status_only) + + +if __name__ == "__main__": + main() diff --git a/src/analysis/built_up/indices.py b/src/analysis/built_up/indices.py new file mode 100644 index 0000000..ed96f5a --- /dev/null +++ b/src/analysis/built_up/indices.py @@ -0,0 +1,275 @@ +"""Spectral-index formulas, formula-specific masks and candidate construction.""" + +from __future__ import annotations + +import math +from typing import Any + +try: + import ee +except ImportError: # Allows pure formula tests without Earth Engine installed. + ee = None + + +COMMON_BANDS = ["blue", "green", "red", "nir", "swir1", "swir2"] +INTERMEDIATE_INDICES = ["savi", "mndwi"] +SELECTED_INDICES = ["ndbi", "ibi", "ibui", "vbswir1_bi", "ndbsui"] +INDEX_BANDS = INTERMEDIATE_INDICES + SELECTED_INDICES + ["valid_composite"] +CANDIDATE_BANDS = ( + [f"built_{name}" for name in SELECTED_INDICES] + + [f"valid_{name}" for name in SELECTED_INDICES] +) + + +def require_earth_engine() -> None: + """Raise an actionable error when Earth Engine is unavailable.""" + if ee is None: + raise ImportError( + "The Earth Engine Python API is required for raster processing." + ) + + +def safe_ratio( + numerator: Any, + denominator: Any, + epsilon: float, + name: str, +): + """Divide two Earth Engine images while masking unstable denominators.""" + require_earth_engine() + valid_denominator = denominator.abs().gt(float(epsilon)) + safe_numerator = numerator.updateMask(valid_denominator) + safe_denominator = denominator.updateMask(valid_denominator) + + return ( + safe_numerator.divide(safe_denominator) + .rename(name) + .toFloat() + ) + + +def build_index_stack( + composite, + observation_count, + *, + epsilon: float, + savi_l: float, + minimum_valid_observations: int, +): + """Calculate seven continuous indices and the explicit composite-valid band. + + Each index retains the maximum valid coverage permitted by its own formula. + NDBSUI receives an additional mask where red or SWIR1 reflectance is + negative, while the other indices are not forced to share that mask. + """ + require_earth_engine() + composite = composite.select(COMMON_BANDS) + count = observation_count.select("valid_observation_count") + + all_bands_valid = ( + composite.mask() + .reduce(ee.Reducer.min()) + .eq(1) + ) + observation_valid = count.gte(int(minimum_valid_observations)) + base_valid = all_bands_valid.And(observation_valid) + + source = composite.updateMask(base_valid) + blue = source.select("blue") + green = source.select("green") + red = source.select("red") + nir = source.select("nir") + swir1 = source.select("swir1") + swir2 = source.select("swir2") + + savi = safe_ratio( + nir.subtract(red).multiply(1.0 + float(savi_l)), + nir.add(red).add(float(savi_l)), + epsilon, + "savi", + ) + mndwi = safe_ratio( + green.subtract(swir1), + green.add(swir1), + epsilon, + "mndwi", + ) + ndbi = safe_ratio( + swir1.subtract(nir), + swir1.add(nir), + epsilon, + "ndbi", + ) + + background = savi.add(mndwi).divide(2.0) + ibi = safe_ratio( + ndbi.subtract(background), + ndbi.add(background), + epsilon, + "ibi", + ) + ibui = ( + ndbi.subtract(savi) + .subtract(mndwi) + .rename("ibui") + .toFloat() + ) + vbswir1_bi = safe_ratio( + swir1.subtract(blue), + swir1.add(blue), + epsilon, + "vbswir1_bi", + ) + + nonnegative_inputs = red.gte(0).And(swir1.gte(0)) + geometric_mean = ( + red.max(0) + .multiply(swir1.max(0)) + .sqrt() + ) + ndbsui = safe_ratio( + swir2.add(geometric_mean).subtract(red.add(swir1)), + swir2.add(geometric_mean).add(red.add(swir1)), + epsilon, + "ndbsui", + ).updateMask(nonnegative_inputs) + + valid_composite = ( + base_valid.rename("valid_composite") + .unmask(0) + .toUint8() + ) + + images = { + "savi": savi, + "mndwi": mndwi, + "ndbi": ndbi, + "ibi": ibi, + "ibui": ibui, + "vbswir1_bi": vbswir1_bi, + "ndbsui": ndbsui, + } + stack = ee.Image.cat( + [images[name] for name in INTERMEDIATE_INDICES + SELECTED_INDICES] + + [valid_composite] + ).select(INDEX_BANDS) + + return stack, images + + +def build_candidate_stack( + index_images: dict[str, Any], + thresholds: dict[str, float], +): + """Create five masked candidate bands and five explicit validity bands.""" + require_earth_engine() + built_bands = [] + valid_bands = [] + + for index_name in SELECTED_INDICES: + if index_name not in thresholds: + raise KeyError(f"Missing threshold for index '{index_name}'.") + + index_image = index_images[index_name] + valid = ( + index_image.mask() + .reduce(ee.Reducer.min()) + .rename(f"valid_{index_name}") + .unmask(0) + .toUint8() + ) + built = ( + index_image.gt(float(thresholds[index_name])) + .rename(f"built_{index_name}") + .updateMask(valid.eq(1)) + .toUint8() + ) + built_bands.append(built) + valid_bands.append(valid) + + return ee.Image.cat(built_bands + valid_bands).select(CANDIDATE_BANDS) + + +def _safe_scalar_ratio( + numerator: float, + denominator: float, + epsilon: float, +) -> float | None: + """Return a scalar ratio or ``None`` for an unstable denominator.""" + if not math.isfinite(numerator) or not math.isfinite(denominator): + return None + + if abs(denominator) <= epsilon: + return None + + return numerator / denominator + + +def reference_index_values( + *, + blue: float, + green: float, + red: float, + nir: float, + swir1: float, + swir2: float, + epsilon: float = 1e-6, + savi_l: float = 0.5, +) -> dict[str, float | None]: + """Calculate scalar reference values used by the critical formula tests. + + This function is a small executable specification of the Earth Engine + formulas. ``None`` represents a value that must remain masked. + """ + savi = _safe_scalar_ratio( + (nir - red) * (1.0 + savi_l), + nir + red + savi_l, + epsilon, + ) + mndwi = _safe_scalar_ratio( + green - swir1, + green + swir1, + epsilon, + ) + ndbi = _safe_scalar_ratio( + swir1 - nir, + swir1 + nir, + epsilon, + ) + vbswir1_bi = _safe_scalar_ratio( + swir1 - blue, + swir1 + blue, + epsilon, + ) + + if savi is None or mndwi is None or ndbi is None: + ibi = None + ibui = None + else: + background = (savi + mndwi) / 2.0 + ibi = _safe_scalar_ratio( + ndbi - background, + ndbi + background, + epsilon, + ) + ibui = ndbi - savi - mndwi + + if red < 0 or swir1 < 0: + ndbsui = None + else: + geometric_mean = math.sqrt(red * swir1) + ndbsui = _safe_scalar_ratio( + swir2 + geometric_mean - (red + swir1), + swir2 + geometric_mean + (red + swir1), + epsilon, + ) + + return { + "savi": savi, + "mndwi": mndwi, + "ndbi": ndbi, + "ibi": ibi, + "ibui": ibui, + "vbswir1_bi": vbswir1_bi, + "ndbsui": ndbsui, + } diff --git a/src/analysis/built_up/otsu.py b/src/analysis/built_up/otsu.py new file mode 100644 index 0000000..49753f1 --- /dev/null +++ b/src/analysis/built_up/otsu.py @@ -0,0 +1,203 @@ +"""Deterministic local Otsu calculation and histogram validation.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass +from typing import Any + +import numpy as np + + +PASS = "PASS" +FAIL_NO_VALID_PIXELS = "FAIL_NO_VALID_PIXELS" +FAIL_DEGENERATE_HISTOGRAM = "FAIL_DEGENERATE_HISTOGRAM" +FAIL_EMPTY_CLASS = "FAIL_EMPTY_CLASS" +FAIL_NONFINITE_THRESHOLD = "FAIL_NONFINITE_THRESHOLD" + + +@dataclass(frozen=True) +class OtsuResult: + """Represent one successful or failed Otsu threshold calculation.""" + + status: str + threshold: float | None + failure_reason: str + histogram_bucket_count: int + histogram_valid_pixel_count: int + histogram_min: float | None + histogram_max: float | None + histogram_mean: float | None + + def to_dict(self) -> dict[str, Any]: + """Return a JSON-serialisable result dictionary.""" + return asdict(self) + + +def _failure( + status: str, + reason: str, + *, + bucket_count: int = 0, + valid_pixel_count: int = 0, + histogram_min: float | None = None, + histogram_max: float | None = None, + histogram_mean: float | None = None, +) -> OtsuResult: + """Build a consistently populated failed Otsu result.""" + return OtsuResult( + status=status, + threshold=None, + failure_reason=reason, + histogram_bucket_count=bucket_count, + histogram_valid_pixel_count=valid_pixel_count, + histogram_min=histogram_min, + histogram_max=histogram_max, + histogram_mean=histogram_mean, + ) + + +def otsu_from_histogram(histogram: dict[str, Any] | None) -> OtsuResult: + """Calculate Otsu's threshold from an Earth Engine histogram dictionary. + + The threshold is the midpoint between the two adjacent bucket centres at + the split that maximises between-class variance. No fallback threshold is + fabricated when the distribution is empty or degenerate. + """ + if not histogram: + return _failure( + FAIL_NO_VALID_PIXELS, + "No histogram was returned.", + ) + + counts = np.asarray(histogram.get("histogram", []), dtype=float) + means = np.asarray(histogram.get("bucketMeans", []), dtype=float) + + if counts.size == 0 or means.size == 0 or counts.size != means.size: + return _failure( + FAIL_NO_VALID_PIXELS, + "Histogram counts or bucket centres are missing.", + ) + + finite = np.isfinite(counts) & np.isfinite(means) & (counts > 0) + counts = counts[finite] + means = means[finite] + + if counts.size == 0: + return _failure( + FAIL_NO_VALID_PIXELS, + "The histogram contains no positive finite counts.", + ) + + order = np.argsort(means) + counts = counts[order] + means = means[order] + valid_pixel_count = int(round(float(counts.sum()))) + histogram_mean = float(np.average(means, weights=counts)) + histogram_min = float(means.min()) + histogram_max = float(means.max()) + bucket_count = int(counts.size) + + if bucket_count < 2 or np.allclose(means, means[0]): + return _failure( + FAIL_DEGENERATE_HISTOGRAM, + "At least two distinct non-empty histogram bins are required.", + bucket_count=bucket_count, + valid_pixel_count=valid_pixel_count, + histogram_min=histogram_min, + histogram_max=histogram_max, + histogram_mean=histogram_mean, + ) + + cumulative_weight = np.cumsum(counts) + cumulative_sum = np.cumsum(counts * means) + total_weight = cumulative_weight[-1] + total_sum = cumulative_sum[-1] + + left_weight = cumulative_weight[:-1] + right_weight = total_weight - left_weight + valid_splits = (left_weight > 0) & (right_weight > 0) + + if not valid_splits.any(): + return _failure( + FAIL_EMPTY_CLASS, + "No split leaves observations in both classes.", + bucket_count=bucket_count, + valid_pixel_count=valid_pixel_count, + histogram_min=histogram_min, + histogram_max=histogram_max, + histogram_mean=histogram_mean, + ) + + left_mean = cumulative_sum[:-1] / left_weight + right_mean = (total_sum - cumulative_sum[:-1]) / right_weight + between_variance = ( + left_weight + * right_weight + * np.square(left_mean - right_mean) + ) + between_variance[~valid_splits] = -np.inf + split_index = int(np.argmax(between_variance)) + + if not np.isfinite(between_variance[split_index]): + return _failure( + FAIL_EMPTY_CLASS, + "Between-class variance has no valid finite maximum.", + bucket_count=bucket_count, + valid_pixel_count=valid_pixel_count, + histogram_min=histogram_min, + histogram_max=histogram_max, + histogram_mean=histogram_mean, + ) + + threshold = float( + (means[split_index] + means[split_index + 1]) / 2.0 + ) + + if not np.isfinite(threshold): + return _failure( + FAIL_NONFINITE_THRESHOLD, + "The selected threshold is not finite.", + bucket_count=bucket_count, + valid_pixel_count=valid_pixel_count, + histogram_min=histogram_min, + histogram_max=histogram_max, + histogram_mean=histogram_mean, + ) + + return OtsuResult( + status=PASS, + threshold=threshold, + failure_reason="", + histogram_bucket_count=bucket_count, + histogram_valid_pixel_count=valid_pixel_count, + histogram_min=histogram_min, + histogram_max=histogram_max, + histogram_mean=histogram_mean, + ) + + +def histogram_quantile( + histogram: dict[str, Any], + probability: float, +) -> float: + """Approximate a quantile from histogram bucket centres and counts.""" + if probability < 0 or probability > 1: + raise ValueError("Probability must be in the interval [0, 1].") + + counts = np.asarray(histogram["histogram"], dtype=float) + means = np.asarray(histogram["bucketMeans"], dtype=float) + finite = np.isfinite(counts) & np.isfinite(means) & (counts > 0) + + if not finite.any(): + raise ValueError("The histogram contains no valid observations.") + + counts = counts[finite] + means = means[finite] + order = np.argsort(means) + counts = counts[order] + means = means[order] + cumulative = np.cumsum(counts) + target = probability * cumulative[-1] + index = int(np.searchsorted(cumulative, target, side="left")) + index = min(index, len(means) - 1) + return float(means[index]) diff --git a/tests/reference/built_up_candidates_v1.json b/tests/reference/built_up_candidates_v1.json new file mode 100644 index 0000000..eac5fe9 --- /dev/null +++ b/tests/reference/built_up_candidates_v1.json @@ -0,0 +1,35 @@ +{ + "binary_candidate_layer_count": 25, + "candidate_area_summary_sha256": "0d9a0b41fe08b9383a433d477b873d117f414d134d877145c2cc6d3bad2655b9", + "continuous_index_layer_count": 35, + "dependency_manifest_sha256": "51273195d56387ebeb655f844f66bbb239308ba6332ff6d01a079d65af97923b", + "earth_engine_assets": [ + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2025" + ], + "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", + "histograms_sha256": "a826db830346f1d7cc93f27a756c2a2a356a5abde85cd3d68f12340cf41c5fde", + "orchestration_version_sha256": "2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7", + "output_manifest_sha256": "6be6c2cd314e377a5f73b82377eede085b57ac9b287b5958300d8edc5a79054f", + "pipeline_stage": "built_up_candidates", + "pipeline_version": 1, + "processed_epochs": [ + 2005, + 2010, + 2015, + 2020, + 2025 + ], + "processing_recipe_sha256": "186ae55cfc67cefc6a7109e676bb4be5f59dce00eefe1bdef1b6c3f7ad7e017c", + "stable_signature": "d0d437043b4c2113c7769c0bae912a754cd7a3383cac15715429962b54847650", + "task_manifest_sha256": "4111859d2af577a69e9718d6ef5b22db3dfa81b39863bdcc218b05b4ec0862c4", + "threshold_table_sha256": "cfded18084e387b8f227d3a42edc8868f5090e081fc4980ca4ae241714bc3308" +} diff --git a/tests/test_built_up_candidates.py b/tests/test_built_up_candidates.py new file mode 100644 index 0000000..95e539a --- /dev/null +++ b/tests/test_built_up_candidates.py @@ -0,0 +1,289 @@ +"""Critical tests for formulas, Otsu thresholds, outputs and traceability.""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +import pytest +import yaml + +from src.analysis.built_up.indices import ( + CANDIDATE_BANDS, + INDEX_BANDS, + reference_index_values, +) +from src.analysis.built_up.otsu import ( + FAIL_DEGENERATE_HISTOGRAM, + FAIL_NO_VALID_PIXELS, + PASS, + otsu_from_histogram, +) + + +PROJECT_ROOT = Path(__file__).resolve().parents[1] +CONFIG_PATH = PROJECT_ROOT / "configs/built_up_candidates.yaml" + + +@pytest.fixture(scope="session") +def config() -> dict[str, Any]: + """Load the built-up candidate configuration.""" + with CONFIG_PATH.open("r", encoding="utf-8") as file: + return yaml.safe_load(file) + + +def project_path(value: str | Path) -> Path: + """Resolve one repository-relative path.""" + path = Path(value) + return path if path.is_absolute() else PROJECT_ROOT / path + + +def generated_tables( + config: dict[str, Any], +) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame] | None: + """Load generated tables or return ``None`` before pipeline finalization.""" + directory = project_path(config["metadata"]["directory"]) + paths = [ + directory / config["metadata"]["output_manifest"], + directory / config["metadata"]["threshold_table"], + directory / config["metadata"]["candidate_area_summary"], + ] + + if not all(path.is_file() for path in paths): + return None + + return tuple(pd.read_csv(path) for path in paths) + + +# 1. Formula specification and mask-sensitive numerical safeguards. + + +def test_index_formulas_on_reference_pixels() -> None: + """Check all formulas and the two crucial invalid-value safeguards.""" + values = reference_index_values( + blue=0.1, + green=0.2, + red=0.3, + nir=0.5, + swir1=0.4, + swir2=0.45, + ) + expected = { + "savi": 0.23076923076923078, + "mndwi": -0.3333333333333333, + "ndbi": -0.11111111111111108, + "ibi": 0.3684210526315789, + "ibui": -0.008547008547008572, + "vbswir1_bi": 0.6000000000000001, + "ndbsui": 0.06442763086842888, + } + + for name, expected_value in expected.items(): + assert values[name] == pytest.approx(expected_value) + + zero_denominator = reference_index_values( + blue=0.1, + green=0.2, + red=0.3, + nir=0.5, + swir1=-0.1, + swir2=0.45, + ) + assert zero_denominator["vbswir1_bi"] is None + + negative_square_root_input = reference_index_values( + blue=0.1, + green=0.2, + red=-0.01, + nir=0.5, + swir1=0.4, + swir2=0.45, + ) + assert negative_square_root_input["ndbsui"] is None + + +# 2. Otsu must find a defensible split for a clearly bimodal histogram. + + +def test_otsu_returns_expected_split_for_bimodal_histogram() -> None: + """Ensure the selected threshold lies between two separated modes.""" + result = otsu_from_histogram( + { + "bucketMeans": [0, 1, 2, 8, 9, 10], + "histogram": [10, 20, 10, 10, 20, 10], + } + ) + + assert result.status == PASS + assert result.threshold == pytest.approx(5.0) + assert 2 < result.threshold < 8 + + +# 3. Empty and degenerate distributions must never receive a fallback threshold. + + +@pytest.mark.parametrize( + ("histogram", "expected_status"), + [ + (None, FAIL_NO_VALID_PIXELS), + ( + {"bucketMeans": [], "histogram": []}, + FAIL_NO_VALID_PIXELS, + ), + ( + {"bucketMeans": [1.0], "histogram": [100]}, + FAIL_DEGENERATE_HISTOGRAM, + ), + ( + { + "bucketMeans": [1.0, 1.0], + "histogram": [50, 50], + }, + FAIL_DEGENERATE_HISTOGRAM, + ), + ], +) +def test_otsu_rejects_degenerate_histograms( + histogram: dict[str, Any] | None, + expected_status: str, +) -> None: + """Ensure invalid histograms produce explicit failure states.""" + result = otsu_from_histogram(histogram) + assert result.status == expected_status + assert result.threshold is None + + +# 4. Dynamic metadata counts must match passed orchestration epochs. + + +def test_processed_epochs_and_dynamic_counts( + config: dict[str, Any], +) -> None: + """Check two assets, seven indices and five candidates per epoch.""" + tables = generated_tables(config) + + if tables is None: + pytest.skip("Generated built-up candidate metadata is not available.") + + outputs, thresholds, areas = tables + source = pd.read_csv( + project_path(config["inputs"]["composite_manifest"]) + ) + expected = source[ + source["status"].astype(str).str.upper() == "PASS" + ]["epoch"].astype(int) + + include = config.get("epochs", {}).get("include") + + if include: + expected = expected[expected.isin([int(value) for value in include])] + + expected_epochs = set(expected) + observed_epochs = set(outputs["epoch"].astype(int)) + + assert observed_epochs == expected_epochs + assert len(outputs) == len(expected_epochs) * 2 + assert outputs["continuous_index_layers"].sum() == ( + len(expected_epochs) * 7 + ) + assert outputs["binary_candidate_layers"].sum() == ( + len(expected_epochs) * 5 + ) + assert len(thresholds) == len(expected_epochs) * 5 + assert len(areas) == len(expected_epochs) * 5 + + +# 5. Every successful threshold must be finite, bounded and non-degenerate. + + +def test_thresholds_are_finite_bounded_and_create_two_classes( + config: dict[str, Any], +) -> None: + """Validate all scientific acceptance conditions in the threshold table.""" + tables = generated_tables(config) + + if tables is None: + pytest.skip("Generated built-up candidate metadata is not available.") + + _, thresholds, _ = tables + successful = thresholds[thresholds["status"] == PASS] + + assert len(successful) == len(thresholds) + assert np.isfinite(successful["threshold_value"]).all() + assert ( + successful["threshold_value"] + >= successful["histogram_min"] + ).all() + assert ( + successful["threshold_value"] + <= successful["histogram_max"] + ).all() + assert (successful["histogram_valid_pixel_count"] > 0).all() + assert (successful["candidate_built_pixel_count_core"] > 0).all() + assert (successful["candidate_nonbuilt_pixel_count_core"] > 0).all() + + +# 6–7. Post-export schema, grid, types, binary domain and recipe traceability. + + +def test_exported_assets_schema_grid_domain_and_traceability( + config: dict[str, Any], +) -> None: + """Run the critical integration checks against finalized EE assets.""" + tables = generated_tables(config) + + if tables is None: + pytest.skip("Generated built-up candidate metadata is not available.") + + outputs, _, _ = tables + + try: + import ee + from src.analysis.orchestration.common import initialize_earth_engine + from src.analysis.built_up.build_candidates import image_metadata + except ImportError: + pytest.skip("Earth Engine integration dependencies are unavailable.") + + initialize_earth_engine(config["project"]["earth_engine_project"]) + grid = json.loads( + project_path( + config["inputs"]["grid_specification"] + ).read_text(encoding="utf-8") + ) + + for row in outputs.itertuples(index=False): + image = ee.Image(str(row.asset_id)) + metadata = image_metadata(str(row.asset_id)) + expected_bands = ( + INDEX_BANDS + if row.product_type == "indices" + else CANDIDATE_BANDS + ) + + assert metadata["band_names"] == expected_bands + assert metadata["crs"] == grid["crs"] + assert metadata["transform"] == [ + float(value) for value in grid["transform"] + ] + assert metadata["width"] == int(grid["width"]) + assert metadata["height"] == int(grid["height"]) + assert image.get("recipe_sha256").getInfo() == row.recipe_sha256 + + if row.product_type == "candidates": + values = image.select( + [f"built_{name}" for name in config["indices"]["selected"]] + ).reduceRegion( + reducer=ee.Reducer.minMax(), + geometry=image.geometry(), + crs=grid["crs"], + crsTransform=grid["transform"], + maxPixels=int(config["histogram"]["max_pixels"]), + tileScale=int(config["histogram"]["tile_scale"]), + ).getInfo() + + for name in config["indices"]["selected"]: + assert values[f"built_{name}_min"] == 0 + assert values[f"built_{name}_max"] == 1 From 407d1008708261d08dbd406f5a5ebc9e3fa2fe3a Mon Sep 17 00:00:00 2001 From: Omer-alt Date: Tue, 28 Jul 2026 07:03:03 +0100 Subject: [PATCH 5/5] Improve 2025 Landsat coverage and exclude IBI candidates Add Landsat 9 to the 2025 catalogue, rebuild the affected composite and candidate products, retain IBI as a continuous diagnostic index, and exclude IBI from Otsu candidate generation and Day 5 comparison. --- configs/built_up_candidates.yaml | 23 +- configs/landsat_catalog.yaml | 61 +- configs/landsat_catalog_2025_recovery.yaml | 85 + configs/orchestrate_sources.yaml | 6 +- .../built_up_candidates_version.json | 22 +- .../candidate_area_summary.csv | 41 +- .../dependency_manifest.csv | 4 +- .../earth_engine_tasks.csv | 20 +- .../built_up_candidates/histograms.json | 5204 +++++----- .../built_up_candidates/output_manifest.csv | 20 +- .../processing_recipe.json | 7 +- .../built_up_candidates/threshold_table.csv | 41 +- .../built_up_candidates_version.json | 35 + .../candidate_area_summary.csv | 26 + .../dependency_manifest.csv | 7 + .../earth_engine_tasks.csv | 11 + .../histograms.json | 9156 +++++++++++++++++ .../output_manifest.csv | 11 + .../processing_recipe.json | 44 + .../threshold_table.csv | 26 + .../landsat/candidate_window_details.csv | 1042 +- .../landsat/compositing_protocol.yaml | 33 +- .../landsat/epoch_quality_summary.csv | 2 +- .../metadata/landsat/monthly_availability.csv | 167 +- .../landsat/monthly_availability.parquet | Bin 9448 -> 9862 bytes data/metadata/landsat/scene_manifest_all.csv | 703 +- .../landsat/scene_manifest_all.parquet | Bin 57404 -> 62108 bytes .../landsat/selected_scene_manifest.csv | 30 +- .../candidate_window_details.csv | 49 + .../candidate_window_summary.csv | 49 + .../catalog_version.json | 7 + .../compositing_protocol.yaml | 46 + .../epoch_quality_summary.csv | 2 + .../monthly_availability.csv | 23 + .../monthly_availability.parquet | Bin 0 -> 7419 bytes .../scene_manifest_all.csv | 128 + .../scene_manifest_all.parquet | Bin 0 -> 27357 bytes .../selected_scene_manifest.csv | 20 + .../candidate_window_details.csv | 577 ++ .../candidate_window_summary.csv | 49 + .../catalog_version.json | 7 + .../compositing_protocol.yaml | 98 + .../epoch_quality_summary.csv | 9 + .../monthly_availability.csv | 87 + .../monthly_availability.parquet | Bin 0 -> 9448 bytes .../scene_manifest_all.csv | 522 + .../scene_manifest_all.parquet | Bin 0 -> 57404 bytes .../selected_scene_manifest.csv | 8 + .../auxiliary_source_manifest.csv | 2 +- .../orchestration/dependency_manifest.csv | 8 +- .../orchestration/earth_engine_tasks.csv | 22 +- .../landsat_composite_manifest.csv | 2 +- .../orchestration/landsat_epoch_quality.csv | 2 +- .../orchestration/orchestrate_version.json | 12 +- .../auxiliary_source_manifest.csv | 4 + .../dependency_manifest.csv | 8 + .../earth_engine_tasks.csv | 12 + .../ghsl_epoch_manifest.csv | 17 + .../ghsl_native_grid.json | 31 + .../grid_linkage.yaml | 32 + .../landsat_composite_manifest.csv | 6 + .../landsat_epoch_quality.csv | 6 + .../orchestrate_version.json | 27 + .../built_up_candidates/01_index_gallery.png | Bin 1234470 -> 1681858 bytes .../02_candidate_map_gallery.png | Bin 1153915 -> 1016024 bytes .../03_histogram_threshold_gallery.png | Bin 535391 -> 499328 bytes .../built_up_candidates_report.md | 18 +- .../01_scene_count_by_epoch_sensor.png | Bin 0 -> 42819 bytes .../02_monthly_scene_availability.png | Bin 0 -> 108432 bytes .../03_monthly_valid_coverage.png | Bin 0 -> 124077 bytes .../04_candidate_window_comparison.png | Bin 0 -> 164945 bytes .../05_selected_window_summary.png | Bin 0 -> 45905 bytes .../day2_landsat_report.md | 7 + .../01_selected_scene_counts.png | Bin 43075 -> 40818 bytes .../02_observation_depth_summary.png | Bin 81521 -> 79787 bytes reports/orchestration/orchestrate_report.md | 4 +- src/analysis/built_up/build_candidates.py | 98 +- src/analysis/built_up/finalize.py | 64 +- src/analysis/built_up/indices.py | 116 +- src/analysis/landsat/build_composites.py | 23 +- src/analysis/landsat/qa_masks.py | 43 +- tests/reference/built_up_candidates_v2.json | 35 + tests/reference/landsat_catalog_v2.json | 7 + tests/reference/orchestrate_sources_v2.json | 27 + tests/test_built_up_candidates.py | 62 +- tests/test_landsat_catalog.py | 183 +- tests/test_orchestrate_sources.py | 17 +- 87 files changed, 15697 insertions(+), 3706 deletions(-) create mode 100644 configs/landsat_catalog_2025_recovery.yaml create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/built_up_candidates_version.json create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/candidate_area_summary.csv create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/dependency_manifest.csv create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/earth_engine_tasks.csv create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/histograms.json create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/output_manifest.csv create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/processing_recipe.json create mode 100644 data/metadata/built_up_candidates_before_2025_recovery/threshold_table.csv create mode 100644 data/metadata/landsat_2025_recovery/candidate_window_details.csv create mode 100644 data/metadata/landsat_2025_recovery/candidate_window_summary.csv create mode 100644 data/metadata/landsat_2025_recovery/catalog_version.json create mode 100644 data/metadata/landsat_2025_recovery/compositing_protocol.yaml create mode 100644 data/metadata/landsat_2025_recovery/epoch_quality_summary.csv create mode 100644 data/metadata/landsat_2025_recovery/monthly_availability.csv create mode 100644 data/metadata/landsat_2025_recovery/monthly_availability.parquet create mode 100644 data/metadata/landsat_2025_recovery/scene_manifest_all.csv create mode 100644 data/metadata/landsat_2025_recovery/scene_manifest_all.parquet create mode 100644 data/metadata/landsat_2025_recovery/selected_scene_manifest.csv create mode 100644 data/metadata/landsat_before_2025_recovery/candidate_window_details.csv create mode 100644 data/metadata/landsat_before_2025_recovery/candidate_window_summary.csv create mode 100644 data/metadata/landsat_before_2025_recovery/catalog_version.json create mode 100644 data/metadata/landsat_before_2025_recovery/compositing_protocol.yaml create mode 100644 data/metadata/landsat_before_2025_recovery/epoch_quality_summary.csv create mode 100644 data/metadata/landsat_before_2025_recovery/monthly_availability.csv create mode 100644 data/metadata/landsat_before_2025_recovery/monthly_availability.parquet create mode 100644 data/metadata/landsat_before_2025_recovery/scene_manifest_all.csv create mode 100644 data/metadata/landsat_before_2025_recovery/scene_manifest_all.parquet create mode 100644 data/metadata/landsat_before_2025_recovery/selected_scene_manifest.csv create mode 100644 data/metadata/orchestration_before_2025_recovery/auxiliary_source_manifest.csv create mode 100644 data/metadata/orchestration_before_2025_recovery/dependency_manifest.csv create mode 100644 data/metadata/orchestration_before_2025_recovery/earth_engine_tasks.csv create mode 100644 data/metadata/orchestration_before_2025_recovery/ghsl_epoch_manifest.csv create mode 100644 data/metadata/orchestration_before_2025_recovery/ghsl_native_grid.json create mode 100644 data/metadata/orchestration_before_2025_recovery/grid_linkage.yaml create mode 100644 data/metadata/orchestration_before_2025_recovery/landsat_composite_manifest.csv create mode 100644 data/metadata/orchestration_before_2025_recovery/landsat_epoch_quality.csv create mode 100644 data/metadata/orchestration_before_2025_recovery/orchestrate_version.json create mode 100644 reports/landsat_2025_recovery/01_scene_count_by_epoch_sensor.png create mode 100644 reports/landsat_2025_recovery/02_monthly_scene_availability.png create mode 100644 reports/landsat_2025_recovery/03_monthly_valid_coverage.png create mode 100644 reports/landsat_2025_recovery/04_candidate_window_comparison.png create mode 100644 reports/landsat_2025_recovery/05_selected_window_summary.png create mode 100644 reports/landsat_2025_recovery/day2_landsat_report.md create mode 100644 tests/reference/built_up_candidates_v2.json create mode 100644 tests/reference/landsat_catalog_v2.json create mode 100644 tests/reference/orchestrate_sources_v2.json diff --git a/configs/built_up_candidates.yaml b/configs/built_up_candidates.yaml index 189e798..c16c5b9 100644 --- a/configs/built_up_candidates.yaml +++ b/configs/built_up_candidates.yaml @@ -1,4 +1,4 @@ -version: 1 +version: 2 project: earth_engine_project: urban-sprawl-ssa @@ -24,19 +24,29 @@ indices: denominator_epsilon: 0.000001 savi_l: 0.5 intermediate: [savi, mndwi] - selected: [ndbi, ibi, ibui, vbswir1_bi, ndbsui] + continuous: [ndbi, ibi, ibui, vbswir1_bi, ndbsui] + candidates: [ndbi, ibui, vbswir1_bi, ndbsui] + + excluded_from_candidates: + ibi: + reason: + "Unstable ratio denominator and extreme histogram values." + decision: + "Retain as a continuous diagnostic band but exclude from Otsu classification and Day 5 method comparison." classification: primary_strategy: epoch_specific_otsu compute_pooled_common_thresholds: false fail_all_exports_on_threshold_error: true + threshold_region: core application_region: context + minimum_valid_observations_for_threshold: 1 minimum_valid_observations_for_candidate: 1 + built_up_direction: ndbi: greater_than - ibi: greater_than ibui: greater_than vbswir1_bi: greater_than ndbsui: greater_than @@ -52,7 +62,10 @@ exports: asset_root: projects/urban-sprawl-ssa/assets/sprawlgp/v1 index_folder: indices candidate_folder: built_up_candidates - overwrite_existing_assets: false # true + + # Change temporarily to true only when replacing existing assets. + overwrite_existing_assets: false + max_pixels: 10000000 shard_size: 256 priority: 100 @@ -79,4 +92,4 @@ reports: generate_map_galleries: true reference: - version: tests/reference/built_up_candidates_v1.json + version: tests/reference/built_up_candidates_v2.json diff --git a/configs/landsat_catalog.yaml b/configs/landsat_catalog.yaml index c0b2210..1088b61 100644 --- a/configs/landsat_catalog.yaml +++ b/configs/landsat_catalog.yaml @@ -1,5 +1,5 @@ catalog: - version: 1 + version: 2 earth_engine_project: urban-sprawl-ssa epochs: [1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025] diagnostic_years_before: 1 @@ -14,23 +14,64 @@ study_area: collections: LT05: id: LANDSAT/LT05/C02/T1_L2 + spacecraft: LANDSAT_5 required_bands: [SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7] + LE07: id: LANDSAT/LE07/C02/T1_L2 + spacecraft: LANDSAT_7 required_bands: [SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7] + LC08: id: LANDSAT/LC08/C02/T1_L2 + spacecraft: LANDSAT_8 + required_bands: [SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7] + + LC09: + id: LANDSAT/LC09/C02/T1_L2 + spacecraft: LANDSAT_9 required_bands: [SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7] epoch_sensor_policy: - 1990: {primary: [LT05], supplemental: [], diagnostic_only: []} - 1995: {primary: [LT05], supplemental: [], diagnostic_only: []} - 2000: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} - 2005: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} - 2010: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} - 2015: {primary: [LC08], supplemental: [LE07], diagnostic_only: []} - 2020: {primary: [LC08], supplemental: [], diagnostic_only: [LE07]} - 2025: {primary: [LC08], supplemental: [], diagnostic_only: [LE07]} + 1990: + primary: [LT05] + supplemental: [] + diagnostic_only: [] + + 1995: + primary: [LT05] + supplemental: [] + diagnostic_only: [] + + 2000: + primary: [LT05] + supplemental: [LE07] + diagnostic_only: [] + + 2005: + primary: [LT05] + supplemental: [LE07] + diagnostic_only: [] + + 2010: + primary: [LT05] + supplemental: [LE07] + diagnostic_only: [] + + 2015: + primary: [LC08] + supplemental: [LE07] + diagnostic_only: [] + + 2020: + primary: [LC08] + supplemental: [] + diagnostic_only: [LE07] + + 2025: + primary: [LC08, LC09] + supplemental: [] + diagnostic_only: [LE07] quality_mask: mask_fill: true @@ -59,4 +100,4 @@ selection: outputs: directory: data/metadata/landsat report_directory: reports/day2 - reference_catalog: tests/reference/landsat_catalog_v1.json + reference_catalog: tests/reference/landsat_catalog_v2.json diff --git a/configs/landsat_catalog_2025_recovery.yaml b/configs/landsat_catalog_2025_recovery.yaml new file mode 100644 index 0000000..1d62922 --- /dev/null +++ b/configs/landsat_catalog_2025_recovery.yaml @@ -0,0 +1,85 @@ +# catalog: +# version: 2 +# earth_engine_project: urban-sprawl-ssa +# epochs: [1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025] +# diagnostic_years_before: 1 +# diagnostic_years_after: 1 + +catalog: + version: 2 + epochs: [2025] + earth_engine_project: urban-sprawl-ssa + diagnostic_years_before: 1 + diagnostic_years_after: 1 + +study_area: + core_boundary: data/processed/boundaries/yaounde_core.gpkg + context_boundary: data/processed/boundaries/yaounde_context_buffer.gpkg + boundary_report: data/metadata/boundary_report.json + grid_specification: data/metadata/grid_specification.json + +collections: + LT05: + id: LANDSAT/LT05/C02/T1_L2 + required_bands: [SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7] + LE07: + id: LANDSAT/LE07/C02/T1_L2 + required_bands: [SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7] + LC08: + id: LANDSAT/LC08/C02/T1_L2 + required_bands: [SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7] + LC09: + id: LANDSAT/LC09/C02/T1_L2 + spacecraft: LANDSAT_9 + required_bands: [SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7] + # - SR_B2 + # - SR_B3 + # - SR_B4 + # - SR_B5 + # - SR_B6 + # - SR_B7 + +epoch_sensor_policy: + 1990: {primary: [LT05], supplemental: [], diagnostic_only: []} + 1995: {primary: [LT05], supplemental: [], diagnostic_only: []} + 2000: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} + 2005: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} + 2010: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} + 2015: {primary: [LC08], supplemental: [LE07], diagnostic_only: []} + 2020: {primary: [LC08], supplemental: [], diagnostic_only: [LE07]} + 2025: {primary: [LC08, LC09], supplemental: [], diagnostic_only: [LE07]} + +quality_mask: + mask_fill: true + mask_dilated_cloud: true + mask_cirrus_landsat8: true + mask_cloud: true + mask_cloud_shadow: true + mask_snow: true + mask_radiometric_saturation: true + mask_water: false + +statistics: + max_pixels: 10000000 + tile_scale: 4 + +candidate_windows: + lengths_months: [3, 4, 6, 12] + allow_cross_year_windows: true + +selection: + minimum_coverage_one_observation_pct: 95.0 + preferred_coverage_three_observations_pct: 80.0 + select_shortest_acceptable_window: true + coverage_comparison_tolerance_pct: 0.5 + +recovery_quality: + fail_below_core_coverage_pct: 80 + preferred_core_coverage_pct: 95 + minimum_selected_scenes: 2 + +outputs: + directory: data/metadata/landsat_2025_recovery + report_directory: reports/landsat_2025_recovery + reference_catalog: + tests/reference/landsat_catalog_2025_recovery.json diff --git a/configs/orchestrate_sources.yaml b/configs/orchestrate_sources.yaml index dc9598c..4608039 100644 --- a/configs/orchestrate_sources.yaml +++ b/configs/orchestrate_sources.yaml @@ -1,4 +1,4 @@ -version: 1 +version: 2 project: earth_engine_project: urban-sprawl-ssa @@ -11,7 +11,7 @@ inputs: selected_scene_manifest: data/metadata/landsat/selected_scene_manifest.csv compositing_protocol: data/metadata/landsat/compositing_protocol.yaml catalog_version: data/metadata/landsat/catalog_version.json - catalog_reference: tests/reference/landsat_catalog_v1.json + catalog_reference: tests/reference/landsat_catalog_v2.json regions: core: data/processed/boundaries/yaounde_core.gpkg @@ -113,4 +113,4 @@ reports: source_alignment_figure: 03_source_alignment.png reference: - day3_sources: tests/reference/orchestrate_sources_v1.json + day3_sources: tests/reference/orchestrate_sources_v2.json diff --git a/data/metadata/built_up_candidates/built_up_candidates_version.json b/data/metadata/built_up_candidates/built_up_candidates_version.json index eac5fe9..c9dd526 100644 --- a/data/metadata/built_up_candidates/built_up_candidates_version.json +++ b/data/metadata/built_up_candidates/built_up_candidates_version.json @@ -1,8 +1,8 @@ { - "binary_candidate_layer_count": 25, - "candidate_area_summary_sha256": "0d9a0b41fe08b9383a433d477b873d117f414d134d877145c2cc6d3bad2655b9", + "binary_candidate_layer_count": 20, + "candidate_area_summary_sha256": "33e3593f918522a3fe47913da83ca453be6d97bd5c92f8f13d6af13c45ddbe33", "continuous_index_layer_count": 35, - "dependency_manifest_sha256": "51273195d56387ebeb655f844f66bbb239308ba6332ff6d01a079d65af97923b", + "dependency_manifest_sha256": "376fa132fac838180018e5fae1e562c7aae1fd78e3ed4d462bbbccffb67d0724", "earth_engine_assets": [ "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2005", "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2005", @@ -16,11 +16,11 @@ "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2025" ], "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", - "histograms_sha256": "a826db830346f1d7cc93f27a756c2a2a356a5abde85cd3d68f12340cf41c5fde", - "orchestration_version_sha256": "2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7", - "output_manifest_sha256": "6be6c2cd314e377a5f73b82377eede085b57ac9b287b5958300d8edc5a79054f", + "histograms_sha256": "8b8e74fc960997af7a7cf664d9c3438acdc44263f90ffd3303c8840298750c8a", + "orchestration_version_sha256": "993e0306c5cfb4c7c3a670cec755d0add6f23be90d90cd564774d82afe77a9f5", + "output_manifest_sha256": "052e4eba9499e1e39f73a5a20adb749dda41144ab3147e9804eee79d1274f041", "pipeline_stage": "built_up_candidates", - "pipeline_version": 1, + "pipeline_version": 2, "processed_epochs": [ 2005, 2010, @@ -28,8 +28,8 @@ 2020, 2025 ], - "processing_recipe_sha256": "186ae55cfc67cefc6a7109e676bb4be5f59dce00eefe1bdef1b6c3f7ad7e017c", - "stable_signature": "d0d437043b4c2113c7769c0bae912a754cd7a3383cac15715429962b54847650", - "task_manifest_sha256": "4111859d2af577a69e9718d6ef5b22db3dfa81b39863bdcc218b05b4ec0862c4", - "threshold_table_sha256": "cfded18084e387b8f227d3a42edc8868f5090e081fc4980ca4ae241714bc3308" + "processing_recipe_sha256": "8aec03a5265981f4a1c24fba28790734d878473a66e4b872ca206553518f2937", + "stable_signature": "cdf7a1f04c8c00641603c2e8092284ddfbf256ea5442e4e3f49543898f76cd55", + "task_manifest_sha256": "d746b0b46677c44218c0e8240c8d2e367099b994319cc0c1b2a55ef8fd4efb86", + "threshold_table_sha256": "e4b46d619a2df8665d0c7e7f50c63ca676e93cf63ece3a8f9baf8b5bfc9f7ba2" } diff --git a/data/metadata/built_up_candidates/candidate_area_summary.csv b/data/metadata/built_up_candidates/candidate_area_summary.csv index a009146..fb5d659 100644 --- a/data/metadata/built_up_candidates/candidate_area_summary.csv +++ b/data/metadata/built_up_candidates/candidate_area_summary.csv @@ -1,26 +1,21 @@ epoch,index_name,threshold,valid_area_ha,candidate_built_area_ha,candidate_nonbuilt_area_ha,candidate_built_fraction,change_from_previous_epoch_ha,quality_flag -2005,ibi,-10299.64614868164,1759.5323407033402,1759.4424419240434,0.089898779296875,0.9999489075720763,,PASS -2005,ibui,-0.17199289871104967,1759.5323407033402,395.5364693527641,1363.9958713505764,0.22479636219397692,,PASS -2005,ndbi,-0.22670226164325458,1759.5323407033402,464.72139249444695,1294.8109482088935,0.2641164255660588,,PASS -2005,ndbsui,-0.11922073596359206,1759.5323407033402,619.3211614388739,1140.2111792644664,0.35198055023604274,,PASS +2005,ibui,-0.17199289871104967,1759.5323407033402,395.536469352764,1363.9958713505764,0.2247963621939769,,PASS +2005,ndbi,-0.22670226164325458,1759.5323407033402,464.72139249444695,1294.8109482088937,0.2641164255660588,,PASS +2005,ndbsui,-0.11922073596359203,1759.5323407033402,619.3211614388739,1140.2111792644664,0.35198055023604274,,PASS 2005,vbswir1_bi,0.43758810491772593,1759.5323407033402,1252.1421899375766,507.3901507657638,0.7116335181637277,,PASS -2010,ibi,-2175.7214965820312,7832.69157057634,7832.242089613205,0.4494809631347656,0.999942614750104,6072.799647689162,PASS -2010,ibui,-0.046867263681580576,7832.69157057634,3259.9702543774893,4572.72131619885,0.4162005135787079,2864.4337850247252,PASS -2010,ndbi,-0.14843226221576813,7832.69157057634,3469.585859188568,4363.105711387772,0.44296214499523195,3004.864466694121,PASS +2010,ibui,-0.046867263681580576,7832.69157057634,3259.9702543774897,4572.721316198851,0.41620051357870796,2864.4337850247257,PASS +2010,ndbi,-0.14843226221576813,7832.69157057634,3469.585859188567,4363.105711387773,0.44296214499523184,3004.86446669412,PASS 2010,ndbsui,-0.0644524275233183,7832.69157057634,3792.080058765055,4040.611511811285,0.48413499045590896,3172.7588973261813,PASS -2010,vbswir1_bi,0.5897822438894713,7832.69157057634,4071.674364017932,3761.0172065584065,0.5198308049449127,2819.5321740803556,PASS -2015,ibi,-158813.41015625,17245.411515109507,17245.32161236903,0.08990274047851562,0.9999947868602382,9413.079522755823,PASS -2015,ibui,-0.05469426766836907,17245.501409018198,8361.773359873956,8883.728049144242,0.484866932051122,5101.803105496467,PASS -2015,ndbi,-0.14057678437402407,17245.501409018198,8723.521980151487,8521.979428866709,0.5058433369521915,5253.936120962919,PASS -2015,ndbsui,-0.06638607182457155,17242.17496173426,10142.589320067906,7099.5856416663555,0.5882430344534526,6350.50926130285,PASS -2015,vbswir1_bi,0.9912593632683961,17245.501409018198,159.85549701377718,17085.645912004416,0.009269402682034161,-3911.818867004155,PASS -2020,ibi,-62098.37890625,9031.964483822525,9031.87458758229,0.089896240234375,0.9999900468784619,-8213.44702478674,PASS -2020,ibui,-2.5869623225178474e-06,9032.054378750503,4213.829438646384,4818.22494010412,0.46654163736659504,-4147.943921227572,PASS -2020,ndbi,-0.11323624558439056,9032.054378750503,4553.462874475744,4478.591504274758,0.5041447586042624,-4170.059105675743,PASS -2020,ndbsui,-0.05470199763121585,9030.581828847871,5074.526916475854,3956.0549123720175,0.5619269071086271,-5068.062403592052,PASS -2020,vbswir1_bi,-46.59364318847656,9032.054378750503,9031.78469804982,0.26968070068359373,0.9999701418205234,8871.929201036042,PASS -2025,ibi,4715.6485595703125,301.66539193634634,0.17980009155273438,301.4855918447936,0.0005960249215152713,-9031.694787490736,PASS -2025,ibui,0.20291774036983648,301.66539193634634,196.61263192749024,105.0527600088561,0.6517573350574367,-4017.2168067188936,PASS -2025,ndbi,0.00017613643107065554,301.66539193634634,216.48100855712892,85.18438337921742,0.7176196353435466,-4336.981865918615,PASS -2025,ndbsui,-0.016547685522320013,301.66539193634634,228.95725765459844,72.70813428174786,0.758977541921382,-4845.569658821255,PASS -2025,vbswir1_bi,0.7266269589381058,301.66539193634634,116.30568940077839,185.35970253556795,0.3855453509407528,-8915.479008649041,PASS +2010,vbswir1_bi,0.5897822438894712,7832.69157057634,4071.674364017932,3761.0172065584065,0.5198308049449127,2819.5321740803556,PASS +2015,ibui,-0.05469426766836907,17245.501409018194,8361.773359873956,8883.72804914424,0.48486693205112213,5101.803105496467,PASS +2015,ndbi,-0.14057678437402407,17245.501409018194,8723.521980151489,8521.979428866709,0.5058433369521917,5253.936120962921,PASS +2015,ndbsui,-0.06638607182457154,17242.174961734258,10142.589320067906,7099.5856416663555,0.5882430344534528,6350.50926130285,PASS +2015,vbswir1_bi,0.9912593632683961,17245.501409018194,159.8554970137772,17085.64591200442,0.009269402682034165,-3911.818867004155,PASS +2020,ibui,-2.5869623225174138e-06,9032.0543787444,4213.829438646384,4818.224940098016,0.4665416373669103,-4147.943921227572,PASS +2020,ndbi,-0.11323624558439055,9032.0543787444,4553.462874475744,4478.591504268656,0.5041447586046031,-4170.059105675745,PASS +2020,ndbsui,-0.05470199763121586,9030.58182884177,5074.526916475853,3956.0549123659143,0.5619269071090066,-5068.062403592053,PASS +2020,vbswir1_bi,-46.59364318847656,9032.0543787444,9031.784698043715,0.26968070068359373,0.9999701418205231,8871.929201029938,PASS +2025,ibui,0.07807277769044582,28940.42080026561,17710.026717896348,11230.39408236927,0.6119477957878833,13496.197279249964,PASS +2025,ndbi,-0.08201249026915092,28940.42080026561,18048.42319399984,10891.997606265775,0.6236406622613516,13494.960319524096,PASS +2025,ndbsui,-0.042982521462942666,28940.42080026561,18900.96423463939,10039.456565626224,0.6530991503228564,13826.437318163535,PASS +2025,vbswir1_bi,0.640530787519159,28940.42080026561,11486.466046883426,17453.954753382186,0.39690045027880194,2454.681348839711,PASS diff --git a/data/metadata/built_up_candidates/dependency_manifest.csv b/data/metadata/built_up_candidates/dependency_manifest.csv index 4129862..5590663 100644 --- a/data/metadata/built_up_candidates/dependency_manifest.csv +++ b/data/metadata/built_up_candidates/dependency_manifest.csv @@ -1,7 +1,7 @@ dependency_name,dependency_path,observed_sha256,status grid_specification,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/grid_specification.json,2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525,PASS grid_reference,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/tests/reference/grid_v1.json,2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525,PASS -orchestration_version,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/orchestration/orchestrate_version.json,2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7,PASS -composite_manifest,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/orchestration/landsat_composite_manifest.csv,cfacd3e15552877993a1f75affceac849a88546be7de35cb18f762bb2bb04668,PASS +orchestration_version,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/orchestration/orchestrate_version.json,993e0306c5cfb4c7c3a670cec755d0add6f23be90d90cd564774d82afe77a9f5,PASS +composite_manifest,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/metadata/orchestration/landsat_composite_manifest.csv,0c3fb4829163cc503a1906c80214a0853554cf3cf7819a4c496caafb9486b779,PASS core_boundary,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/processed/boundaries/yaounde_core.gpkg,8c499234ee75f6d76803c192f62f0e2079d3a87c512bcb28031160b38e37b521,PASS context_boundary,/Users/omer/Documents/MIORPA/Pending.../SprawlGP/data/processed/boundaries/yaounde_context_buffer.gpkg,9e4e810eca96c0079a1495aa6e50ad2e016e50e0cb529a93aa5bf5c9b8ce62b9,PASS diff --git a/data/metadata/built_up_candidates/earth_engine_tasks.csv b/data/metadata/built_up_candidates/earth_engine_tasks.csv index fac62bc..1e1595e 100644 --- a/data/metadata/built_up_candidates/earth_engine_tasks.csv +++ b/data/metadata/built_up_candidates/earth_engine_tasks.csv @@ -1,11 +1,11 @@ task_id,state,description,asset_id,epoch,product_type,source_composite_asset,source_count_asset,recipe_sha256,error_message 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-204.09876796177454, -4.03077860517909, - 1.222275062608168, + 1.2222750626081678, 187.75053215026855, 375.0340270996094, 448, @@ -325,7 +325,7 @@ 2, 7, 1449.9254901960785, - 18098.219607843133, + 18098.219607843148, 8, 1, 0, @@ -362,29 +362,29 @@ -0.6169440856751109, -0.6020578179828562, -0.5854606457686985, - -0.5700386967300276, - -0.5543759677047226, + -0.5700386967300277, + -0.5543759677047225, -0.5381342307190645, - -0.5229858742872759, + -0.5229858742872758, -0.5079138711671408, -0.49181882510730063, - -0.4761879803752494, - -0.4608265555938555, + -0.47618798037524934, + -0.46082655559385566, -0.44513196568053803, - -0.429643952194697, - -0.4139289238612754, - -0.3984108939993734, + -0.42964395219469703, + -0.4139289238612755, + -0.39841089399937335, -0.38270571419814975, - -0.36745978270178237, - -0.35154272368815875, + -0.3674597827017824, + -0.3515427236881587, -0.33596418197003164, - -0.3206402854150301, - -0.30459619283173794, - -0.28913676442779856, - -0.27374165291135516, + -0.32064028541503, + -0.304596192831738, + -0.2891367644277986, + -0.2737416529113552, -0.25796263380118334, -0.24202470867501374, - -0.22652635249221384, + -0.22652635249221378, -0.21157622608672497, -0.19509294706649496, -0.17974669350324757, @@ -398,7 +398,7 @@ -0.055161701834493704, -0.03864900987181399, -0.024107849853144167, - -0.008337876985247933, + -0.008337876985247935, 0.007472131546982174, 0.023770564354506116, 0.03942772382153915, @@ -496,35 +496,35 @@ 45.760784313725495, 35.015686274509804, 31.305882352941175, - 33.78823529411764, + 33.78823529411765, 38, - 37.76470588235294, + 37.764705882352935, 71, 83, 85.9607843137255, 125.65098039215687, 136.04313725490198, 160.00392156862745, - 198.19215686274512, - 220.51372549019607, - 250.59607843137255, - 313.4274509803921, + 198.1921568627451, + 220.5137254901961, + 250.59607843137258, + 313.42745098039217, 324.435294117647, 427.99999999999994, - 481.89019607843136, + 481.8901960784314, 588.3294117647058, 642.6862745098039, - 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-VXAWOSQAA2DGU4BQLT62URDY,PASS,sprawlgp_indices_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2020,2020,indices,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,ce3a0e4382b6c1aeffcf40a9d60f40c8fd31379aeff879eb2bb34b0515d062b9,"savi,mndwi,ndbi,ibi,ibui,vbswir1_bi,ndbsui,valid_composite",7,0 -BXMFRK75UB4ZVSSPAF4NGTBZ,PASS,sprawlgp_candidates_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2020,2020,candidates,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,169c8502def22ec750d25d03e8cfc7ddf2bb0785eb5559e48bbd7ca0651e8cd3,"built_ndbi,built_ibi,built_ibui,built_vbswir1_bi,built_ndbsui,valid_ndbi,valid_ibi,valid_ibui,valid_vbswir1_bi,valid_ndbsui",0,5 -RVW2NOEBZNX45EHX2GLDBBPG,PASS,sprawlgp_indices_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2025,2025,indices,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,b316b3b4129f2b8848063d95e2f20d183cdfd2ed0dd8775781d010c547f9fbe3,"savi,mndwi,ndbi,ibi,ibui,vbswir1_bi,ndbsui,valid_composite",7,0 -IBG5RZ5UUZHGTIK674DQ2FGU,PASS,sprawlgp_candidates_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2025,2025,candidates,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,56542889ccd453b599a7476cf890828c5e74e69e7a683e3876bec133ab688fd0,"built_ndbi,built_ibi,built_ibui,built_vbswir1_bi,built_ndbsui,valid_ndbi,valid_ibi,valid_ibui,valid_vbswir1_bi,valid_ndbsui",0,5 +B5HYJKTX7KC6YALAASTQJPWJ,PASS,sprawlgp_indices_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2005,2005,indices,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,355ae141e747625892abd76baafcc87df4dc2c39c117c0a946650c238dcfe7f0,"savi,mndwi,ndbi,ibi,ibui,vbswir1_bi,ndbsui,valid_composite",7,0 +TMB24FD3G25FKVB42VHH6ZPG,PASS,sprawlgp_candidates_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2005,2005,candidates,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,5f05e51e9bc76b1cd725c53659e1ffff77e1a26154ff7289e16ab9ae77828aa5,"built_ndbi,built_ibui,built_vbswir1_bi,built_ndbsui,valid_ndbi,valid_ibui,valid_vbswir1_bi,valid_ndbsui",0,4 +ICDGUAUZSMUAMAWGUNLUA4AZ,PASS,sprawlgp_indices_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2010,2010,indices,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,5ae1c6462109935b83dbe09bae66c571a911515b2a6cac23cdc3da931d1e0a8e,"savi,mndwi,ndbi,ibi,ibui,vbswir1_bi,ndbsui,valid_composite",7,0 +3BUTBV7HEUSLGQGUGSKQWMGG,PASS,sprawlgp_candidates_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2010,2010,candidates,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,5431c9db978ead94461f144e6a2ebdb2c732e69e0e9d335a4b1a18d6e9d4ab71,"built_ndbi,built_ibui,built_vbswir1_bi,built_ndbsui,valid_ndbi,valid_ibui,valid_vbswir1_bi,valid_ndbsui",0,4 +NXJ5IHIDGAFHPGJSGESAHD5I,PASS,sprawlgp_indices_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2015,2015,indices,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,512bb9f4f4138c6b0e17b9730ff1a0e6cba120837e7e1347977daed369487bb9,"savi,mndwi,ndbi,ibi,ibui,vbswir1_bi,ndbsui,valid_composite",7,0 +FN4LPRO2YRI3TMLOO53SSWGF,PASS,sprawlgp_candidates_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2015,2015,candidates,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,5d291187c276f59b421a43e00b22b8301002a8707f9c1af608f4ec1f88a8df32,"built_ndbi,built_ibui,built_vbswir1_bi,built_ndbsui,valid_ndbi,valid_ibui,valid_vbswir1_bi,valid_ndbsui",0,4 +6V5EDBZDBAFRSOBF6KFONIAJ,PASS,sprawlgp_indices_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2020,2020,indices,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,c74f827836119421fb5cc5c7da8868f8a127aea9abf537b7d2117072941d42be,"savi,mndwi,ndbi,ibi,ibui,vbswir1_bi,ndbsui,valid_composite",7,0 +Z4XIVFTJ4GBE4EDEE3DRVKER,PASS,sprawlgp_candidates_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2020,2020,candidates,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,2b429afc9af5a2bf28e151f3b9c12907f320eba8ce650edfaa763e743bfd5cad,"built_ndbi,built_ibui,built_vbswir1_bi,built_ndbsui,valid_ndbi,valid_ibui,valid_vbswir1_bi,valid_ndbsui",0,4 +FVXSYVWC6EOGECFUFHKEZIJG,PASS,sprawlgp_indices_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2025,2025,indices,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,72e1a12a4343287969c399a54257604f4d0d5eccf9420b5e14cf1127f1c1f4fd,"savi,mndwi,ndbi,ibi,ibui,vbswir1_bi,ndbsui,valid_composite",7,0 +3FTEVVHXDEJRVUZDQD2VJD4Q,PASS,sprawlgp_candidates_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2025,2025,candidates,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,423c404193f8b2dbe52990185d9e1e7e6a7fb7b3f28b4f7d0ca019a6a9598f88,"built_ndbi,built_ibui,built_vbswir1_bi,built_ndbsui,valid_ndbi,valid_ibui,valid_vbswir1_bi,valid_ndbsui",0,4 diff --git a/data/metadata/built_up_candidates/processing_recipe.json b/data/metadata/built_up_candidates/processing_recipe.json index 8ced8d8..d0ee7e2 100644 --- a/data/metadata/built_up_candidates/processing_recipe.json +++ b/data/metadata/built_up_candidates/processing_recipe.json @@ -2,18 +2,15 @@ "application_region": "context", "candidate_band_schema": [ "built_ndbi", - "built_ibi", "built_ibui", "built_vbswir1_bi", "built_ndbsui", "valid_ndbi", - "valid_ibi", "valid_ibui", "valid_vbswir1_bi", "valid_ndbsui" ], "classification_directions": { - "ibi": "greater_than", "ibui": "greater_than", "ndbi": "greater_than", "ndbsui": "greater_than", @@ -35,10 +32,10 @@ "minimum_valid_observations_for_candidate": 1, "minimum_valid_observations_for_threshold": 1, "pipeline_stage": "built_up_candidates", - "pipeline_version": 1, + "pipeline_version": 2, "pooled_thresholds_computed": false, "savi_l": 0.5, - "source_orchestration_sha256": "2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7", + "source_orchestration_sha256": "993e0306c5cfb4c7c3a670cec755d0add6f23be90d90cd564774d82afe77a9f5", "threshold_region": "core", "threshold_strategy": "epoch_specific_otsu" } diff --git a/data/metadata/built_up_candidates/threshold_table.csv b/data/metadata/built_up_candidates/threshold_table.csv index 64e3dc7..d0e48a8 100644 --- a/data/metadata/built_up_candidates/threshold_table.csv +++ b/data/metadata/built_up_candidates/threshold_table.csv @@ -1,26 +1,21 @@ epoch,index_name,threshold_strategy,threshold_value,classification_direction,histogram_bucket_count,histogram_valid_pixel_count,histogram_min,histogram_max,histogram_mean,threshold_region,minimum_observation_count,status,failure_reason,source_composite_asset,source_count_asset,formula_version,grid_version,threshold_record_sha256,candidate_valid_pixel_count_core,candidate_built_pixel_count_core,candidate_nonbuilt_pixel_count_core,candidate_built_fraction_core,candidate_built_area_ha_core -2005,ibi,epoch_specific_otsu,-10299.64614868164,greater_than,11,19571,-19936.8359375,892.3379516601562,-0.1754082629900096,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,e3a45ce0902bdcf88858d9b57dfe97f78952ab6a00d7bbffa488612f58e7236b,19571,19570,1,0.9999489075720763,1759.4424419240434 -2005,ibui,epoch_specific_otsu,-0.17199289871104967,greater_than,107,19571,-0.9809713959693909,1.1791107654571533,-0.29333837895506926,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,ba8322a5509f9c5390055ea4bbf4a805c911f3e3b9facbd96d580b62d5f4016c,19571,4400,15171,0.22479636219397692,395.5364693527641 -2005,ndbi,epoch_specific_otsu,-0.22670226164325458,greater_than,119,19571,-0.6225432455539703,0.5710815191268921,-0.29049071913686947,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,51d76fae5c6fa6347b439c6e22324c20088a46c1e05d3927c402478f62e7c2c8,19571,5169,14402,0.2641164255660588,464.72139249444695 -2005,ndbsui,epoch_specific_otsu,-0.11922073596359206,greater_than,184,19571,-0.3263508081436157,0.07120613008737564,-0.13532372346826088,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,2f583dc2547ff5a29a7733f21a9f155e889d9f5239c80309b6b7bc24c49d884f,19571,6889,12682,0.35198055023604274,619.3211614388739 +2005,ibui,epoch_specific_otsu,-0.17199289871104967,greater_than,107,19571,-0.9809713959693909,1.1791107654571533,-0.29333837895506926,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,722f18407cdb9119022e51f6f07242d2de096bfc8e3e2723a9ed7f2afccaa60a,19571,4400,15171,0.2247963621939769,395.536469352764 +2005,ndbi,epoch_specific_otsu,-0.22670226164325458,greater_than,119,19571,-0.6225432455539703,0.5710815191268921,-0.2904907191368694,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,718a5904056d5066297a1b3ac55aba81cdac63c7285818de63e504568bd00818,19571,5169,14402,0.2641164255660588,464.72139249444695 +2005,ndbsui,epoch_specific_otsu,-0.11922073596359203,greater_than,184,19571,-0.3263508081436157,0.07120613008737564,-0.13532372346826088,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,9b7555507762ea0b4f34ee6cfc87d74f1860e49512e641364966c509cbbc5292,19571,6889,12682,0.35198055023604274,619.3211614388739 2005,vbswir1_bi,epoch_specific_otsu,0.43758810491772593,greater_than,131,19571,-0.37698259949684143,0.8559102848016427,0.5116152952923255,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005,1,1,bd675da7753f5476dfcbc44f3cc7decaa261c40016d46bcf54eaa8ec8c8e7712,19571,13927,5644,0.7116335181637277,1252.1421899375766 -2010,ibi,epoch_specific_otsu,-2175.7214965820312,greater_than,38,87129,-6345.56298828125,6269.611328125,-0.26802683399498156,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,1,0c91ffb78aa114bbbadce3d6fe15abec97f1697449e64339f7db10016cf8565d,87129,87124,5,0.999942614750104,7832.242089613205 -2010,ibui,epoch_specific_otsu,-0.046867263681580576,greater_than,108,87129,-0.8496665954589844,1.2210384607315063,-0.07956944434392232,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,1,feac1fd638794f782b2cbfd388b47660af533d1f37d0eec2f9a6c0953d5e25f9,87129,36264,50865,0.4162005135787079,3259.9702543774893 -2010,ndbi,epoch_specific_otsu,-0.14843226221576813,greater_than,113,87129,-0.5156252980232239,0.6009668707847595,-0.16093610638982075,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,1,b0fc0332efc5bd8c59751cb1763aa84efc2e23f14bde90282f4f3c27d1720575,87129,38595,48533,0.44296214499523195,3469.585859188568 +2010,ibui,epoch_specific_otsu,-0.046867263681580576,greater_than,108,87129,-0.8496665954589844,1.2210384607315063,-0.07956944434392232,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,1,70b2602fdd4a0d3208823fd2e520f7eb2bec7211dd4b3f5395615a23ed9663f9,87129,36264,50865,0.41620051357870796,3259.9702543774897 +2010,ndbi,epoch_specific_otsu,-0.14843226221576813,greater_than,113,87129,-0.5156252980232239,0.6009668707847595,-0.16093610638982075,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,1,b28d87bab9a3cf015d38226f1c6b7dec103a4be5eed75fd9cd56d097c3505e75,87129,38595,48533,0.44296214499523184,3469.585859188567 2010,ndbsui,epoch_specific_otsu,-0.0644524275233183,greater_than,157,87129,-0.222590371966362,0.1653548628091812,-0.06625115550341769,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,1,82f7433272b6d3c749a71fec10f6f7c68386284b3986f515f3edfe18aea6491b,87129,42183,44946,0.48413499045590896,3792.080058765055 -2010,vbswir1_bi,epoch_specific_otsu,0.5897822438894713,greater_than,169,87129,-0.005673715379089117,0.8598147630691528,0.5921138388028433,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010,1,1,dafb16482112b00609be401396ab3b3d200c935226a76541700bf329e472e1b2,87129,45292,41837,0.5198308049449127,4071.674364017932 -2015,ibi,epoch_specific_otsu,-158813.41015625,greater_than,24,191830,-243117.78125,102674.4296875,-1.5545441600219236,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,1,1,13a8392004f32ff67dc9851d2ed7fd3ff02fe45507eb96c41e1a244f61a3c57c,191830,191829,1,0.9999947868602382,17245.32161236903 -2015,ibui,epoch_specific_otsu,-0.05469426766836907,greater_than,198,191831,-0.8343394696712494,0.9849635362625122,-0.06163606997472729,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,1,1,9e471c95da954262012d24880dd1bfd162e6530eb40f73f998a03f756f19aa8f,191831,93014,98817,0.484866932051122,8361.773359873956 -2015,ndbi,epoch_specific_otsu,-0.14057678437402407,greater_than,219,191831,-0.5552840828895569,0.3871047794818878,-0.13906290527156454,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,1,1,0ef2beaefedfecb452f6cc608b4ce0ea7d227df5c4ea20ad978f30efb2a2ae5c,191831,97038,94793,0.5058433369521915,8723.521980151487 -2015,ndbsui,epoch_specific_otsu,-0.06638607182457155,greater_than,107,191794,-0.3034968674182892,0.2405441701412201,-0.05587855893048606,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,1,1,00ce3648604fde48ecb89fd2968de1d9bb74b8648e786c227bce07103c46d940,191794,112823,78971,0.5882430344534526,10142.589320067906 -2015,vbswir1_bi,epoch_specific_otsu,0.9912593632683961,greater_than,42,191831,-12.038434028625488,12.852977752685547,0.6630060926964174,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,1,1,8e521ea00c7b8a094c34d218f4aa5162d0864e03af281dba2b9223c95bd38123,191831,1778,190053,0.009269402682034161,159.85549701377718 -2020,ibi,epoch_specific_otsu,-62098.37890625,greater_than,23,100467,-93095.6953125,47191.27734375,-0.07025936097251015,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,237df6f87cd420fe3ce7ce0add13f44ac3094f1034a146675a078ed2ed89ba64,100467,100466,1,0.9999900468784619,9031.87458758229 -2020,ibui,epoch_specific_otsu,-2.5869623225178474e-06,greater_than,117,100468,-0.8357632756233215,1.6545032262802124,-0.019692658001625507,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,7efd30d4f3424c0339bbaffbec9a78f991f82faa4d80079c8e4149843b5d8340,100468,46874,53594,0.46654163736659504,4213.829438646384 -2020,ndbi,epoch_specific_otsu,-0.11323624558439056,greater_than,232,100468,-0.5789747635523478,0.3631986081600189,-0.11157664314582882,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,f723060b97492d9e063e944efc01b0bd68e763d649e38195b8ac56647ee4884d,100468,50651,49816,0.5041447586042624,4553.462874475744 -2020,ndbsui,epoch_specific_otsu,-0.05470199763121585,greater_than,101,100451,-0.2717781662940979,0.2661985158920288,-0.04701910650878555,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,691c363ac6eb4dc3d3d4de68f8c9f7f523a08894129a67cff2f836aa8e3ffb57,100451,56447,44004,0.5619269071086271,5074.526916475854 -2020,vbswir1_bi,epoch_specific_otsu,-46.59364318847656,greater_than,35,100468,-137.80709838867188,52.92961502075195,0.6423029465574327,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,6ec82d3a87ecae4ef089c1f2c67e619f2a2c11d20f760adf9ae88be4991e7993,100468,100465,3,0.9999701418205234,9031.78469804982 -2025,ibi,epoch_specific_otsu,4715.6485595703125,greater_than,19,3356,-8190.55322265625,12662.2470703125,4.589194603108382,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,b32969ece9614f4b364176fa210a018c0f7f0312cb03118ba47ecba08834e861,3356,2,3354,0.0005960249215152713,0.17980009155273438 -2025,ibui,epoch_specific_otsu,0.20291774036983648,greater_than,151,3356,-0.5897546410560608,0.6892502903938293,0.25187415976471167,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,899b9d37167ef355411eb63cfc3c38e5845c4257aecebe02e5a278623e0a5709,3356,2187,1169,0.6517573350574367,196.61263192749024 -2025,ndbi,epoch_specific_otsu,0.00017613643107065554,greater_than,156,3356,-0.3707534372806549,0.24833732843399048,0.0407387807663998,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,65015f1401464e61c0559e5b1a57708e07a609d367c3c7f9b1ca7c586737f34c,3356,2408,948,0.7176196353435466,216.48100855712892 -2025,ndbsui,epoch_specific_otsu,-0.016547685522320013,greater_than,218,3356,-0.16105851531028748,0.0792907103896141,-6.048664004019315e-05,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,743d535c0b8a680504a21a8d11588c4556f2d82edb6339ed413109ce4ed0661e,3356,2547,809,0.758977541921382,228.95725765459844 -2025,vbswir1_bi,epoch_specific_otsu,0.7266269589381058,greater_than,133,3356,0.25051406025886536,1.5186182260513306,0.6982104994792777,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,c587704fcbf56d99cf8249c2f05f1417b825863d3c89549dfe600f5ab119291f,3356,1294,2062,0.3855453509407528,116.30568940077839 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+2015,vbswir1_bi,epoch_specific_otsu,0.9912593632683961,greater_than,42,191831,-12.038434028625488,12.852977752685547,0.6630060926964167,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015,1,1,99c338d5aa39ab8ea947acd3672357cd8bff1882a775e1eb2d5420fdab9063bc,191831,1778,190053,0.009269402682034165,159.8554970137772 +2020,ibui,epoch_specific_otsu,-2.5869623225174138e-06,greater_than,117,100468,-0.8357632756233215,1.6545032262802124,-0.019692658001625507,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,11d98d686fa78bec4d8bc1640e9ca37d610004ca530a8fb16222776ac7aaccd7,100468,46874,53594,0.4665416373669103,4213.829438646384 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+2025,ndbi,epoch_specific_otsu,-0.08201249026915092,greater_than,210,321917,-0.5066338777542114,0.33059027791023254,-0.0465137615134975,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,c1553a2a1366b16327d4ddb7d1dcb622f5f2cbbbd09c3181e64c474ab6ac4e8e,321917,200764,121153,0.6236406622613516,18048.42319399984 +2025,ndbsui,epoch_specific_otsu,-0.042982521462942666,greater_than,171,321917,-0.21257087588310242,0.1350911557674408,-0.02680516695406976,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,6ce8fc08477a9b6aa669c623a107593e75d89c9484ec7a891f16f1fe2e485880,321917,210247,111670,0.6530991503228564,18900.96423463939 +2025,vbswir1_bi,epoch_specific_otsu,0.640530787519159,greater_than,152,321917,-0.37878286838531494,0.7998712360858917,0.6309156975772431,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,c14a57d24796eaafe688ff073f8bacd98a14f5282bc06952833386d485d71e41,321917,127767,194151,0.39690045027880194,11486.466046883426 diff --git a/data/metadata/built_up_candidates_before_2025_recovery/built_up_candidates_version.json b/data/metadata/built_up_candidates_before_2025_recovery/built_up_candidates_version.json new file mode 100644 index 0000000..eac5fe9 --- /dev/null +++ b/data/metadata/built_up_candidates_before_2025_recovery/built_up_candidates_version.json @@ -0,0 +1,35 @@ +{ + "binary_candidate_layer_count": 25, + "candidate_area_summary_sha256": "0d9a0b41fe08b9383a433d477b873d117f414d134d877145c2cc6d3bad2655b9", + "continuous_index_layer_count": 35, + "dependency_manifest_sha256": "51273195d56387ebeb655f844f66bbb239308ba6332ff6d01a079d65af97923b", + "earth_engine_assets": [ + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2025" + ], + "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", + "histograms_sha256": "a826db830346f1d7cc93f27a756c2a2a356a5abde85cd3d68f12340cf41c5fde", + "orchestration_version_sha256": "2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7", + "output_manifest_sha256": "6be6c2cd314e377a5f73b82377eede085b57ac9b287b5958300d8edc5a79054f", + "pipeline_stage": "built_up_candidates", + "pipeline_version": 1, + "processed_epochs": [ + 2005, + 2010, + 2015, + 2020, + 2025 + ], + "processing_recipe_sha256": "186ae55cfc67cefc6a7109e676bb4be5f59dce00eefe1bdef1b6c3f7ad7e017c", + "stable_signature": "d0d437043b4c2113c7769c0bae912a754cd7a3383cac15715429962b54847650", + "task_manifest_sha256": "4111859d2af577a69e9718d6ef5b22db3dfa81b39863bdcc218b05b4ec0862c4", + "threshold_table_sha256": "cfded18084e387b8f227d3a42edc8868f5090e081fc4980ca4ae241714bc3308" +} diff --git a/data/metadata/built_up_candidates_before_2025_recovery/candidate_area_summary.csv b/data/metadata/built_up_candidates_before_2025_recovery/candidate_area_summary.csv new file mode 100644 index 0000000..a009146 --- /dev/null +++ b/data/metadata/built_up_candidates_before_2025_recovery/candidate_area_summary.csv @@ -0,0 +1,26 @@ +epoch,index_name,threshold,valid_area_ha,candidate_built_area_ha,candidate_nonbuilt_area_ha,candidate_built_fraction,change_from_previous_epoch_ha,quality_flag +2005,ibi,-10299.64614868164,1759.5323407033402,1759.4424419240434,0.089898779296875,0.9999489075720763,,PASS +2005,ibui,-0.17199289871104967,1759.5323407033402,395.5364693527641,1363.9958713505764,0.22479636219397692,,PASS +2005,ndbi,-0.22670226164325458,1759.5323407033402,464.72139249444695,1294.8109482088935,0.2641164255660588,,PASS +2005,ndbsui,-0.11922073596359206,1759.5323407033402,619.3211614388739,1140.2111792644664,0.35198055023604274,,PASS +2005,vbswir1_bi,0.43758810491772593,1759.5323407033402,1252.1421899375766,507.3901507657638,0.7116335181637277,,PASS +2010,ibi,-2175.7214965820312,7832.69157057634,7832.242089613205,0.4494809631347656,0.999942614750104,6072.799647689162,PASS +2010,ibui,-0.046867263681580576,7832.69157057634,3259.9702543774893,4572.72131619885,0.4162005135787079,2864.4337850247252,PASS +2010,ndbi,-0.14843226221576813,7832.69157057634,3469.585859188568,4363.105711387772,0.44296214499523195,3004.864466694121,PASS +2010,ndbsui,-0.0644524275233183,7832.69157057634,3792.080058765055,4040.611511811285,0.48413499045590896,3172.7588973261813,PASS +2010,vbswir1_bi,0.5897822438894713,7832.69157057634,4071.674364017932,3761.0172065584065,0.5198308049449127,2819.5321740803556,PASS +2015,ibi,-158813.41015625,17245.411515109507,17245.32161236903,0.08990274047851562,0.9999947868602382,9413.079522755823,PASS +2015,ibui,-0.05469426766836907,17245.501409018198,8361.773359873956,8883.728049144242,0.484866932051122,5101.803105496467,PASS +2015,ndbi,-0.14057678437402407,17245.501409018198,8723.521980151487,8521.979428866709,0.5058433369521915,5253.936120962919,PASS +2015,ndbsui,-0.06638607182457155,17242.17496173426,10142.589320067906,7099.5856416663555,0.5882430344534526,6350.50926130285,PASS +2015,vbswir1_bi,0.9912593632683961,17245.501409018198,159.85549701377718,17085.645912004416,0.009269402682034161,-3911.818867004155,PASS +2020,ibi,-62098.37890625,9031.964483822525,9031.87458758229,0.089896240234375,0.9999900468784619,-8213.44702478674,PASS +2020,ibui,-2.5869623225178474e-06,9032.054378750503,4213.829438646384,4818.22494010412,0.46654163736659504,-4147.943921227572,PASS +2020,ndbi,-0.11323624558439056,9032.054378750503,4553.462874475744,4478.591504274758,0.5041447586042624,-4170.059105675743,PASS +2020,ndbsui,-0.05470199763121585,9030.581828847871,5074.526916475854,3956.0549123720175,0.5619269071086271,-5068.062403592052,PASS 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b/data/metadata/built_up_candidates_before_2025_recovery/processing_recipe.json new file mode 100644 index 0000000..8ced8d8 --- /dev/null +++ b/data/metadata/built_up_candidates_before_2025_recovery/processing_recipe.json @@ -0,0 +1,44 @@ +{ + "application_region": "context", + "candidate_band_schema": [ + "built_ndbi", + "built_ibi", + "built_ibui", + "built_vbswir1_bi", + "built_ndbsui", + "valid_ndbi", + "valid_ibi", + "valid_ibui", + "valid_vbswir1_bi", + "valid_ndbsui" + ], + "classification_directions": { + "ibi": "greater_than", + "ibui": "greater_than", + "ndbi": "greater_than", + "ndbsui": "greater_than", + "vbswir1_bi": "greater_than" + }, + "denominator_epsilon": 1e-06, + "formula_version": 1, + "grid_sha256": "70534f7aa78e3ac0bd0acdb7f87c8d22a025612b568d908d1e3b761d28ffa43b", + "index_band_schema": [ + "savi", + "mndwi", + "ndbi", + "ibi", + "ibui", + "vbswir1_bi", + "ndbsui", + "valid_composite" + ], + "minimum_valid_observations_for_candidate": 1, + "minimum_valid_observations_for_threshold": 1, + "pipeline_stage": "built_up_candidates", + "pipeline_version": 1, + "pooled_thresholds_computed": false, + "savi_l": 0.5, + "source_orchestration_sha256": "2716650fb498ea2cc5f7597c467d93c17d9482cb5de3f2249a02ae5182d7c6f7", + "threshold_region": "core", + "threshold_strategy": "epoch_specific_otsu" +} diff --git a/data/metadata/built_up_candidates_before_2025_recovery/threshold_table.csv b/data/metadata/built_up_candidates_before_2025_recovery/threshold_table.csv new file mode 100644 index 0000000..64e3dc7 --- /dev/null +++ b/data/metadata/built_up_candidates_before_2025_recovery/threshold_table.csv @@ -0,0 +1,26 @@ 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+2020,ndbi,epoch_specific_otsu,-0.11323624558439056,greater_than,232,100468,-0.5789747635523478,0.3631986081600189,-0.11157664314582882,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,f723060b97492d9e063e944efc01b0bd68e763d649e38195b8ac56647ee4884d,100468,50651,49816,0.5041447586042624,4553.462874475744 +2020,ndbsui,epoch_specific_otsu,-0.05470199763121585,greater_than,101,100451,-0.2717781662940979,0.2661985158920288,-0.04701910650878555,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,691c363ac6eb4dc3d3d4de68f8c9f7f523a08894129a67cff2f836aa8e3ffb57,100451,56447,44004,0.5619269071086271,5074.526916475854 +2020,vbswir1_bi,epoch_specific_otsu,-46.59364318847656,greater_than,35,100468,-137.80709838867188,52.92961502075195,0.6423029465574327,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020,1,1,6ec82d3a87ecae4ef089c1f2c67e619f2a2c11d20f760adf9ae88be4991e7993,100468,100465,3,0.9999701418205234,9031.78469804982 +2025,ibi,epoch_specific_otsu,4715.6485595703125,greater_than,19,3356,-8190.55322265625,12662.2470703125,4.589194603108382,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,b32969ece9614f4b364176fa210a018c0f7f0312cb03118ba47ecba08834e861,3356,2,3354,0.0005960249215152713,0.17980009155273438 +2025,ibui,epoch_specific_otsu,0.20291774036983648,greater_than,151,3356,-0.5897546410560608,0.6892502903938293,0.25187415976471167,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,899b9d37167ef355411eb63cfc3c38e5845c4257aecebe02e5a278623e0a5709,3356,2187,1169,0.6517573350574367,196.61263192749024 +2025,ndbi,epoch_specific_otsu,0.00017613643107065554,greater_than,156,3356,-0.3707534372806549,0.24833732843399048,0.0407387807663998,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,65015f1401464e61c0559e5b1a57708e07a609d367c3c7f9b1ca7c586737f34c,3356,2408,948,0.7176196353435466,216.48100855712892 +2025,ndbsui,epoch_specific_otsu,-0.016547685522320013,greater_than,218,3356,-0.16105851531028748,0.0792907103896141,-6.048664004019315e-05,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,743d535c0b8a680504a21a8d11588c4556f2d82edb6339ed413109ce4ed0661e,3356,2547,809,0.758977541921382,228.95725765459844 +2025,vbswir1_bi,epoch_specific_otsu,0.7266269589381058,greater_than,133,3356,0.25051406025886536,1.5186182260513306,0.6982104994792777,core,1,PASS,,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025,projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025,1,1,c587704fcbf56d99cf8249c2f05f1417b825863d3c89549dfe600f5ab119291f,3356,1294,2062,0.3855453509407528,116.30568940077839 diff --git a/data/metadata/landsat/candidate_window_details.csv b/data/metadata/landsat/candidate_window_details.csv index e0761a6..4c889d5 100644 --- a/data/metadata/landsat/candidate_window_details.csv +++ b/data/metadata/landsat/candidate_window_details.csv @@ -1,577 +1,577 @@ epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_keys,scene_count,coverage_at_least_1_core_pct,coverage_at_least_3_core_pct,supplemental_sensor_used 1990,Jan-Mar_3m,3,1990-01-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False -1995,Jan-Mar_3m,3,1995-01-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True -2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False -2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",4,20.214509718071028,7.108844196338966,True -2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False -2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True -2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_only,LC08,3,99.95200756228057,55.84014934025937,False -2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",12,99.9983916925276,77.91912842227305,True -2020,Jan-Mar_3m,3,2020-01-01,2020-04-01,primary_only,LC08,12,99.9999889269216,92.4627230944063,False -2025,Jan-Mar_3m,3,2025-01-01,2025-04-01,primary_only,LC08,9,99.97331931309324,82.78798056832599,False +1990,Nov-Jan_3m,3,1989-11-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1990,Oct-Dec_3m,3,1990-10-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1990,Apr-Jun_3m,3,1990-04-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1990,Oct-Mar_6m,6,1989-10-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1990,May-Oct_6m,6,1990-05-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1990,Jun-May_12m,12,1989-06-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1990,Dec-Nov_12m,12,1989-12-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1990,Nov-Oct_12m,12,1989-11-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1990,Oct-Sep_12m,12,1989-10-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1990,Aug-Jul_12m,12,1989-08-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False 1990,Feb-Apr_3m,3,1990-02-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False -1995,Feb-Apr_3m,3,1995-02-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False -2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False -2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True -2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False -2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True -2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False -2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True -2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_only,LC08,3,66.60550893983303,30.373115138961243,False -2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",11,96.61199834332788,52.721554256203554,True -2020,Feb-Apr_3m,3,2020-02-01,2020-05-01,primary_only,LC08,10,99.99197718965614,83.1191679886652,False -2025,Feb-Apr_3m,3,2025-02-01,2025-05-01,primary_only,LC08,6,99.20631169511765,67.12832615980658,False +1990,Jul-Jun_12m,12,1989-07-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1990,Nov-Apr_6m,6,1989-11-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1990,May-Apr_12m,12,1989-05-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1990,Apr-Mar_12m,12,1989-04-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1990,Mar-Feb_12m,12,1989-03-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False 1990,Mar-May_3m,3,1990-03-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False -1995,Mar-May_3m,3,1995-03-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353795,44.263437174874525,True -2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False -2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True -2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False -2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True -2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False -2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108565,69.94945631243047,True -2020,Mar-May_3m,3,2020-03-01,2020-06-01,primary_only,LC08,10,97.53274785282866,54.14122230543774,False -2025,Mar-May_3m,3,2025-03-01,2025-06-01,primary_only,LC08,4,95.98010283732056,60.34000438856134,False -1990,Apr-Jun_3m,3,1990-04-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False -1995,Apr-Jun_3m,3,1995-04-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True -2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False -2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",1,8.681343174190491,0.0,True -2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False -2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_only,LC08,6,97.54262569620018,55.898040761197564,False -2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",12,99.40227487869481,63.41647525357126,True -2020,Apr-Jun_3m,3,2020-04-01,2020-07-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False -2025,Apr-Jun_3m,3,2025-04-01,2025-07-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,Sep-Nov_3m,3,1990-09-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1990,Jan-Dec_12m,12,1990-01-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1990,Feb-Jan_12m,12,1989-02-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1990,Dec-May_6m,6,1989-12-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1990,Dec-Feb_3m,3,1989-12-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1990,Jan-Apr_4m,4,1990-01-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1990,Sep-Aug_12m,12,1989-09-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1990,Feb-May_4m,4,1990-02-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1990,Aug-Oct_3m,3,1990-08-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1990,Sep-Feb_6m,6,1989-09-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1990,Aug-Jan_6m,6,1989-08-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1990,Jul-Dec_6m,6,1990-07-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1990,Jun-Nov_6m,6,1990-06-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False 1990,May-Jul_3m,3,1990-05-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False -1995,May-Jul_3m,3,1995-05-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False -2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False -2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True -2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False -2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",1,5.734228027042754,0.0,True -2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False -2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False -2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True -2020,May-Jul_3m,3,2020-05-01,2020-08-01,primary_only,LC08,5,52.298947894275784,17.898937105226143,False -2025,May-Jul_3m,3,2025-05-01,2025-08-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,Apr-Sep_6m,6,1990-04-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1990,Feb-Jul_6m,6,1990-02-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1990,Jan-Jun_6m,6,1990-01-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1990,Dec-Mar_4m,4,1989-12-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False 1990,Jun-Aug_3m,3,1990-06-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False -1995,Jun-Aug_3m,3,1995-06-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False -2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False -2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True -2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False -2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True -2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False -2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_only,LC08,2,70.05992553901368,0.0,False -2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",2,70.05992553901368,0.0,True -2020,Jun-Aug_3m,3,2020-06-01,2020-09-01,primary_only,LC08,2,39.44042763503558,0.0,False -2025,Jun-Aug_3m,3,2025-06-01,2025-09-01,primary_only,LC08,1,1.041198517932418,0.0,False +1990,Mar-Aug_6m,6,1990-03-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1990,Mar-Jun_4m,4,1990-03-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1990,Oct-Jan_4m,4,1989-10-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1990,Sep-Dec_4m,4,1990-09-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1990,Aug-Nov_4m,4,1990-08-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1990,Jul-Oct_4m,4,1990-07-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False 1990,Jul-Sep_3m,3,1990-07-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1990,Jun-Sep_4m,4,1990-06-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1990,May-Aug_4m,4,1990-05-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1990,Apr-Jul_4m,4,1990-04-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1990,Nov-Feb_4m,4,1989-11-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Nov_4m,4,1995-08-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-May_12m,12,1994-06-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Dec_4m,4,1995-09-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Dec_12m,12,1995-01-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Mar_12m,12,1994-04-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Apr_12m,12,1994-05-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Nov_6m,6,1995-06-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Feb_4m,4,1994-11-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Dec_6m,6,1995-07-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Jan_4m,4,1994-10-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Oct_4m,4,1995-07-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Aug_3m,3,1995-06-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Mar_6m,6,1994-10-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Aug_6m,6,1995-03-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Apr_3m,3,1995-02-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False 1995,Jul-Sep_3m,3,1995-07-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False -2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False -2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False -2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True -2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False -2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True -2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False -2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True -2020,Jul-Sep_3m,3,2020-07-01,2020-10-01,primary_only,LC08,1,31.174148193366612,0.0,False -2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False -1990,Aug-Oct_3m,3,1990-08-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Jul_3m,3,1995-05-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False 1995,Aug-Oct_3m,3,1995-08-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False -2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False -2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False -2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",2,8.789972621051634,0.0,True -2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False -2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True -2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_only,LC08,0,0.0,0.0,False -2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",3,8.655065766282677,2.935682513385204,True -2020,Aug-Oct_3m,3,2020-08-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False -2025,Aug-Oct_3m,3,2025-08-01,2025-11-01,primary_only,LC08,2,12.073733385514196,0.0,False -1990,Sep-Nov_3m,3,1990-09-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False -1995,Sep-Nov_3m,3,1995-09-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False -2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False -2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False -2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",4,81.04892728307274,32.53886464324526,True -2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False -2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True -2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False -2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True -2020,Sep-Nov_3m,3,2020-09-01,2020-12-01,primary_only,LC08,3,25.049198118928206,8.907243651425132,False -2025,Sep-Nov_3m,3,2025-09-01,2025-12-01,primary_only,LC08,2,14.69865565480517,0.0,False -1990,Oct-Dec_3m,3,1990-10-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False -1995,Oct-Dec_3m,3,1995-10-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False -2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False -2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True -2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False -2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230215,48.999682988950326,True -2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False -2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",6,77.06529687299054,28.833652379980872,True -2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False -2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",16,99.99661400937033,81.62494116586562,True -2020,Oct-Dec_3m,3,2020-10-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False -2025,Oct-Dec_3m,3,2025-10-01,2026-01-01,primary_only,LC08,5,57.48063060201698,20.584371624624833,False -1990,Nov-Jan_3m,3,1989-11-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Jun_3m,3,1995-04-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-May_3m,3,1995-03-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Mar_3m,3,1995-01-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Mar_4m,4,1994-12-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Jan_12m,12,1994-02-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Jun_6m,6,1995-01-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Feb_12m,12,1994-03-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Jul_6m,6,1995-02-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Jul_12m,12,1994-08-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Jul_4m,4,1995-04-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-May_6m,6,1994-12-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Jun_4m,4,1995-03-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Aug_12m,12,1994-09-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-May_4m,4,1995-02-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Feb_6m,6,1994-09-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Sep_6m,6,1995-04-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Sep_12m,12,1994-10-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Feb_3m,3,1994-12-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Aug_4m,4,1995-05-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False 1995,Nov-Jan_3m,3,1994-11-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Apr_6m,6,1994-11-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Dec_3m,3,1995-10-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Nov_12m,12,1994-12-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Jan_6m,6,1994-08-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Apr_4m,4,1995-01-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Sep_4m,4,1995-06-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Jun_12m,12,1994-07-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Nov_3m,3,1995-09-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Oct_6m,6,1995-05-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Oct_12m,12,1994-11-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False 2000,Nov-Jan_3m,3,1999-11-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False 2000,Nov-Jan_3m,3,1999-11-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True -2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False -2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True -2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False -2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",4,84.02771858544709,39.686868140752516,True -2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_only,LC08,6,99.99622271947547,80.038023561852,False -2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",14,99.99970147171831,83.94154596448331,True -2020,Nov-Jan_3m,3,2019-11-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False -2025,Nov-Jan_3m,3,2024-11-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False -1990,Dec-Feb_3m,3,1989-12-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False -1995,Dec-Feb_3m,3,1994-12-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False 2000,Dec-Feb_3m,3,1999-12-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False 2000,Dec-Feb_3m,3,1999-12-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True -2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False -2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True -2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False -2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.686868140752516,True -2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_only,LC08,4,99.98661468043085,74.20328925979955,False -2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",13,99.99917716066042,83.94154596448331,True -2020,Dec-Feb_3m,3,2019-12-01,2020-03-01,primary_only,LC08,12,99.9999966096206,92.4627230944063,False -2025,Dec-Feb_3m,3,2024-12-01,2025-03-01,primary_only,LC08,11,99.98651246303923,78.98972857254677,False -1990,Jan-Apr_4m,4,1990-01-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False -1995,Jan-Apr_4m,4,1995-01-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552892,65.10708852743944,True +2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353796,44.263437174874525,True +2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False 2000,Jan-Apr_4m,4,2000-01-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353796,44.263437174874525,True +2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True 2000,Jan-Apr_4m,4,2000-01-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True -2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False -2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True -2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False -2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True -2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_only,LC08,5,99.97910683045646,60.62558215383077,False -2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",17,99.99975039988183,77.91912842227305,True -2020,Jan-Apr_4m,4,2020-01-01,2020-05-01,primary_only,LC08,14,99.9999968100652,92.4627230944063,False -2025,Jan-Apr_4m,4,2025-01-01,2025-05-01,primary_only,LC08,9,99.97331931309324,82.78798056832599,False -1990,Feb-May_4m,4,1990-02-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False -1995,Feb-May_4m,4,1995-02-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353795,44.263437174874525,True -2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False -2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True -2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False -2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True -2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False -2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",18,99.86951234815521,69.94945631243047,True -2020,Feb-May_4m,4,2020-02-01,2020-06-01,primary_only,LC08,14,99.99565066080038,83.1191679886652,False -2025,Feb-May_4m,4,2025-02-01,2025-06-01,primary_only,LC08,8,99.87323530155264,77.57867623864364,False -1990,Mar-Jun_4m,4,1990-03-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False -1995,Mar-Jun_4m,4,1995-03-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True -2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False -2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True -2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False -2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True -2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False -2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108565,69.94945631243047,True -2020,Mar-Jun_4m,4,2020-03-01,2020-07-01,primary_only,LC08,11,97.8290753978749,54.14122230543774,False -2025,Mar-Jun_4m,4,2025-03-01,2025-07-01,primary_only,LC08,4,95.98010283732056,60.34000438856134,False -1990,Apr-Jul_4m,4,1990-04-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False -1995,Apr-Jul_4m,4,1995-04-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True -2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False -2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",2,13.917763187815046,0.0,True -2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False -2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False -2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",14,99.8210406536092,69.6273866274382,True -2020,Apr-Jul_4m,4,2020-04-01,2020-08-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False -2025,Apr-Jul_4m,4,2025-04-01,2025-08-01,primary_only,LC08,2,84.02840287962343,0.0,False -1990,May-Aug_4m,4,1990-05-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False -1995,May-Aug_4m,4,1995-05-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",3,98.21578307322392,57.6336846624187,True +2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",11,99.1500794403698,52.0061067316496,True +2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Oct_4m,4,2000-07-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_only,LT05,1,92.88684247459292,0.0,False +2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True 2000,May-Aug_4m,4,2000-05-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False 2000,May-Aug_4m,4,2000-05-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True -2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False -2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True -2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False -2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False -2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True -2020,May-Aug_4m,4,2020-05-01,2020-09-01,primary_only,LC08,6,67.16934456750927,24.28684380484538,False -2025,May-Aug_4m,4,2025-05-01,2025-09-01,primary_only,LC08,3,84.19469891213093,37.900752262065645,False -1990,Jun-Sep_4m,4,1990-06-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False -1995,Jun-Sep_4m,4,1995-06-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False 2000,Jun-Sep_4m,4,2000-06-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False -2000,Jun-Sep_4m,4,2000-06-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True -2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False -2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True -2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False -2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True -2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False -2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True -2020,Jun-Sep_4m,4,2020-06-01,2020-10-01,primary_only,LC08,2,39.44042763503558,0.0,False -2025,Jun-Sep_4m,4,2025-06-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False -1990,Jul-Oct_4m,4,1990-07-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False -1995,Jul-Oct_4m,4,1995-07-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False -2000,Jul-Oct_4m,4,2000-07-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Sep_4m,4,2000-06-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",1,12.2449143488938,0.0,True 2000,Jul-Oct_4m,4,2000-07-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False -2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True -2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False -2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True -2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_only,LC08,2,70.05992553901368,0.0,False -2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",5,72.65125867408607,30.716739720027615,True -2020,Jul-Oct_4m,4,2020-07-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False -2025,Jul-Oct_4m,4,2025-07-01,2025-11-01,primary_only,LC08,2,12.073733385514196,0.0,False -1990,Aug-Nov_4m,4,1990-08-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False -1995,Aug-Nov_4m,4,1995-08-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",1,12.2449143488938,0.0,True +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552892,65.10708852743944,True +2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False 2000,Aug-Nov_4m,4,2000-08-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False 2000,Aug-Nov_4m,4,2000-08-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True -2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False -2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",5,81.19875872881087,32.53886464324526,True -2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False -2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True -2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False -2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True -2020,Aug-Nov_4m,4,2020-08-01,2020-12-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False -2025,Aug-Nov_4m,4,2025-08-01,2025-12-01,primary_only,LC08,3,15.586811987903781,5.395098365951714,False -1990,Sep-Dec_4m,4,1990-09-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False -1995,Sep-Dec_4m,4,1995-09-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True 2000,Sep-Dec_4m,4,2000-09-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False -2000,Sep-Dec_4m,4,2000-09-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True -2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False -2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230215,48.999682988950326,True -2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False -2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True -2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False -2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",17,99.99661403038459,81.62494116586562,True -2020,Sep-Dec_4m,4,2020-09-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False -2025,Sep-Dec_4m,4,2025-09-01,2026-01-01,primary_only,LC08,5,57.48063060201698,20.584371624624833,False -1990,Oct-Jan_4m,4,1989-10-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False -1995,Oct-Jan_4m,4,1994-10-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",1,12.2449143488938,0.0,True +2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False 2000,Oct-Jan_4m,4,1999-10-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False 2000,Oct-Jan_4m,4,1999-10-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True -2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False -2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True -2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False -2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.686868140752516,True -2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False -2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",20,99.99985260336734,83.94154596448331,True -2020,Oct-Jan_4m,4,2019-10-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False -2025,Oct-Jan_4m,4,2024-10-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False -1990,Nov-Feb_4m,4,1989-11-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False -1995,Nov-Feb_4m,4,1994-11-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False -2000,Nov-Feb_4m,4,1999-11-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False 2000,Nov-Feb_4m,4,1999-11-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2000,Nov-Feb_4m,4,1999-11-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Dec_4m,4,2000-09-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",4,20.214509718071028,7.108844196338966,True +2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True +2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",9,63.8530225472835,20.979279514264185,True +2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True +2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True +2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True +2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",12,92.54897664388146,42.90104908880774,True +2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",5,81.19875872881087,32.53886464324526,True +2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230216,48.999682988950326,True +2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",7,60.36951364729075,20.979279514264185,True +2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",4,81.04892728307274,32.53886464324526,True +2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230216,48.999682988950326,True +2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",1,8.681343174190491,0.0,True +2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True +2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",2,8.789972621051634,0.0,True +2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",2,13.917763187815046,0.0,True +2005,Mar-Aug_6m,6,2005-03-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True +2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",8,93.75585813242849,48.999682988950326,True 2005,Nov-Feb_4m,4,2004-11-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",6,90.67826640340226,44.45034468183726,True +2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",13,95.45057829972708,48.999682988950326,True 2005,Nov-Feb_4m,4,2004-11-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True -2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False -2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.686868140752516,True -2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_only,LC08,6,99.99622271947547,80.038023561852,False -2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",16,99.9997773111493,83.94154596448331,True -2020,Nov-Feb_4m,4,2019-11-01,2020-03-01,primary_only,LC08,16,99.99999811029484,92.4627230944063,False -2025,Nov-Feb_4m,4,2024-11-01,2025-03-01,primary_only,LC08,14,99.9921366831703,78.98972857254677,False -1990,Dec-Mar_4m,4,1989-12-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False -1995,Dec-Mar_4m,4,1994-12-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",6,82.27686477521533,32.53886464324526,True +2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False 2005,Dec-Mar_4m,4,2004-12-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False 2005,Dec-Mar_4m,4,2004-12-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True -2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False -2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True -2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_only,LC08,5,99.98973232638939,74.20328925979955,False -2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",17,99.99975919252638,83.94154596448331,True -2020,Dec-Mar_4m,4,2019-12-01,2020-04-01,primary_only,LC08,16,99.99999946438633,92.4627230944063,False -2025,Dec-Mar_4m,4,2024-12-01,2025-04-01,primary_only,LC08,13,99.99660531685394,82.78798056832599,False -1990,Jan-Jun_6m,6,1990-01-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False -1995,Jan-Jun_6m,6,1995-01-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True +2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False 2005,Jan-Jun_6m,6,2005-01-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False -2005,Jan-Jun_6m,6,2005-01-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True -2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False -2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True -2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_only,LC08,9,99.99882064616772,77.8534842100577,False -2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",24,99.99999038674221,87.5917409147415,True -2020,Jan-Jun_6m,6,2020-01-01,2020-07-01,primary_only,LC08,19,99.99999847836753,92.4627230944063,False -2025,Jan-Jun_6m,6,2025-01-01,2025-07-01,primary_only,LC08,11,99.9957386681783,84.64614702896075,False -1990,Feb-Jul_6m,6,1990-02-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False -1995,Feb-Jul_6m,6,1995-02-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False -2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False -2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",1,5.734228027042754,0.0,True +2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False 2005,Feb-Jul_6m,6,2005-02-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False 2005,Feb-Jul_6m,6,2005-02-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",5,22.83854293048695,8.021730615521188,True -2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False -2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True -2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False -2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",20,99.96093189987528,69.94945631243047,True -2020,Feb-Jul_6m,6,2020-02-01,2020-08-01,primary_only,LC08,15,99.99617303505754,83.1191679886652,False -2025,Feb-Jul_6m,6,2025-02-01,2025-08-01,primary_only,LC08,8,99.87323530155264,77.57867623864364,False -1990,Mar-Aug_6m,6,1990-03-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False -1995,Mar-Aug_6m,6,1995-03-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True -2005,Mar-Aug_6m,6,2005-03-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True 2005,Mar-Aug_6m,6,2005-03-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",5,22.9194008930627,8.021730615521188,True -2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False -2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True -2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False -2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",18,99.9476267771708,69.94945631243047,True -2020,Mar-Aug_6m,6,2020-03-01,2020-09-01,primary_only,LC08,12,98.50584265050763,54.14122230543774,False -2025,Mar-Aug_6m,6,2025-03-01,2025-09-01,primary_only,LC08,5,96.02195794700079,60.34000438856134,False -1990,Apr-Sep_6m,6,1990-04-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False -1995,Apr-Sep_6m,6,1995-04-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True +2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",5,57.44590265932441,22.97408918804021,True +2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False 2005,Apr-Sep_6m,6,2005-04-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False 2005,Apr-Sep_6m,6,2005-04-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",3,14.598348724401722,5.0687312352337885,True +2005,Jan-Jun_6m,6,2005-01-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True +2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True +2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.68686814075251,True +2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False 2010,Apr-Sep_6m,6,2010-04-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False 2010,Apr-Sep_6m,6,2010-04-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True -2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False -2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",15,99.82104176427299,69.6273866274382,True -2020,Apr-Sep_6m,6,2020-04-01,2020-10-01,primary_only,LC08,8,90.54213772277048,40.58143388086358,False -2025,Apr-Sep_6m,6,2025-04-01,2025-10-01,primary_only,LC08,3,84.19469891213093,37.900752262065645,False -1990,May-Oct_6m,6,1990-05-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False -1995,May-Oct_6m,6,1995-05-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False -2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False -2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True -2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False -2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True +2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True +2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True 2010,May-Oct_6m,6,2010-05-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False 2010,May-Oct_6m,6,2010-05-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True -2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False -2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",12,98.94667317251714,63.13126063996379,True -2020,May-Oct_6m,6,2020-05-01,2020-11-01,primary_only,LC08,9,75.39316049053653,25.146863455003754,False -2025,May-Oct_6m,6,2025-05-01,2025-11-01,primary_only,LC08,4,85.95677093334616,41.26989078011156,False -1990,Jun-Nov_6m,6,1990-06-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False -1995,Jun-Nov_6m,6,1995-06-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False -2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False -2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True -2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False -2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",6,82.27686477521533,32.53886464324526,True -2010,Jun-Nov_6m,6,2010-06-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",9,91.63102270855704,46.28341533178432,True +2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True 2010,Jun-Nov_6m,6,2010-06-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True -2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_only,LC08,6,92.56824893242367,45.63231976158158,False -2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",11,93.31279853448552,45.63231976158158,True -2020,Jun-Nov_6m,6,2020-06-01,2020-12-01,primary_only,LC08,5,54.61011489671122,20.251522816828125,False -2025,Jun-Nov_6m,6,2025-06-01,2025-12-01,primary_only,LC08,3,15.586811987903781,5.395098365951714,False -1990,Jul-Dec_6m,6,1990-07-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False -1995,Jul-Dec_6m,6,1995-07-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False -2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False -2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True -2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False -2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",8,93.75585813242849,48.999682988950326,True +2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",12,96.4234233774342,55.456614464889256,True +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True 2010,Jul-Dec_6m,6,2010-07-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False -2010,Jul-Dec_6m,6,2010-07-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True -2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_only,LC08,10,99.98186760213181,69.57196223718582,False -2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",19,99.99898623817593,81.62494116586562,True -2020,Jul-Dec_6m,6,2020-07-01,2021-01-01,primary_only,LC08,6,57.53070893529786,20.155114847508894,False -2025,Jul-Dec_6m,6,2025-07-01,2026-01-01,primary_only,LC08,6,57.923341646023005,20.584371624624833,False -1990,Aug-Jan_6m,6,1989-08-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False -1995,Aug-Jan_6m,6,1994-08-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False -2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False -2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True -2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False -2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True +2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False 2010,Aug-Jan_6m,6,2009-08-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False -2010,Aug-Jan_6m,6,2009-08-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.686868140752516,True -2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False -2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",22,99.99985404685276,83.94154596448331,True -2020,Aug-Jan_6m,6,2019-08-01,2020-02-01,primary_only,LC08,13,99.99910847793507,76.43072751898961,False -2025,Aug-Jan_6m,6,2024-08-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False -1990,Sep-Feb_6m,6,1989-09-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False -1995,Sep-Feb_6m,6,1994-09-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False -2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False -2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True -2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False -2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2010,Aug-Jan_6m,6,2009-08-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.68686814075251,True +2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",10,91.97785219851266,46.28341533178432,True +2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",6,77.06529687299054,28.833652379980872,True +2010,Jul-Dec_6m,6,2010-07-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True +2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Nov_6m,6,2010-06-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.68686814075251,True +2010,Oct-Mar_6m,6,2009-10-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",8,91.34324607677756,46.28341533178432,True 2010,Sep-Feb_6m,6,2009-09-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False -2010,Sep-Feb_6m,6,2009-09-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",6,84.59016229601333,39.686868140752516,True -2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False -2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",22,99.99989004865289,83.94154596448331,True -2020,Sep-Feb_6m,6,2019-09-01,2020-03-01,primary_only,LC08,17,99.99999842840064,92.4627230944063,False -2025,Sep-Feb_6m,6,2024-09-01,2025-03-01,primary_only,LC08,14,99.9921366831703,78.98972857254677,False -1990,Oct-Mar_6m,6,1989-10-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False -1995,Oct-Mar_6m,6,1994-10-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True -2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False -2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2010,Sep-Feb_6m,6,2009-09-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",6,84.59016229601333,39.68686814075251,True +2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",11,93.21865856027992,50.16453781543488,True +2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False 2010,Oct-Mar_6m,6,2009-10-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False -2010,Oct-Mar_6m,6,2009-10-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True -2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_only,LC08,11,99.9981944418267,80.038023561852,False -2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",26,99.99996782226512,83.94154596448331,True -2020,Oct-Mar_6m,6,2019-10-01,2020-04-01,primary_only,LC08,20,99.99999970146352,92.4627230944063,False -2025,Oct-Mar_6m,6,2024-10-01,2025-04-01,primary_only,LC08,16,99.99802087889053,82.78798056832599,False -1990,Nov-Apr_6m,6,1989-11-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False -1995,Nov-Apr_6m,6,1994-11-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False -2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False -2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True -2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False -2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True -2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False -2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True -2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_only,LC08,9,99.99873859639293,80.038023561852,False -2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",25,99.99998988585686,83.94154596448331,True -2020,Nov-Apr_6m,6,2019-11-01,2020-05-01,primary_only,LC08,22,99.99999991399756,92.4627230944063,False -2025,Nov-Apr_6m,6,2024-11-01,2025-05-01,primary_only,LC08,16,99.99802087889053,82.78798056832599,False -1990,Dec-May_6m,6,1989-12-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False -1995,Dec-May_6m,6,1994-12-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552893,65.10708852743944,True -2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False -2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True -2010,Dec-May_6m,6,2009-12-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",12,98.36361505477768,61.833501818017,True +2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False 2010,Dec-May_6m,6,2009-12-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2010,Dec-May_6m,6,2009-12-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",4,84.02771858544709,39.68686814075251,True +2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.68686814075251,True +2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False +2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True +2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",15,99.821041764273,69.6273866274382,True +2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False +2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108563,69.94945631243047,True +2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.89804076119757,False +2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",26,99.99996782226512,83.94154596448331,True +2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_only,LC08,11,99.9981944418267,80.038023561852,False +2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108563,69.94945631243047,True +2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",20,99.99985260336734,83.94154596448331,True +2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",14,99.8210406536092,69.6273866274382,True +2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False +2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",17,99.9966140303846,81.62494116586562,True +2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False +2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False +2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_only,LC08,5,99.97910683045646,60.62558215383077,False +2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_only,LC08,6,98.3099767854342,63.13126063996379,False +2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",5,72.65125867408607,30.716739720027615,True +2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_only,LC08,6,92.56824893242369,45.63231976158158,False 2015,Dec-May_6m,6,2014-12-01,2015-06-01,primary_only,LC08,11,99.99974768482708,89.20002667996266,False +2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",16,99.99661400937032,81.62494116586562,True +2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",2,70.05992553901368,0.0,True +2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_only,LC08,10,99.9818676021318,69.57196223718582,False 2015,Dec-May_6m,6,2014-12-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",29,99.99999856063324,89.20002667996266,True -2020,Dec-May_6m,6,2019-12-01,2020-06-01,primary_only,LC08,22,99.99999991635079,92.4627230944063,False -2025,Dec-May_6m,6,2024-12-01,2025-06-01,primary_only,LC08,15,99.9994578148844,84.64614702896075,False -1990,Jan-Dec_12m,12,1990-01-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False -1995,Jan-Dec_12m,12,1995-01-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False -2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False -2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",11,99.1500794403698,52.0061067316496,True -2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False -2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",13,95.45057829972708,48.999682988950326,True -2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False -2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",12,96.4234233774342,55.456614464889256,True -2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_only,LC08,19,99.99999978615487,96.12321885528449,False -2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",43,99.99999999990254,96.12321885528449,True -2020,Jan-Dec_12m,12,2020-01-01,2021-01-01,primary_only,LC08,25,99.99999935377349,92.4627230944063,False -2025,Jan-Dec_12m,12,2025-01-01,2026-01-01,primary_only,LC08,17,99.99820697396805,84.64614702896075,False -1990,Feb-Jan_12m,12,1989-02-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False -1995,Feb-Jan_12m,12,1994-02-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False -2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_only,LT05,1,92.88684247459292,0.0,False -2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",3,98.21578307322392,57.6336846624187,True -2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False -2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",6,90.67826640340226,44.45034468183726,True -2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False -2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",12,98.36361505477768,61.833501818017,True -2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_only,LC08,20,99.99998288037484,91.05817819518664,False -2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",43,99.99999993373655,91.05817819518664,True -2020,Feb-Jan_12m,12,2019-02-01,2020-02-01,primary_only,LC08,18,99.9996394421078,76.43072751898961,False -2025,Feb-Jan_12m,12,2024-02-01,2025-02-01,primary_only,LC08,18,99.99998192456317,82.13699542031398,False -1990,Mar-Feb_12m,12,1989-03-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False -1995,Mar-Feb_12m,12,1994-03-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False -2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True -2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False -2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",5,57.44590265932441,22.97408918804021,True -2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False -2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",11,93.21865856027992,50.16453781543488,True +2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_only,LC08,4,99.98661468043085,74.20328925979955,False +2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",11,93.31279853448552,45.63231976158158,True +2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_only,LC08,6,97.54262569620018,55.89804076119757,False +2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",14,99.99970147171832,83.94154596448331,True +2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_only,LC08,19,99.99999978615487,96.12321885528448,False +2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",13,99.99917716066042,83.94154596448331,True +2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",12,98.94667317251714,63.13126063996379,True +2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_only,LC08,6,98.3099767854342,63.13126063996379,False +2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",12,99.4022748786948,63.41647525357126,True +2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",19,99.99898623817592,81.62494116586562,True +2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",25,99.99998988585686,83.94154596448331,True +2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_only,LC08,9,99.99873859639293,80.038023561852,False +2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",22,99.99985404685276,83.94154596448331,True +2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_only,LC08,6,99.99622271947548,80.038023561852,False +2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",22,99.99989004865289,83.94154596448331,True +2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_only,LC08,0,0.0,0.0,False +2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False +2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True +2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True +2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",46,99.99999999014057,91.05817819518664,True +2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_only,LC08,7,98.114985812616,55.89804076119757,False +2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",11,96.61199834332788,52.721554256203554,True +2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",43,99.99999993373656,91.05817819518664,True 2015,Mar-Feb_12m,12,2014-03-01,2015-03-01,primary_only,LC08,20,99.99998288037484,91.05817819518664,False -2015,Mar-Feb_12m,12,2014-03-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",43,99.99999992303607,91.05817819518664,True -2020,Mar-Feb_12m,12,2019-03-01,2020-03-01,primary_only,LC08,20,99.99999875162852,92.4627230944063,False -2025,Mar-Feb_12m,12,2024-03-01,2025-03-01,primary_only,LC08,20,99.99916078896021,78.98972857254677,False -1990,Apr-Mar_12m,12,1989-04-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False -1995,Apr-Mar_12m,12,1994-04-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False -2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True -2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False -2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True -2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False -2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True +2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",42,99.99999994297887,89.20002667996266,True +2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_only,LC08,15,99.9999786819862,89.20002667996266,False +2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False +2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_only,LC08,20,99.99998288037484,91.05817819518664,False +2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_only,LC08,3,99.95200756228056,55.84014934025937,False 2015,Apr-Mar_12m,12,2014-04-01,2015-04-01,primary_only,LC08,18,99.9999747937986,91.05817819518664,False -2015,Apr-Mar_12m,12,2014-04-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",42,99.99999993787675,91.05817819518664,True -2020,Apr-Mar_12m,12,2019-04-01,2020-04-01,primary_only,LC08,23,99.99999978896516,92.4627230944063,False -2025,Apr-Mar_12m,12,2024-04-01,2025-04-01,primary_only,LC08,20,99.99941834568435,82.78798056832599,False -1990,May-Apr_12m,12,1989-05-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False -1995,May-Apr_12m,12,1994-05-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False -2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False -2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True -2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False -2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True -2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False -2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True -2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_only,LC08,20,99.99998902665786,91.05817819518664,False -2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",46,99.99999999014055,91.05817819518664,True -2020,May-Apr_12m,12,2019-05-01,2020-05-01,primary_only,LC08,25,99.99999993920503,92.4627230944063,False -2025,May-Apr_12m,12,2024-05-01,2025-05-01,primary_only,LC08,18,99.99876965319511,82.78798056832599,False -1990,Jun-May_12m,12,1989-06-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False -1995,Jun-May_12m,12,1994-06-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False -2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552893,65.10708852743944,True -2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False -2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True -2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False -2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True -2015,Jun-May_12m,12,2014-06-01,2015-06-01,primary_only,LC08,20,99.99997220174258,89.20002667996266,False 2015,Jun-May_12m,12,2014-06-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",45,99.99999989988848,89.20002667996266,True -2020,Jun-May_12m,12,2019-06-01,2020-06-01,primary_only,LC08,27,99.99999996122469,92.4627230944063,False -2025,Jun-May_12m,12,2024-06-01,2025-06-01,primary_only,LC08,19,99.99975550478729,84.64614702896075,False -1990,Jul-Jun_12m,12,1989-07-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False -1995,Jul-Jun_12m,12,1994-07-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False -2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True -2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False -2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True -2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False -2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True -2015,Jul-Jun_12m,12,2014-07-01,2015-07-01,primary_only,LC08,18,99.99997209501109,89.20002667996266,False +2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False 2015,Jul-Jun_12m,12,2014-07-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",41,99.99999988022066,89.20002667996266,True -2020,Jul-Jun_12m,12,2019-07-01,2020-07-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False -2025,Jul-Jun_12m,12,2024-07-01,2025-07-01,primary_only,LC08,18,99.99968390274987,84.64614702896075,False -1990,Aug-Jul_12m,12,1989-08-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False -1995,Aug-Jul_12m,12,1994-08-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False -2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False -2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True -2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False -2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",7,60.36951364729075,20.979279514264185,True -2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False -2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True -2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False -2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",42,99.99999994297887,89.20002667996266,True -2020,Aug-Jul_12m,12,2019-08-01,2020-08-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False -2025,Aug-Jul_12m,12,2024-08-01,2025-08-01,primary_only,LC08,18,99.99968390274987,84.64614702896075,False -1990,Sep-Aug_12m,12,1989-09-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False -1995,Sep-Aug_12m,12,1994-09-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False -2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False -2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True -2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False -2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True -2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False -2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Apr-Mar_12m,12,2014-04-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",42,99.99999993787677,91.05817819518664,True +2015,Mar-Feb_12m,12,2014-03-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",43,99.99999992303609,91.05817819518664,True +2015,Jul-Jun_12m,12,2014-07-01,2015-07-01,primary_only,LC08,18,99.99997209501107,89.20002667996266,False +2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_only,LC08,20,99.99998902665786,91.05817819518664,False 2015,Sep-Aug_12m,12,2014-09-01,2015-09-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False -2015,Sep-Aug_12m,12,2014-09-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",40,99.99999994241495,89.20002667996266,True -2020,Sep-Aug_12m,12,2019-09-01,2020-09-01,primary_only,LC08,29,99.99999997651783,92.4627230944063,False -2025,Sep-Aug_12m,12,2024-09-01,2025-09-01,primary_only,LC08,19,99.99968719394975,84.64614702896075,False -1990,Oct-Sep_12m,12,1989-10-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False -1995,Oct-Sep_12m,12,1994-10-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False -2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False -2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True -2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False -2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True -2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False -2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",9,91.63102270855705,46.28341533178432,True -2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False -2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",41,99.9999999424153,89.20002667996266,True -2020,Oct-Sep_12m,12,2019-10-01,2020-10-01,primary_only,LC08,28,99.99999997176484,92.4627230944063,False -2025,Oct-Sep_12m,12,2024-10-01,2025-10-01,primary_only,LC08,19,99.99968719394975,84.64614702896075,False -1990,Nov-Oct_12m,12,1989-11-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False -1995,Nov-Oct_12m,12,1994-11-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False -2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False -2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True -2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False -2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",9,63.8530225472835,20.979279514264185,True -2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False -2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",8,91.34324607677755,46.28341533178432,True -2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_only,LC08,15,99.9999786819862,89.20002667996266,False -2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",37,99.99999989346502,89.20002667996266,True -2020,Nov-Oct_12m,12,2019-11-01,2020-11-01,primary_only,LC08,31,99.99999997883752,92.4627230944063,False -2025,Nov-Oct_12m,12,2024-11-01,2025-11-01,primary_only,LC08,20,99.99972206748909,84.64614702896075,False -1990,Dec-Nov_12m,12,1989-12-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False -1995,Dec-Nov_12m,12,1994-12-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False -2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False -2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True -2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False -2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",12,92.54897664388146,42.90104908880774,True -2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False -2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",10,91.97785219851266,46.28341533178432,True 2015,Dec-Nov_12m,12,2014-12-01,2015-12-01,primary_only,LC08,17,99.99998124856444,89.20002667996266,False +2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",12,99.9983916925276,77.91912842227305,True +2015,Sep-Aug_12m,12,2014-09-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",40,99.99999994241496,89.20002667996266,True 2015,Dec-Nov_12m,12,2014-12-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",40,99.99999990374664,89.20002667996266,True +2015,Jun-May_12m,12,2014-06-01,2015-06-01,primary_only,LC08,20,99.99997220174258,89.20002667996266,False +2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_only,LC08,3,66.60550893983303,30.373115138961243,False +2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",37,99.99999989346502,89.20002667996266,True +2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",41,99.9999999424153,89.20002667996266,True +2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_only,LC08,6,99.99622271947548,80.038023561852,False +2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",16,99.9997773111493,83.94154596448331,True +2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",18,99.8695123481552,69.94945631243047,True +2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",17,99.99975039988183,77.91912842227305,True +2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",18,99.9476267771708,69.94945631243047,True +2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True +2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",20,99.96093189987528,69.94945631243047,True +2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False +2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True +2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",24,99.9999903867422,87.5917409147415,True +2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.89804076119757,False +2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_only,LC08,9,99.99882064616772,77.8534842100577,False +2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",17,99.99975919252638,83.94154596448331,True +2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_only,LC08,5,99.9897323263894,74.20328925979955,False +2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_only,LC08,6,98.3099767854342,63.13126063996379,False +2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True +2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",43,99.99999999990254,96.12321885528448,True +2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",3,8.655065766282677,2.935682513385204,True +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2020,Feb-Jan_12m,12,2019-02-01,2020-02-01,primary_only,LC08,18,99.9996394421078,76.43072751898961,False +2020,Jun-Sep_4m,4,2020-06-01,2020-10-01,primary_only,LC08,2,39.44042763503558,0.0,False +2020,Mar-Aug_6m,6,2020-03-01,2020-09-01,primary_only,LC08,12,98.50584265050765,54.14122230543774,False +2020,Mar-May_3m,3,2020-03-01,2020-06-01,primary_only,LC08,10,97.53274785282866,54.14122230543774,False +2020,Nov-Apr_6m,6,2019-11-01,2020-05-01,primary_only,LC08,22,99.99999991399756,92.4627230944063,False +2020,Sep-Feb_6m,6,2019-09-01,2020-03-01,primary_only,LC08,17,99.99999842840064,92.4627230944063,False +2020,Jan-Dec_12m,12,2020-01-01,2021-01-01,primary_only,LC08,25,99.99999935377348,92.4627230944063,False +2020,Dec-May_6m,6,2019-12-01,2020-06-01,primary_only,LC08,22,99.9999999163508,92.4627230944063,False +2020,May-Apr_12m,12,2019-05-01,2020-05-01,primary_only,LC08,25,99.99999993920504,92.4627230944063,False +2020,Apr-Sep_6m,6,2020-04-01,2020-10-01,primary_only,LC08,8,90.54213772277048,40.58143388086358,False +2020,Jul-Oct_4m,4,2020-07-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2020,Sep-Dec_4m,4,2020-09-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False +2020,Aug-Nov_4m,4,2020-08-01,2020-12-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2020,Oct-Jan_4m,4,2019-10-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False +2020,Jan-Mar_3m,3,2020-01-01,2020-04-01,primary_only,LC08,12,99.9999889269216,92.4627230944063,False +2020,May-Jul_3m,3,2020-05-01,2020-08-01,primary_only,LC08,5,52.298947894275784,17.898937105226143,False +2020,Nov-Feb_4m,4,2019-11-01,2020-03-01,primary_only,LC08,16,99.99999811029484,92.4627230944063,False +2020,Dec-Mar_4m,4,2019-12-01,2020-04-01,primary_only,LC08,16,99.99999946438632,92.4627230944063,False +2020,Jun-May_12m,12,2019-06-01,2020-06-01,primary_only,LC08,27,99.99999996122467,92.4627230944063,False +2020,Jan-Jun_6m,6,2020-01-01,2020-07-01,primary_only,LC08,19,99.99999847836752,92.4627230944063,False +2020,Jul-Jun_12m,12,2019-07-01,2020-07-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False +2020,Aug-Jul_12m,12,2019-08-01,2020-08-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False +2020,Feb-Jul_6m,6,2020-02-01,2020-08-01,primary_only,LC08,15,99.99617303505754,83.1191679886652,False +2020,Feb-Apr_3m,3,2020-02-01,2020-05-01,primary_only,LC08,10,99.99197718965614,83.1191679886652,False +2020,Mar-Feb_12m,12,2019-03-01,2020-03-01,primary_only,LC08,20,99.99999875162852,92.4627230944063,False 2020,Dec-Nov_12m,12,2019-12-01,2020-12-01,primary_only,LC08,27,99.99999996203172,92.4627230944063,False -2025,Dec-Nov_12m,12,2024-12-01,2025-12-01,primary_only,LC08,18,99.999542324259,84.64614702896075,False +2020,Apr-Jul_4m,4,2020-04-01,2020-08-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False +2020,Dec-Feb_3m,3,2019-12-01,2020-03-01,primary_only,LC08,12,99.9999966096206,92.4627230944063,False +2020,Feb-May_4m,4,2020-02-01,2020-06-01,primary_only,LC08,14,99.99565066080038,83.1191679886652,False +2020,Mar-Jun_4m,4,2020-03-01,2020-07-01,primary_only,LC08,11,97.8290753978749,54.14122230543774,False +2020,Oct-Dec_3m,3,2020-10-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False +2020,Oct-Mar_6m,6,2019-10-01,2020-04-01,primary_only,LC08,20,99.99999970146352,92.4627230944063,False +2020,Oct-Sep_12m,12,2019-10-01,2020-10-01,primary_only,LC08,28,99.99999997176484,92.4627230944063,False +2020,Nov-Jan_3m,3,2019-11-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False +2020,Nov-Oct_12m,12,2019-11-01,2020-11-01,primary_only,LC08,31,99.99999997883752,92.4627230944063,False +2020,Jan-Apr_4m,4,2020-01-01,2020-05-01,primary_only,LC08,14,99.9999968100652,92.4627230944063,False +2020,Jul-Sep_3m,3,2020-07-01,2020-10-01,primary_only,LC08,1,31.174148193366612,0.0,False +2020,Sep-Aug_12m,12,2019-09-01,2020-09-01,primary_only,LC08,29,99.99999997651784,92.4627230944063,False +2020,May-Aug_4m,4,2020-05-01,2020-09-01,primary_only,LC08,6,67.16934456750927,24.28684380484538,False +2020,Jun-Aug_3m,3,2020-06-01,2020-09-01,primary_only,LC08,2,39.44042763503558,0.0,False +2020,May-Oct_6m,6,2020-05-01,2020-11-01,primary_only,LC08,9,75.39316049053653,25.146863455003754,False +2020,Apr-Mar_12m,12,2019-04-01,2020-04-01,primary_only,LC08,23,99.99999978896516,92.4627230944063,False +2020,Jun-Nov_6m,6,2020-06-01,2020-12-01,primary_only,LC08,5,54.61011489671122,20.251522816828125,False +2020,Aug-Oct_3m,3,2020-08-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2020,Jul-Dec_6m,6,2020-07-01,2021-01-01,primary_only,LC08,6,57.53070893529786,20.155114847508894,False +2020,Aug-Jan_6m,6,2019-08-01,2020-02-01,primary_only,LC08,13,99.99910847793508,76.43072751898961,False +2020,Apr-Jun_3m,3,2020-04-01,2020-07-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False +2020,Sep-Nov_3m,3,2020-09-01,2020-12-01,primary_only,LC08,3,25.049198118928206,8.907243651425132,False +2025,Oct-Mar_6m,6,2024-10-01,2025-04-01,primary_only,"LC08,LC09",28,99.99999994773997,93.13259747785794,False +2025,Sep-Feb_6m,6,2024-09-01,2025-03-01,primary_only,"LC08,LC09",26,99.99999977781616,93.13259747785794,False +2025,Aug-Jan_6m,6,2024-08-01,2025-02-01,primary_only,"LC08,LC09",24,99.99999330925422,92.05031841879324,False +2025,Jul-Dec_6m,6,2025-07-01,2026-01-01,primary_only,"LC08,LC09",9,87.05739782385776,36.64322634288394,False +2025,Feb-Jul_6m,6,2025-02-01,2025-08-01,primary_only,"LC08,LC09",15,99.99500498331224,81.88893838720487,False +2025,May-Oct_6m,6,2025-05-01,2025-11-01,primary_only,"LC08,LC09",8,98.94115641399868,66.10323064380431,False +2025,Apr-Sep_6m,6,2025-04-01,2025-10-01,primary_only,"LC08,LC09",9,99.30587944735944,66.10323064380431,False +2025,Mar-Aug_6m,6,2025-03-01,2025-09-01,primary_only,"LC08,LC09",12,99.8432498425607,74.52738350661544,False +2025,Jun-Nov_6m,6,2025-06-01,2025-12-01,primary_only,"LC08,LC09",5,91.0111234468398,45.1749505747198,False +2025,Nov-Apr_6m,6,2024-11-01,2025-05-01,primary_only,"LC08,LC09",28,99.99999996493946,93.13259747785794,False +2025,Oct-Sep_12m,12,2024-10-01,2025-10-01,primary_only,"LC08,LC09",37,99.99999999963724,93.13259747785794,False +2025,Jan-Dec_12m,12,2025-01-01,2026-01-01,primary_only,"LC08,LC09",28,99.9999807074608,87.09824271688724,False +2025,Feb-Jan_12m,12,2024-02-01,2025-02-01,primary_only,"LC08,LC09",45,99.99999999999996,96.27986432561814,False +2025,Mar-Feb_12m,12,2024-03-01,2025-03-01,primary_only,"LC08,LC09",45,99.99999999999312,93.13259747785794,False +2025,Apr-Mar_12m,12,2024-04-01,2025-04-01,primary_only,"LC08,LC09",42,99.99999999983974,93.13259747785794,False +2025,May-Apr_12m,12,2024-05-01,2025-05-01,primary_only,"LC08,LC09",38,99.99999999855504,93.13259747785794,False +2025,Jun-May_12m,12,2024-06-01,2025-06-01,primary_only,"LC08,LC09",38,99.99999999757246,93.13259747785794,False +2025,Jul-Jun_12m,12,2024-07-01,2025-07-01,primary_only,"LC08,LC09",39,99.9999999996658,93.13259747785794,False +2025,Aug-Jul_12m,12,2024-08-01,2025-08-01,primary_only,"LC08,LC09",39,99.9999999996658,93.13259747785794,False +2025,Sep-Aug_12m,12,2024-09-01,2025-09-01,primary_only,"LC08,LC09",38,99.99999999965172,93.13259747785794,False +2025,Jan-Jun_6m,6,2025-01-01,2025-07-01,primary_only,"LC08,LC09",19,99.99985093770988,87.09824271688724,False +2025,Dec-May_6m,6,2024-12-01,2025-06-01,primary_only,"LC08,LC09",25,99.9999777796364,84.64614702896776,False +2025,Dec-Mar_4m,4,2024-12-01,2025-04-01,primary_only,"LC08,LC09",19,99.99966266158692,84.28845013067622,False +2025,Dec-Feb_3m,3,2024-12-01,2025-03-01,primary_only,"LC08,LC09",16,99.99850619910347,84.28845013067622,False +2025,Oct-Jan_4m,4,2024-10-01,2025-02-01,primary_only,"LC08,LC09",21,99.9999926613995,92.05031841879324,False +2025,Nov-Oct_12m,12,2024-11-01,2025-11-01,primary_only,"LC08,LC09",36,99.99999999962876,93.13259747785794,False +2025,Jan-Mar_3m,3,2025-01-01,2025-04-01,primary_only,"LC08,LC09",11,99.97874861143325,82.78798056834006,False +2025,Feb-Apr_3m,3,2025-02-01,2025-05-01,primary_only,"LC08,LC09",9,99.58521430847968,67.12832615982062,False +2025,Mar-May_3m,3,2025-03-01,2025-06-01,primary_only,"LC08,LC09",9,98.51249496232172,61.27305858515149,False +2025,Apr-Jun_3m,3,2025-04-01,2025-07-01,primary_only,"LC08,LC09",8,99.2985762334982,66.10323064380431,False +2025,May-Jul_3m,3,2025-05-01,2025-08-01,primary_only,"LC08,LC09",6,98.79575964410743,66.10323064380431,False +2025,Jun-Aug_3m,3,2025-06-01,2025-09-01,primary_only,"LC08,LC09",3,89.46220997785885,41.80581205667388,False +2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,"LC08,LC09",1,1.0411985179324068,0.0,False +2025,Aug-Oct_3m,3,2025-08-01,2025-11-01,primary_only,"LC08,LC09",2,12.073733385514185,0.0,False +2025,Sep-Nov_3m,3,2025-09-01,2025-12-01,primary_only,"LC08,LC09",2,14.69865565480517,0.0,False +2025,Oct-Dec_3m,3,2025-10-01,2026-01-01,primary_only,"LC08,LC09",8,86.92122177885553,36.64322634288394,False +2025,Nov-Jan_3m,3,2024-11-01,2025-02-01,primary_only,"LC08,LC09",19,99.99999154731272,92.05031841879324,False +2025,Jan-Apr_4m,4,2025-01-01,2025-05-01,primary_only,"LC08,LC09",13,99.98762188217746,82.78798056834006,False +2025,Feb-May_4m,4,2025-02-01,2025-06-01,primary_only,"LC08,LC09",13,99.95309254940888,77.57867623865769,False +2025,Mar-Jun_4m,4,2025-03-01,2025-07-01,primary_only,"LC08,LC09",11,99.841600590254,74.52738350661544,False +2025,Apr-Jul_4m,4,2025-04-01,2025-08-01,primary_only,"LC08,LC09",8,99.2985762334982,66.10323064380431,False +2025,May-Aug_4m,4,2025-05-01,2025-09-01,primary_only,"LC08,LC09",7,98.80829817684533,66.10323064380431,False +2025,Jun-Sep_4m,4,2025-06-01,2025-10-01,primary_only,"LC08,LC09",3,89.46220997785885,41.80581205667388,False +2025,Jul-Oct_4m,4,2025-07-01,2025-11-01,primary_only,"LC08,LC09",2,12.073733385514185,0.0,False +2025,Aug-Nov_4m,4,2025-08-01,2025-12-01,primary_only,"LC08,LC09",3,15.58681198790377,5.39509836595171,False +2025,Sep-Dec_4m,4,2025-09-01,2026-01-01,primary_only,"LC08,LC09",8,86.92122177885553,36.64322634288394,False +2025,Nov-Feb_4m,4,2024-11-01,2025-03-01,primary_only,"LC08,LC09",23,99.99999973345032,93.13259747785794,False +2025,Dec-Nov_12m,12,2024-12-01,2025-12-01,primary_only,"LC08,LC09",30,99.99999800263896,87.09824271688724,False diff --git a/data/metadata/landsat/compositing_protocol.yaml b/data/metadata/landsat/compositing_protocol.yaml index 6546240..2ab51a6 100644 --- a/data/metadata/landsat/compositing_protocol.yaml +++ b/data/metadata/landsat/compositing_protocol.yaml @@ -1,4 +1,4 @@ -catalog_version: 1 +catalog_version: 2 common_window_name: Jul-Sep_3m quality_mask: mask_fill: true @@ -87,12 +87,33 @@ epochs: earth_engine_asset_ids: - LANDSAT/LC08/C02/T1_L2/LC08_185057_20200805 2025: - window_name: Jul-Sep_3m - window_start: '2025-07-01' - window_end: '2025-10-01' + window_name: Nov-Jan_3m + window_start: '2024-11-01' + window_end: '2025-02-01' sensor_mode: primary_only sensor_keys: - LC08 - scene_count: 1 + - LC09 + scene_count: 19 earth_engine_asset_ids: - - LANDSAT/LC08/C02/T1_L2/LC08_185058_20250819 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241104 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241112 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241112 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241120 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20241120 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241128 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241128 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241206 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20241206 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241214 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241214 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241222 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20241222 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241230 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241230 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20250107 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20250107 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20250123 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20250131 + window_exception: true + exception_reason: "The original common window (Jul-Sep_3m) provided insufficient valid coverage for 2025. The shortest acceptable fallback window (Nov-Jan_3m) was selected using Landsat 8 and Landsat 9. This exception is explicitly documented for reproducibility." diff --git a/data/metadata/landsat/epoch_quality_summary.csv b/data/metadata/landsat/epoch_quality_summary.csv index 85c2577..300cb18 100644 --- a/data/metadata/landsat/epoch_quality_summary.csv +++ b/data/metadata/landsat/epoch_quality_summary.csv @@ -6,4 +6,4 @@ epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_ke 2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True 2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False 2020,Jul-Sep_3m,3,2020-07-01,2020-10-01,primary_only,LC08,1,31.174148193366612,0.0,False -2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False +2025,Nov-Jan_3m,3,2024-11-01,2025-02-01,primary_only,"LC08,LC09",19,99.99999154731272,92.05031841879324,False diff --git a/data/metadata/landsat/monthly_availability.csv b/data/metadata/landsat/monthly_availability.csv index 9e3ac9c..ad91561 100644 --- a/data/metadata/landsat/monthly_availability.csv +++ b/data/metadata/landsat/monthly_availability.csv @@ -1,87 +1,98 @@ epoch,acquisition_month,sensor_key,scene_count,positive_core_scene_count,median_valid_fraction_core,maximum_valid_fraction_core,median_valid_fraction_context,median_cloud_cover_scene_pct -2000,2,LE07,3,2,0.015526905539321192,0.056200007669942736,0.010236236773862048,72.0 -2000,2,LT05,1,1,0.9288684247459292,0.9288684247459292,0.8154552928859043,64.0 -2000,3,LE07,5,4,0.05827231575420734,0.2522056105380081,0.097467415767743,50.0 +2000,2,LE07,3,2,0.0155269055393211,0.0562000076699427,0.010236236773862,72.0 +2000,3,LE07,5,4,0.0582723157542073,0.2522056105380081,0.097467415767743,50.0 2000,4,LE07,3,0,0.0,0.0,0.0,73.0 -2000,5,LE07,5,4,0.019877276796696795,0.9706612313316593,0.04307927662904046,73.0 -2000,6,LE07,2,2,0.14148110877947076,0.16051307407000348,0.15705383101736708,73.5 +2000,5,LE07,5,4,0.0198772767966967,0.9706612313316592,0.0430792766290404,73.0 +2000,6,LE07,2,2,0.1414811087794707,0.1605130740700034,0.157053831017367,73.5 2000,7,LE07,1,0,0.0,0.0,0.0,91.0 -2000,10,LE07,7,1,0.0,0.03152757866190153,0.0,85.0 -2000,11,LE07,4,3,0.04314612284048972,0.20982367244200936,0.07746573027679036,84.5 -2000,12,LE07,10,10,0.06679138722044628,0.7303458139535156,0.10689707045725384,45.5 -2005,1,LE07,7,5,0.104610447783321,0.7363132381306495,0.16798012345349247,39.0 -2005,2,LE07,6,4,0.011245322206550242,0.75419127090601,0.015272967332451202,74.0 -2005,3,LE07,4,4,0.0538154565190576,0.15634049666658387,0.05517342704967254,74.5 -2005,4,LE07,5,3,9.532486951154665e-05,0.7411858705491349,0.01673259712820605,39.0 -2005,5,LE07,2,0,0.0,0.0,0.0004406107889281183,77.5 -2005,7,LE07,1,1,0.05734228027042752,0.05734228027042752,0.09042452341700336,62.0 -2005,8,LE07,1,1,0.007906225044681189,0.007906225044681189,0.015055644178510061,88.0 -2005,10,LE07,1,1,0.08063098790176154,0.08063098790176154,0.06636136718476024,44.0 -2005,11,LE07,7,5,0.11211303951564955,0.7327498463457367,0.09555110036858026,35.0 +2000,10,LE07,7,1,0.0,0.0315275786619015,0.0,85.0 +2000,11,LE07,4,3,0.0431461228404897,0.2098236724420093,0.0774657302767903,84.5 +2000,12,LE07,10,10,0.0667913872204462,0.7303458139535156,0.1068970704572538,45.5 +2000,2,LT05,1,1,0.9288684247459292,0.9288684247459292,0.8154552928859043,64.0 +2005,1,LE07,7,5,0.104610447783321,0.7363132381306495,0.1679801234534924,39.0 +2005,2,LE07,6,4,0.0112453222065502,0.75419127090601,0.0152729673324512,74.0 +2005,3,LE07,4,4,0.0538154565190576,0.1563404966665838,0.0551734270496725,74.5 +2005,4,LE07,5,3,9.532486951154664e-05,0.7411858705491349,0.016732597128206,39.0 +2005,5,LE07,2,0,0.0,0.0,0.0004406107889281,77.5 +2005,7,LE07,1,1,0.0573422802704275,0.0573422802704275,0.0904245234170033,62.0 +2005,8,LE07,1,1,0.0079062250446811,0.0079062250446811,0.01505564417851,88.0 +2005,10,LE07,1,1,0.0806309879017615,0.0806309879017615,0.0663613671847602,44.0 +2005,11,LE07,7,5,0.1121130395156495,0.7327498463457367,0.0955511003685802,35.0 2005,12,LE07,7,3,0.0,0.6059375898868017,0.0,59.0 -2010,1,LE07,7,5,0.08659745048075301,0.7516076546023943,0.16922799355469223,32.0 -2010,2,LE07,6,3,0.0010192416620041906,0.7368480525912148,0.00046996763376655183,77.0 -2010,3,LE07,7,4,0.004551921690065322,0.386779132513751,0.005081226074063892,82.0 -2010,5,LE07,5,3,0.00011791933306923999,0.25206352025917605,0.005085814243104539,84.0 +2010,1,LE07,7,5,0.086597450480753,0.7516076546023943,0.1692279935546922,32.0 +2010,2,LE07,6,3,0.0010192416620041,0.7368480525912148,0.0004699676337665,77.0 +2010,3,LE07,7,4,0.0045519216900653,0.386779132513751,0.0050812260740638,82.0 +2010,5,LE07,5,3,0.0001179193330692,0.252063520259176,0.0050858142431045,84.0 2010,7,LE07,1,0,0.0,0.0,0.0,88.0 -2010,9,LE07,3,3,0.0005397240450286249,0.27034545800423415,0.002566527850955313,62.0 -2010,10,LE07,3,1,0.0,0.033243018610880255,0.0,95.0 -2010,11,LE07,8,6,0.034040334794312004,0.34107751658905344,0.04312038822347471,63.0 -2010,12,LE07,12,10,0.07910465135997324,0.7547161229046729,0.12558609028171683,48.0 -2015,1,LC08,10,9,0.37277776223022074,0.9988759215315823,0.35488069932430844,44.900000000000006 -2015,1,LE07,11,10,0.0838476453409242,0.7355609422231408,0.1674763365724742,38.0 -2015,2,LC08,7,1,0.0,0.11324719198284146,0.0,99.97 -2015,2,LE07,10,6,0.024590421813856497,0.3393084295626647,0.03459090861198448,79.5 -2015,3,LC08,6,4,0.15026728475956908,0.27845060213314593,0.14782354315274063,70.745 -2015,3,LE07,12,7,0.002468477002422559,0.6031369889134685,0.02320419735092974,69.5 +2010,9,LE07,3,3,0.0005397240450286,0.2703454580042341,0.0025665278509553,62.0 +2010,10,LE07,3,1,0.0,0.0332430186108802,0.0,95.0 +2010,11,LE07,8,6,0.034040334794312,0.3410775165890534,0.0431203882234747,63.0 +2010,12,LE07,12,10,0.0791046513599732,0.7547161229046729,0.1255860902817168,48.0 +2015,1,LC08,10,9,0.3727777622302207,0.9988759215315824,0.3548806993243084,44.900000000000006 +2015,2,LC08,7,1,0.0,0.1132471919828414,0.0,99.97 +2015,3,LC08,6,4,0.150267284759569,0.2784506021331459,0.1478235431527406,70.745 2015,4,LC08,7,4,0.3017998345745521,0.693808118101605,0.2574084675715563,71.43 -2015,4,LE07,10,6,0.00527160864561388,0.6020313367719243,0.011583203257682797,84.0 -2015,5,LC08,10,9,0.2869458720924506,0.9490599091442404,0.24839544674381386,36.730000000000004 -2015,5,LE07,10,9,0.10985165436736002,0.6682229037719429,0.1478817878346414,43.0 -2015,6,LC08,3,3,0.003454007653131867,0.01149007082782565,0.012897141628375347,68.28 -2015,6,LE07,4,2,0.012626838945833411,0.13925442501154864,0.021914584992439944,71.0 -2015,7,LC08,3,3,0.37107471373710127,0.5934748983637005,0.3558215331126683,59.16 -2015,8,LC08,2,0,0.0,0.0,0.004814229811755707,84.97999999999999 -2015,8,LE07,2,2,0.004896617593188165,0.009790132058062364,0.0031756405788082183,59.5 -2015,9,LE07,5,2,0.0,0.08691931718200067,0.0013499225091404112,60.0 -2015,10,LC08,7,7,0.038741225847751824,0.21888628475264346,0.12558751874435253,71.44 -2015,10,LE07,8,4,0.011774628723543434,0.18751370199100975,0.02301294774197999,83.5 -2015,11,LC08,8,8,0.27207254643451373,0.6184552701442015,0.2832179848015751,34.0 -2015,11,LE07,6,4,0.007465481532690483,0.20400088958818738,0.005332454261445532,83.5 -2015,12,LC08,9,7,0.159083846460528,0.9915052804394313,0.1989727287040516,66.65 -2015,12,LE07,11,9,0.07410559564034151,0.7570089969168675,0.08412086160198243,42.0 +2015,5,LC08,10,9,0.2869458720924506,0.9490599091442404,0.2483954467438138,36.73 +2015,6,LC08,3,3,0.0034540076531318,0.0114900708278256,0.0128971416283753,68.28 +2015,7,LC08,3,3,0.3710747137371012,0.5934748983637005,0.3558215331126683,59.16 +2015,8,LC08,2,0,0.0,0.0,0.0048142298117557,84.97999999999999 +2015,10,LC08,7,7,0.0387412258477518,0.2188862847526434,0.1255875187443525,71.44 +2015,11,LC08,8,8,0.2720725464345137,0.6184552701442015,0.2832179848015751,34.0 +2015,12,LC08,9,7,0.159083846460528,0.9915052804394312,0.1989727287040516,66.65 +2015,1,LE07,11,10,0.0838476453409242,0.7355609422231408,0.1674763365724742,38.0 +2015,2,LE07,10,6,0.0245904218138564,0.3393084295626647,0.0345909086119844,79.5 +2015,3,LE07,12,7,0.0024684770024225,0.6031369889134685,0.0232041973509297,69.5 +2015,4,LE07,10,6,0.0052716086456138,0.6020313367719243,0.0115832032576827,84.0 +2015,5,LE07,10,9,0.10985165436736,0.6682229037719429,0.1478817878346414,43.0 +2015,6,LE07,4,2,0.0126268389458334,0.1392544250115486,0.0219145849924399,71.0 +2015,8,LE07,2,2,0.0048966175931881,0.0097901320580623,0.0031756405788082,59.5 +2015,9,LE07,5,2,0.0,0.0869193171820006,0.0013499225091404,60.0 +2015,10,LE07,8,4,0.0117746287235434,0.1875137019910097,0.0230129477419799,83.5 +2015,11,LE07,6,4,0.0074654815326904,0.2040008895881873,0.0053324542614455,83.5 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1999-02-01,2,1999,True,64.0,64.0,LANDSAT/LT05/C02/T1_L2,32,LANDSAT/LT05/C02/T1_L2/LT05_185057_19990201,2000,True,False,LT05_L2SP_185057_19990201_20200908_02_T1,LT51850571999032LBG00,-1,LT05,primary,False,LT05_185057_19990201,645602081.8586032,269120107.82026947,0.8154552928859043,0.9288684247459292,185,57 -1999-10-23,10,1999,False,85.0,85.0,LANDSAT/LE07/C02/T1_L2,296,LANDSAT/LE07/C02/T1_L2/LE07_185057_19991023,2000,False,False,LE07_L2SP_185057_19991023_20200918_02_T1,LE71850571999296AGS00,-1,LE07,supplemental,False,LE07_185057_19991023,253303.27934259147,0.0,0.0003199455293741017,0.0,185,57 +1999-10-23,10,1999,False,85.0,85.0,LANDSAT/LE07/C02/T1_L2,296,LANDSAT/LE07/C02/T1_L2/LE07_185057_19991023,2000,False,False,LE07_L2SP_185057_19991023_20200918_02_T1,LE71850571999296AGS00,-1,LE07,supplemental,False,LE07_185057_19991023,253303.27934259147,0.0,0.0003199455293741,0.0,185,57 1999-11-08,11,1999,False,85.0,85.0,LANDSAT/LE07/C02/T1_L2,312,LANDSAT/LE07/C02/T1_L2/LE07_185057_19991108,2000,False,False,LE07_L2SP_185057_19991108_20200918_02_T1,LE71850571999312EDC01,-1,LE07,supplemental,False,LE07_185057_19991108,0.0,0.0,0.0,0.0,185,57 1999-12-10,12,1999,True,36.0,36.0,LANDSAT/LE07/C02/T1_L2,344,LANDSAT/LE07/C02/T1_L2/LE07_185057_19991210,2000,True,False,LE07_L2SP_185057_19991210_20200918_02_T1,LE71850571999344SGS00,-1,LE07,supplemental,False,LE07_185057_19991210,555971966.2848871,211602352.88546336,0.7022441459575246,0.7303458139535156,185,57 -1999-12-10,12,1999,True,65.0,65.0,LANDSAT/LE07/C02/T1_L2,344,LANDSAT/LE07/C02/T1_L2/LE07_185058_19991210,2000,True,False,LE07_L2SP_185058_19991210_20200918_02_T1,LE71850581999344SGS00,-1,LE07,supplemental,False,LE07_185058_19991210,124717965.78750625,20222011.639918666,0.15753035527177775,0.06979630117311644,185,58 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zPTGXixStR*?#a@Rbk?*)(>WWDe~3H-^n;1*Bho0&wnW1dA&v=Zl!uuOXL17wov|&# zQQporOqX>UXmHxwT%5OX0{WliUx`5^zm%DcI0reL&y*9Q5v;6+js$mOhD2+bJPlkL?cr-sZpK_S*o5pA86sKc#bNS`r#hq>1mN zYxy-jx7(npTlIU5N)QcGbN_RF&SvD%sU_q{4^2;wd-Hxb3((iJ@C)B!@gEENuM`$% zJ28VTj4ge=%pFi}SZbhcBFbXxjxFArv;0<+YiM%xBWJlt%V4C5T<>hD%_$?R+UPT< zOFwI4+>f(fQJ#i*_*v*!7+XZztkDX$-irJAtodgTX7e@*}x|8oC-0F?pV Ag#Z8m diff --git a/data/metadata/landsat/selected_scene_manifest.csv b/data/metadata/landsat/selected_scene_manifest.csv index b39f262..eb7efa4 100644 --- a/data/metadata/landsat/selected_scene_manifest.csv +++ b/data/metadata/landsat/selected_scene_manifest.csv @@ -1,8 +1,26 @@ acquisition_date,acquisition_month,acquisition_year,candidate_for_composite,cloud_cover_land_pct,cloud_cover_scene_pct,collection_id,day_of_year,earth_engine_asset_id,epoch,has_positive_core_coverage,landsat7_orbit_drift_period,landsat_product_id,landsat_scene_id,relative_year,sensor_key,sensor_role,slc_off,system_index,valid_area_context_m2,valid_area_core_m2,valid_fraction_context,valid_fraction_core,wrs_path,wrs_row,selected_window_name,selected_sensor_mode -2005-07-19,7,2005,True,62.0,62.0,LANDSAT/LE07/C02/T1_L2,200,LANDSAT/LE07/C02/T1_L2/LE07_185057_20050719,2005,True,False,LE07_L2SP_185057_20050719_20200914_02_T1,LE71850572005200ASN00,0,LE07,supplemental,True,LE07_185057_20050719,71589774.544829,16613720.778869867,0.09042452341700336,0.05734228027042752,185,57,Jul-Sep_3m,primary_plus_supplemental -2005-08-20,8,2005,True,88.0,88.0,LANDSAT/LE07/C02/T1_L2,232,LANDSAT/LE07/C02/T1_L2/LE07_185058_20050820,2005,True,False,LE07_L2SP_185058_20050820_20200914_02_T1,LE71850582005232ASN00,0,LE07,supplemental,True,LE07_185058_20050820,11919666.60853894,2290662.5737201767,0.015055644178510061,0.007906225044681189,185,58,Jul-Sep_3m,primary_plus_supplemental -2010-09-19,9,2010,True,62.0,62.0,LANDSAT/LE07/C02/T1_L2,262,LANDSAT/LE07/C02/T1_L2/LE07_185057_20100919,2010,True,False,LE07_L2SP_185057_20100919_20200911_02_T1,LE71850572010262ASN00,0,LE07,supplemental,True,LE07_185057_20100919,193269507.92430636,78326915.7057634,0.2441173094371267,0.27034545800423415,185,57,Jul-Sep_3m,primary_plus_supplemental +2005-07-19,7,2005,True,62.0,62.0,LANDSAT/LE07/C02/T1_L2,200,LANDSAT/LE07/C02/T1_L2/LE07_185057_20050719,2005,True,False,LE07_L2SP_185057_20050719_20200914_02_T1,LE71850572005200ASN00,0,LE07,supplemental,True,LE07_185057_20050719,71589774.544829,16613720.778869867,0.0904245234170033,0.0573422802704275,185,57,Jul-Sep_3m,primary_plus_supplemental +2005-08-20,8,2005,True,88.0,88.0,LANDSAT/LE07/C02/T1_L2,232,LANDSAT/LE07/C02/T1_L2/LE07_185058_20050820,2005,True,False,LE07_L2SP_185058_20050820_20200914_02_T1,LE71850582005232ASN00,0,LE07,supplemental,True,LE07_185058_20050820,11919666.60853894,2290662.5737201767,0.01505564417851,0.0079062250446811,185,58,Jul-Sep_3m,primary_plus_supplemental +2010-09-19,9,2010,True,62.0,62.0,LANDSAT/LE07/C02/T1_L2,262,LANDSAT/LE07/C02/T1_L2/LE07_185057_20100919,2010,True,False,LE07_L2SP_185057_20100919_20200911_02_T1,LE71850572010262ASN00,0,LE07,supplemental,True,LE07_185057_20100919,193269507.92430636,78326915.7057634,0.2441173094371267,0.2703454580042341,185,57,Jul-Sep_3m,primary_plus_supplemental 2015-07-07,7,2015,True,53.41,53.41,LANDSAT/LC08/C02/T1_L2,188,LANDSAT/LC08/C02/T1_L2/LC08_185057_20150707,2015,True,False,LC08_L2SP_185057_20150707_20200909_02_T1,LC81850572015188LGN01,0,LC08,primary,False,LC08_185057_20150707,387275078.16174865,171946881.15267694,0.4891643338271739,0.5934748983637005,185,57,Jul-Sep_3m,primary_only -2015-07-07,7,2015,True,59.16,59.16,LANDSAT/LC08/C02/T1_L2,188,LANDSAT/LC08/C02/T1_L2/LC08_185058_20150707,2015,True,False,LC08_L2SP_185058_20150707_20200909_02_T1,LC81850582015188LGN01,0,LC08,primary,False,LC08_185058_20150707,182123123.62682796,76347146.40389764,0.23003839252014283,0.2635122815177989,185,58,Jul-Sep_3m,primary_only -2020-08-05,8,2020,True,69.1,69.1,LANDSAT/LC08/C02/T1_L2,218,LANDSAT/LC08/C02/T1_L2/LC08_185057_20200805,2020,True,False,LC08_L2SP_185057_20200805_20200916_02_T1,LC81850572020218LGN00,0,LC08,primary,False,LC08_185057_20200805,261111533.79986328,90320543.787444,0.32980807877458024,0.3117414819336662,185,57,Jul-Sep_3m,primary_only -2025-08-19,8,2025,True,69.29,69.29,LANDSAT/LC08/C02/T1_L2,231,LANDSAT/LC08/C02/T1_L2/LC08_185058_20250819,2025,True,False,LC08_L2SP_185058_20250819_20250826_02_T1,LC81850582025231LGN00,0,LC08,primary,False,LC08_185058_20250819,5203438.217529297,3016653.919363463,0.006572424957914526,0.010411985179324188,185,58,Jul-Sep_3m,primary_only +2015-07-07,7,2015,True,59.16,59.16,LANDSAT/LC08/C02/T1_L2,188,LANDSAT/LC08/C02/T1_L2/LC08_185058_20150707,2015,True,False,LC08_L2SP_185058_20150707_20200909_02_T1,LC81850582015188LGN01,0,LC08,primary,False,LC08_185058_20150707,182123123.62682796,76347146.40389764,0.2300383925201428,0.2635122815177989,185,58,Jul-Sep_3m,primary_only +2020-08-05,8,2020,True,69.1,69.1,LANDSAT/LC08/C02/T1_L2,218,LANDSAT/LC08/C02/T1_L2/LC08_185057_20200805,2020,True,False,LC08_L2SP_185057_20200805_20200916_02_T1,LC81850572020218LGN00,0,LC08,primary,False,LC08_185057_20200805,261111533.79986328,90320543.787444,0.3298080787745802,0.3117414819336662,185,57,Jul-Sep_3m,primary_only +2024-11-04,11,2024,True,84.76,84.76,LANDSAT/LC08/C02/T1_L2,309,LANDSAT/LC08/C02/T1_L2/LC08_185057_20241104,2025,True,False,LC08_L2SP_185057_20241104_20241113_02_T1,LC81850572024309LGN00,-1,LC08,primary,False,LC08_185057_20241104,17573787.579118088,58441.13220214844,0.0221973232431165,0.000201709648709,185,57,Nov-Jan_3m,primary_only +2024-11-12,11,2024,True,3.93,3.93,LANDSAT/LC09/C02/T1_L2,317,LANDSAT/LC09/C02/T1_L2/LC09_185057_20241112,2025,True,False,LC09_L2SP_185057_20241112_20241116_02_T1,LC91850572024317LGN00,-1,LC09,primary,False,LC09_185057_20241112,673529094.333424,252634354.29258657,0.8507296991758005,0.8719678236201476,185,57,Nov-Jan_3m,primary_only +2024-11-12,11,2024,True,8.5,8.5,LANDSAT/LC09/C02/T1_L2,317,LANDSAT/LC09/C02/T1_L2/LC09_185058_20241112,2025,True,False,LC09_L2SP_185058_20241112_20241116_02_T1,LC91850582024317LGN00,-1,LC09,primary,False,LC09_185058_20241112,320085531.191116,108238249.28158824,0.404297705847792,0.3735844672542663,185,58,Nov-Jan_3m,primary_only +2024-11-20,11,2024,True,13.75,13.75,LANDSAT/LC08/C02/T1_L2,325,LANDSAT/LC08/C02/T1_L2/LC08_185057_20241120,2025,True,False,LC08_L2SP_185057_20241120_20241203_02_T1,LC81850572024325LGN00,-1,LC08,primary,False,LC08_185057_20241120,339628518.7849677,101752958.71050283,0.4289823113037272,0.3512004778691,185,57,Nov-Jan_3m,primary_only +2024-11-20,11,2024,True,37.9,37.9,LANDSAT/LC08/C02/T1_L2,325,LANDSAT/LC08/C02/T1_L2/LC08_185058_20241120,2025,True,False,LC08_L2SP_185058_20241120_20241203_02_T1,LC81850582024325LGN00,-1,LC08,primary,False,LC08_185058_20241120,131283079.65087987,29328277.201330572,0.1658227028318637,0.1012265893662074,185,58,Nov-Jan_3m,primary_only +2024-11-28,11,2024,True,0.2,0.2,LANDSAT/LC09/C02/T1_L2,333,LANDSAT/LC09/C02/T1_L2/LC09_185057_20241128,2025,True,False,LC09_L2SP_185057_20241128_20241129_02_T1,LC91850572024333LGN00,-1,LC09,primary,False,LC09_185057_20241128,786697218.076303,287969768.95778453,0.9936715329852976,0.9939280562599422,185,57,Nov-Jan_3m,primary_only +2024-11-28,11,2024,True,18.23,18.23,LANDSAT/LC09/C02/T1_L2,333,LANDSAT/LC09/C02/T1_L2/LC09_185058_20241128,2025,True,False,LC09_L2SP_185058_20241128_20241129_02_T1,LC91850582024333LGN00,-1,LC09,primary,False,LC09_185058_20241128,362288129.4612674,107635292.77569152,0.4576035007018888,0.3715033620393537,185,58,Nov-Jan_3m,primary_only +2024-12-06,12,2024,True,42.65,42.65,LANDSAT/LC08/C02/T1_L2,341,LANDSAT/LC08/C02/T1_L2/LC08_185057_20241206,2025,True,False,LC08_L2SP_185057_20241206_20241210_02_T1,LC81850572024341LGN00,-1,LC08,primary,False,LC08_185057_20241206,388915661.7654075,165041796.49307933,0.4912365430418252,0.5696419891008178,185,57,Nov-Jan_3m,primary_only +2024-12-06,12,2024,True,57.92,57.92,LANDSAT/LC08/C02/T1_L2,341,LANDSAT/LC08/C02/T1_L2/LC08_185058_20241206,2025,True,False,LC08_L2SP_185058_20241206_20241210_02_T1,LC81850582024341LGN00,-1,LC08,primary,False,LC08_185058_20241206,216947395.7646551,80866392.42590548,0.274024677313304,0.2791104654197807,185,58,Nov-Jan_3m,primary_only +2024-12-14,12,2024,True,11.53,11.53,LANDSAT/LC09/C02/T1_L2,349,LANDSAT/LC09/C02/T1_L2/LC09_185057_20241214,2025,True,False,LC09_L2SP_185057_20241214_20241215_02_T1,LC91850572024349LGN00,-1,LC09,primary,False,LC09_185057_20241214,246085814.95849177,85802366.37587148,0.3108292026170907,0.2961469863420814,185,57,Nov-Jan_3m,primary_only +2024-12-14,12,2024,True,43.97,43.97,LANDSAT/LC09/C02/T1_L2,349,LANDSAT/LC09/C02/T1_L2/LC09_185058_20241214,2025,True,False,LC09_L2SP_185058_20241214_20241215_02_T1,LC91850582024349LGN00,-1,LC09,primary,False,LC09_185058_20241214,157140751.02134797,47588149.51458359,0.1984833394271631,0.1642505639367009,185,58,Nov-Jan_3m,primary_only +2024-12-22,12,2024,True,15.12,15.12,LANDSAT/LC08/C02/T1_L2,357,LANDSAT/LC08/C02/T1_L2/LC08_185057_20241222,2025,True,False,LC08_L2SP_185057_20241222_20241228_02_T1,LC81850572024357LGN00,-1,LC08,primary,False,LC08_185057_20241222,314040261.3406173,132486800.1298419,0.3966619694785397,0.4572783740798063,185,57,Nov-Jan_3m,primary_only +2024-12-22,12,2024,True,43.55,43.55,LANDSAT/LC08/C02/T1_L2,357,LANDSAT/LC08/C02/T1_L2/LC08_185058_20241222,2025,True,False,LC08_L2SP_185058_20241222_20241228_02_T1,LC81850582024357LGN00,-1,LC08,primary,False,LC08_185058_20241222,191759666.12631407,70792348.3771561,0.2422102392460754,0.2443398884377281,185,58,Nov-Jan_3m,primary_only +2024-12-30,12,2024,True,27.23,27.23,LANDSAT/LC09/C02/T1_L2,365,LANDSAT/LC09/C02/T1_L2/LC09_185057_20241230,2025,True,False,LC09_L2SP_185057_20241230_20241231_02_T1,LC91850572024365LGN00,-1,LC09,primary,False,LC09_185057_20241230,514943700.5148629,211097593.38664505,0.6504216419411523,0.7286036358444906,185,57,Nov-Jan_3m,primary_only +2024-12-30,12,2024,True,74.08,74.08,LANDSAT/LC09/C02/T1_L2,365,LANDSAT/LC09/C02/T1_L2/LC09_185058_20241230,2025,True,False,LC09_L2SP_185058_20241230_20241231_02_T1,LC91850582024365LGN00,-1,LC09,primary,False,LC09_185058_20241230,166957264.2020843,63313513.46308449,0.2108825058113088,0.2185266793771552,185,58,Nov-Jan_3m,primary_only +2025-01-07,1,2025,True,22.37,22.37,LANDSAT/LC08/C02/T1_L2,7,LANDSAT/LC08/C02/T1_L2/LC08_185057_20250107,2025,True,False,LC08_L2SP_185057_20250107_20250111_02_T1,LC81850572025007LGN00,0,LC08,primary,False,LC08_185057_20250107,708253464.6362598,259485242.19011375,0.8945897986879631,0.8956136726837076,185,57,Nov-Jan_3m,primary_only +2025-01-07,1,2025,True,44.72,44.72,LANDSAT/LC08/C02/T1_L2,7,LANDSAT/LC08/C02/T1_L2/LC08_185058_20250107,2025,True,False,LC08_L2SP_185058_20250107_20250111_02_T1,LC81850582025007LGN00,0,LC08,primary,False,LC08_185058_20250107,379248517.6705928,121156837.6406174,0.4790260436634574,0.4181729928616,185,58,Nov-Jan_3m,primary_only +2025-01-23,1,2025,True,43.04,43.04,LANDSAT/LC08/C02/T1_L2,23,LANDSAT/LC08/C02/T1_L2/LC08_185057_20250123,2025,True,False,LC08_L2SP_185057_20250123_20250128_02_T1,LC81850572025023LGN00,0,LC08,primary,False,LC08_185057_20250123,283837137.63755167,129366981.39311096,0.3585126236547963,0.4465103153905038,185,57,Nov-Jan_3m,primary_only +2025-01-31,1,2025,True,75.0,75.0,LANDSAT/LC09/C02/T1_L2,31,LANDSAT/LC09/C02/T1_L2/LC09_185057_20250131,2025,True,False,LC09_L2SP_185057_20250131_20250201_02_T1,LC91850572025031LGN00,0,LC09,primary,False,LC09_185057_20250131,37971223.876953125,32526141.99942532,0.0479611766409988,0.1122640241443641,185,57,Nov-Jan_3m,primary_only diff --git a/data/metadata/landsat_2025_recovery/candidate_window_details.csv b/data/metadata/landsat_2025_recovery/candidate_window_details.csv new file mode 100644 index 0000000..b04ecf8 --- /dev/null +++ b/data/metadata/landsat_2025_recovery/candidate_window_details.csv @@ -0,0 +1,49 @@ +epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_keys,scene_count,coverage_at_least_1_core_pct,coverage_at_least_3_core_pct,supplemental_sensor_used +2025,Jan-Mar_3m,3,2025-01-01,2025-04-01,primary_only,"LC08,LC09",11,99.97874861143323,82.78798056834006,False +2025,Feb-Apr_3m,3,2025-02-01,2025-05-01,primary_only,"LC08,LC09",9,99.58521430847968,67.12832615982062,False +2025,Mar-May_3m,3,2025-03-01,2025-06-01,primary_only,"LC08,LC09",9,98.51249496232171,61.27305858515149,False +2025,Apr-Jun_3m,3,2025-04-01,2025-07-01,primary_only,"LC08,LC09",8,99.29857623349821,66.10323064380431,False +2025,May-Jul_3m,3,2025-05-01,2025-08-01,primary_only,"LC08,LC09",6,98.79575964410743,66.10323064380431,False +2025,Jun-Aug_3m,3,2025-06-01,2025-09-01,primary_only,"LC08,LC09",3,89.46220997785885,41.80581205667388,False +2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,"LC08,LC09",1,1.0411985179324068,0.0,False +2025,Aug-Oct_3m,3,2025-08-01,2025-11-01,primary_only,"LC08,LC09",2,12.073733385514185,0.0,False +2025,Sep-Nov_3m,3,2025-09-01,2025-12-01,primary_only,"LC08,LC09",2,14.69865565480517,0.0,False +2025,Oct-Dec_3m,3,2025-10-01,2026-01-01,primary_only,"LC08,LC09",8,86.92122177885553,36.64322634288394,False +2025,Nov-Jan_3m,3,2024-11-01,2025-02-01,primary_only,"LC08,LC09",19,99.99999154731272,92.05031841879325,False +2025,Dec-Feb_3m,3,2024-12-01,2025-03-01,primary_only,"LC08,LC09",16,99.99850619910349,84.28845013067622,False +2025,Jan-Apr_4m,4,2025-01-01,2025-05-01,primary_only,"LC08,LC09",13,99.98762188217746,82.78798056834006,False +2025,Feb-May_4m,4,2025-02-01,2025-06-01,primary_only,"LC08,LC09",13,99.95309254940888,77.57867623865769,False +2025,Mar-Jun_4m,4,2025-03-01,2025-07-01,primary_only,"LC08,LC09",11,99.841600590254,74.52738350661544,False +2025,Apr-Jul_4m,4,2025-04-01,2025-08-01,primary_only,"LC08,LC09",8,99.29857623349821,66.10323064380431,False +2025,May-Aug_4m,4,2025-05-01,2025-09-01,primary_only,"LC08,LC09",7,98.80829817684533,66.10323064380431,False +2025,Jun-Sep_4m,4,2025-06-01,2025-10-01,primary_only,"LC08,LC09",3,89.46220997785885,41.80581205667388,False +2025,Jul-Oct_4m,4,2025-07-01,2025-11-01,primary_only,"LC08,LC09",2,12.073733385514185,0.0,False +2025,Aug-Nov_4m,4,2025-08-01,2025-12-01,primary_only,"LC08,LC09",3,15.58681198790377,5.39509836595171,False +2025,Sep-Dec_4m,4,2025-09-01,2026-01-01,primary_only,"LC08,LC09",8,86.92122177885553,36.64322634288394,False +2025,Oct-Jan_4m,4,2024-10-01,2025-02-01,primary_only,"LC08,LC09",21,99.9999926613995,92.05031841879325,False +2025,Nov-Feb_4m,4,2024-11-01,2025-03-01,primary_only,"LC08,LC09",23,99.99999973345031,93.13259747785794,False +2025,Dec-Mar_4m,4,2024-12-01,2025-04-01,primary_only,"LC08,LC09",19,99.99966266158692,84.28845013067622,False +2025,Jan-Jun_6m,6,2025-01-01,2025-07-01,primary_only,"LC08,LC09",19,99.99985093770988,87.09824271688724,False +2025,Feb-Jul_6m,6,2025-02-01,2025-08-01,primary_only,"LC08,LC09",15,99.99500498331224,81.88893838720487,False +2025,Mar-Aug_6m,6,2025-03-01,2025-09-01,primary_only,"LC08,LC09",12,99.8432498425607,74.52738350661544,False +2025,Apr-Sep_6m,6,2025-04-01,2025-10-01,primary_only,"LC08,LC09",9,99.30587944735944,66.10323064380431,False +2025,May-Oct_6m,6,2025-05-01,2025-11-01,primary_only,"LC08,LC09",8,98.94115641399867,66.10323064380431,False +2025,Jun-Nov_6m,6,2025-06-01,2025-12-01,primary_only,"LC08,LC09",5,91.01112344683979,45.1749505747198,False +2025,Jul-Dec_6m,6,2025-07-01,2026-01-01,primary_only,"LC08,LC09",9,87.05739782385776,36.64322634288394,False +2025,Aug-Jan_6m,6,2024-08-01,2025-02-01,primary_only,"LC08,LC09",24,99.99999330925422,92.05031841879325,False +2025,Sep-Feb_6m,6,2024-09-01,2025-03-01,primary_only,"LC08,LC09",26,99.99999977781617,93.13259747785794,False +2025,Oct-Mar_6m,6,2024-10-01,2025-04-01,primary_only,"LC08,LC09",28,99.99999994773997,93.13259747785794,False +2025,Nov-Apr_6m,6,2024-11-01,2025-05-01,primary_only,"LC08,LC09",28,99.99999996493946,93.13259747785794,False +2025,Dec-May_6m,6,2024-12-01,2025-06-01,primary_only,"LC08,LC09",25,99.99997777963641,84.64614702896776,False +2025,Jan-Dec_12m,12,2025-01-01,2026-01-01,primary_only,"LC08,LC09",28,99.99998070746081,87.09824271688724,False +2025,Feb-Jan_12m,12,2024-02-01,2025-02-01,primary_only,"LC08,LC09",45,99.99999999999997,96.27986432561814,False +2025,Mar-Feb_12m,12,2024-03-01,2025-03-01,primary_only,"LC08,LC09",45,99.99999999999312,93.13259747785794,False +2025,Apr-Mar_12m,12,2024-04-01,2025-04-01,primary_only,"LC08,LC09",42,99.99999999983974,93.13259747785794,False +2025,May-Apr_12m,12,2024-05-01,2025-05-01,primary_only,"LC08,LC09",38,99.99999999855503,93.13259747785794,False +2025,Jun-May_12m,12,2024-06-01,2025-06-01,primary_only,"LC08,LC09",38,99.99999999757246,93.13259747785794,False +2025,Jul-Jun_12m,12,2024-07-01,2025-07-01,primary_only,"LC08,LC09",39,99.9999999996658,93.13259747785794,False +2025,Aug-Jul_12m,12,2024-08-01,2025-08-01,primary_only,"LC08,LC09",39,99.9999999996658,93.13259747785794,False +2025,Sep-Aug_12m,12,2024-09-01,2025-09-01,primary_only,"LC08,LC09",38,99.99999999965172,93.13259747785794,False +2025,Oct-Sep_12m,12,2024-10-01,2025-10-01,primary_only,"LC08,LC09",37,99.99999999963725,93.13259747785794,False +2025,Nov-Oct_12m,12,2024-11-01,2025-11-01,primary_only,"LC08,LC09",36,99.99999999962877,93.13259747785794,False +2025,Dec-Nov_12m,12,2024-12-01,2025-12-01,primary_only,"LC08,LC09",30,99.99999800263895,87.09824271688724,False diff --git a/data/metadata/landsat_2025_recovery/candidate_window_summary.csv b/data/metadata/landsat_2025_recovery/candidate_window_summary.csv new file mode 100644 index 0000000..4c2b014 --- /dev/null +++ b/data/metadata/landsat_2025_recovery/candidate_window_summary.csv @@ -0,0 +1,49 @@ +window_name,number_of_months,minimum_epoch_coverage_1_pct,minimum_epoch_coverage_3_pct,median_epoch_coverage_3_pct,total_scene_count +Apr-Jul_4m,4,99.29857623349821,66.10323064380431,66.10323064380431,8 +Apr-Jun_3m,3,99.29857623349821,66.10323064380431,66.10323064380431,8 +Apr-Mar_12m,12,99.99999999983974,93.13259747785794,93.13259747785794,42 +Apr-Sep_6m,6,99.30587944735944,66.10323064380431,66.10323064380431,9 +Aug-Jan_6m,6,99.99999330925422,92.05031841879325,92.05031841879325,24 +Aug-Jul_12m,12,99.9999999996658,93.13259747785794,93.13259747785794,39 +Aug-Nov_4m,4,15.58681198790377,5.39509836595171,5.39509836595171,3 +Aug-Oct_3m,3,12.073733385514185,0.0,0.0,2 +Dec-Feb_3m,3,99.99850619910349,84.28845013067622,84.28845013067622,16 +Dec-Mar_4m,4,99.99966266158692,84.28845013067622,84.28845013067622,19 +Dec-May_6m,6,99.99997777963641,84.64614702896776,84.64614702896776,25 +Dec-Nov_12m,12,99.99999800263895,87.09824271688724,87.09824271688724,30 +Feb-Apr_3m,3,99.58521430847968,67.12832615982062,67.12832615982062,9 +Feb-Jan_12m,12,99.99999999999997,96.27986432561814,96.27986432561814,45 +Feb-Jul_6m,6,99.99500498331224,81.88893838720487,81.88893838720487,15 +Feb-May_4m,4,99.95309254940888,77.57867623865769,77.57867623865769,13 +Jan-Apr_4m,4,99.98762188217746,82.78798056834006,82.78798056834006,13 +Jan-Dec_12m,12,99.99998070746081,87.09824271688724,87.09824271688724,28 +Jan-Jun_6m,6,99.99985093770988,87.09824271688724,87.09824271688724,19 +Jan-Mar_3m,3,99.97874861143323,82.78798056834006,82.78798056834006,11 +Jul-Dec_6m,6,87.05739782385776,36.64322634288394,36.64322634288394,9 +Jul-Jun_12m,12,99.9999999996658,93.13259747785794,93.13259747785794,39 +Jul-Oct_4m,4,12.073733385514185,0.0,0.0,2 +Jul-Sep_3m,3,1.0411985179324068,0.0,0.0,1 +Jun-Aug_3m,3,89.46220997785885,41.80581205667388,41.80581205667388,3 +Jun-May_12m,12,99.99999999757246,93.13259747785794,93.13259747785794,38 +Jun-Nov_6m,6,91.01112344683979,45.1749505747198,45.1749505747198,5 +Jun-Sep_4m,4,89.46220997785885,41.80581205667388,41.80581205667388,3 +Mar-Aug_6m,6,99.8432498425607,74.52738350661544,74.52738350661544,12 +Mar-Feb_12m,12,99.99999999999312,93.13259747785794,93.13259747785794,45 +Mar-Jun_4m,4,99.841600590254,74.52738350661544,74.52738350661544,11 +Mar-May_3m,3,98.51249496232171,61.27305858515149,61.27305858515149,9 +May-Apr_12m,12,99.99999999855503,93.13259747785794,93.13259747785794,38 +May-Aug_4m,4,98.80829817684533,66.10323064380431,66.10323064380431,7 +May-Jul_3m,3,98.79575964410743,66.10323064380431,66.10323064380431,6 +May-Oct_6m,6,98.94115641399867,66.10323064380431,66.10323064380431,8 +Nov-Apr_6m,6,99.99999996493946,93.13259747785794,93.13259747785794,28 +Nov-Feb_4m,4,99.99999973345031,93.13259747785794,93.13259747785794,23 +Nov-Jan_3m,3,99.99999154731272,92.05031841879325,92.05031841879325,19 +Nov-Oct_12m,12,99.99999999962877,93.13259747785794,93.13259747785794,36 +Oct-Dec_3m,3,86.92122177885553,36.64322634288394,36.64322634288394,8 +Oct-Jan_4m,4,99.9999926613995,92.05031841879325,92.05031841879325,21 +Oct-Mar_6m,6,99.99999994773997,93.13259747785794,93.13259747785794,28 +Oct-Sep_12m,12,99.99999999963725,93.13259747785794,93.13259747785794,37 +Sep-Aug_12m,12,99.99999999965172,93.13259747785794,93.13259747785794,38 +Sep-Dec_4m,4,86.92122177885553,36.64322634288394,36.64322634288394,8 +Sep-Feb_6m,6,99.99999977781617,93.13259747785794,93.13259747785794,26 +Sep-Nov_3m,3,14.69865565480517,0.0,0.0,2 diff --git a/data/metadata/landsat_2025_recovery/catalog_version.json b/data/metadata/landsat_2025_recovery/catalog_version.json new file mode 100644 index 0000000..852de0a --- /dev/null +++ b/data/metadata/landsat_2025_recovery/catalog_version.json @@ -0,0 +1,7 @@ +{ + "catalog_version": 2, + "config_sha256": "6927b65b52ebc55eeb3a4693f1a872056154158a352cdd6e505e0232cceb8068", + "selected_scene_manifest_sha256": "a67d0a509641d457dbb997e0d960faa020c442199293792b8836e54bcae190cb", + "compositing_protocol_sha256": "41d84fb83fbcc99324dd61ba439fd9365c911247cb4a5841aa933eeb3f1671de", + "selected_scene_count": 19 +} \ No newline at end of file diff --git a/data/metadata/landsat_2025_recovery/compositing_protocol.yaml b/data/metadata/landsat_2025_recovery/compositing_protocol.yaml new file mode 100644 index 0000000..39a7d3a --- /dev/null +++ b/data/metadata/landsat_2025_recovery/compositing_protocol.yaml @@ -0,0 +1,46 @@ +catalog_version: 2 +common_window_name: Nov-Jan_3m +quality_mask: + mask_fill: true + mask_dilated_cloud: true + mask_cirrus_landsat8: true + mask_cloud: true + mask_cloud_shadow: true + mask_snow: true + mask_radiometric_saturation: true + mask_water: false +selection_thresholds: + minimum_coverage_one_observation_pct: 95.0 + preferred_coverage_three_observations_pct: 80.0 + select_shortest_acceptable_window: true + coverage_comparison_tolerance_pct: 0.5 +epochs: + 2025: + window_name: Nov-Jan_3m + window_start: '2024-11-01' + window_end: '2025-02-01' + sensor_mode: primary_only + sensor_keys: + - LC08 + - LC09 + scene_count: 19 + earth_engine_asset_ids: + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241104 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241112 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241112 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241120 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20241120 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241128 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241128 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241206 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20241206 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241214 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241214 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20241222 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20241222 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20241230 + - LANDSAT/LC09/C02/T1_L2/LC09_185058_20241230 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20250107 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20250107 + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20250123 + - LANDSAT/LC09/C02/T1_L2/LC09_185057_20250131 diff --git a/data/metadata/landsat_2025_recovery/epoch_quality_summary.csv b/data/metadata/landsat_2025_recovery/epoch_quality_summary.csv new file mode 100644 index 0000000..00ea868 --- /dev/null +++ b/data/metadata/landsat_2025_recovery/epoch_quality_summary.csv @@ -0,0 +1,2 @@ +epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_keys,scene_count,coverage_at_least_1_core_pct,coverage_at_least_3_core_pct,supplemental_sensor_used +2025,Nov-Jan_3m,3,2024-11-01,2025-02-01,primary_only,"LC08,LC09",19,99.99999154731272,92.05031841879325,False diff --git a/data/metadata/landsat_2025_recovery/monthly_availability.csv b/data/metadata/landsat_2025_recovery/monthly_availability.csv new file mode 100644 index 0000000..a1241b9 --- /dev/null +++ b/data/metadata/landsat_2025_recovery/monthly_availability.csv @@ -0,0 +1,23 @@ +epoch,acquisition_month,sensor_key,scene_count,positive_core_scene_count,median_valid_fraction_core,maximum_valid_fraction_core,median_valid_fraction_context,median_cloud_cover_scene_pct +2025,1,LC08,11,9,0.1995697098742528,0.8956136726837076,0.216970263179735,46.85 +2025,1,LC09,9,5,0.11226402414436412,0.888123340997233,0.047961176640998825,75.0 +2025,1,LE07,4,1,0.0,0.2036311664801989,5.44973710009e-05,57.5 +2025,2,LC08,10,7,0.2411079854507498,0.998854200824894,0.2353623981916319,42.345 +2025,2,LC09,6,3,0.13292665966442962,0.6772911129436356,0.11285268472754796,40.519999999999996 +2025,3,LC08,10,6,0.0348898633648286,0.6835895489744053,0.0538213183270177,52.730000000000004 +2025,3,LC09,7,6,0.36815792522790547,0.879957818655098,0.3550810829109508,58.8 +2025,4,LC08,6,4,0.24390875850995286,0.6972430741614988,0.24292428976788127,62.455 +2025,4,LC09,9,6,0.1005779243941857,0.6600389909912651,0.070350464978786,63.14 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b/data/metadata/landsat_2025_recovery/selected_scene_manifest.csv @@ -0,0 +1,20 @@ +acquisition_date,acquisition_month,acquisition_year,candidate_for_composite,cloud_cover_land_pct,cloud_cover_scene_pct,collection_id,day_of_year,earth_engine_asset_id,epoch,has_positive_core_coverage,landsat7_orbit_drift_period,landsat_product_id,landsat_scene_id,relative_year,sensor_key,sensor_role,slc_off,system_index,valid_area_context_m2,valid_area_core_m2,valid_fraction_context,valid_fraction_core,wrs_path,wrs_row,selected_window_name,selected_sensor_mode +2024-11-04,11,2024,True,84.76,84.76,LANDSAT/LC08/C02/T1_L2,309,LANDSAT/LC08/C02/T1_L2/LC08_185057_20241104,2025,True,False,LC08_L2SP_185057_20241104_20241113_02_T1,LC81850572024309LGN00,-1,LC08,primary,False,LC08_185057_20241104,17573787.579118088,58441.13220214844,0.0221973232431165,0.000201709648709,185,57,Nov-Jan_3m,primary_only 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+2024-12-14,12,2024,True,43.97,43.97,LANDSAT/LC09/C02/T1_L2,349,LANDSAT/LC09/C02/T1_L2/LC09_185058_20241214,2025,True,False,LC09_L2SP_185058_20241214_20241215_02_T1,LC91850582024349LGN00,-1,LC09,primary,False,LC09_185058_20241214,157140751.02134797,47588149.51458359,0.1984833394271631,0.1642505639367009,185,58,Nov-Jan_3m,primary_only +2024-12-22,12,2024,True,15.12,15.12,LANDSAT/LC08/C02/T1_L2,357,LANDSAT/LC08/C02/T1_L2/LC08_185057_20241222,2025,True,False,LC08_L2SP_185057_20241222_20241228_02_T1,LC81850572024357LGN00,-1,LC08,primary,False,LC08_185057_20241222,314040261.3406173,132486800.1298419,0.3966619694785397,0.4572783740798063,185,57,Nov-Jan_3m,primary_only +2024-12-22,12,2024,True,43.55,43.55,LANDSAT/LC08/C02/T1_L2,357,LANDSAT/LC08/C02/T1_L2/LC08_185058_20241222,2025,True,False,LC08_L2SP_185058_20241222_20241228_02_T1,LC81850582024357LGN00,-1,LC08,primary,False,LC08_185058_20241222,191759666.12631407,70792348.3771561,0.2422102392460754,0.2443398884377281,185,58,Nov-Jan_3m,primary_only +2024-12-30,12,2024,True,27.23,27.23,LANDSAT/LC09/C02/T1_L2,365,LANDSAT/LC09/C02/T1_L2/LC09_185057_20241230,2025,True,False,LC09_L2SP_185057_20241230_20241231_02_T1,LC91850572024365LGN00,-1,LC09,primary,False,LC09_185057_20241230,514943700.5148629,211097593.38664505,0.6504216419411523,0.7286036358444906,185,57,Nov-Jan_3m,primary_only +2024-12-30,12,2024,True,74.08,74.08,LANDSAT/LC09/C02/T1_L2,365,LANDSAT/LC09/C02/T1_L2/LC09_185058_20241230,2025,True,False,LC09_L2SP_185058_20241230_20241231_02_T1,LC91850582024365LGN00,-1,LC09,primary,False,LC09_185058_20241230,166957264.2020843,63313513.46308449,0.2108825058113088,0.21852667937715523,185,58,Nov-Jan_3m,primary_only +2025-01-07,1,2025,True,22.37,22.37,LANDSAT/LC08/C02/T1_L2,7,LANDSAT/LC08/C02/T1_L2/LC08_185057_20250107,2025,True,False,LC08_L2SP_185057_20250107_20250111_02_T1,LC81850572025007LGN00,0,LC08,primary,False,LC08_185057_20250107,708253464.6362598,259485242.19011375,0.8945897986879631,0.8956136726837076,185,57,Nov-Jan_3m,primary_only +2025-01-07,1,2025,True,44.72,44.72,LANDSAT/LC08/C02/T1_L2,7,LANDSAT/LC08/C02/T1_L2/LC08_185058_20250107,2025,True,False,LC08_L2SP_185058_20250107_20250111_02_T1,LC81850582025007LGN00,0,LC08,primary,False,LC08_185058_20250107,379248517.6705928,121156837.6406174,0.4790260436634574,0.4181729928616,185,58,Nov-Jan_3m,primary_only +2025-01-23,1,2025,True,43.04,43.04,LANDSAT/LC08/C02/T1_L2,23,LANDSAT/LC08/C02/T1_L2/LC08_185057_20250123,2025,True,False,LC08_L2SP_185057_20250123_20250128_02_T1,LC81850572025023LGN00,0,LC08,primary,False,LC08_185057_20250123,283837137.63755167,129366981.39311096,0.3585126236547963,0.4465103153905038,185,57,Nov-Jan_3m,primary_only +2025-01-31,1,2025,True,75.0,75.0,LANDSAT/LC09/C02/T1_L2,31,LANDSAT/LC09/C02/T1_L2/LC09_185057_20250131,2025,True,False,LC09_L2SP_185057_20250131_20250201_02_T1,LC91850572025031LGN00,0,LC09,primary,False,LC09_185057_20250131,37971223.876953125,32526141.99942532,0.047961176640998825,0.11226402414436412,185,57,Nov-Jan_3m,primary_only diff --git a/data/metadata/landsat_before_2025_recovery/candidate_window_details.csv b/data/metadata/landsat_before_2025_recovery/candidate_window_details.csv new file mode 100644 index 0000000..e0761a6 --- /dev/null +++ b/data/metadata/landsat_before_2025_recovery/candidate_window_details.csv @@ -0,0 +1,577 @@ +epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_keys,scene_count,coverage_at_least_1_core_pct,coverage_at_least_3_core_pct,supplemental_sensor_used +1990,Jan-Mar_3m,3,1990-01-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Mar_3m,3,1995-01-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",4,20.214509718071028,7.108844196338966,True +2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_only,LC08,3,99.95200756228057,55.84014934025937,False +2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",12,99.9983916925276,77.91912842227305,True +2020,Jan-Mar_3m,3,2020-01-01,2020-04-01,primary_only,LC08,12,99.9999889269216,92.4627230944063,False +2025,Jan-Mar_3m,3,2025-01-01,2025-04-01,primary_only,LC08,9,99.97331931309324,82.78798056832599,False +1990,Feb-Apr_3m,3,1990-02-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Apr_3m,3,1995-02-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True +2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_only,LC08,3,66.60550893983303,30.373115138961243,False +2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",11,96.61199834332788,52.721554256203554,True +2020,Feb-Apr_3m,3,2020-02-01,2020-05-01,primary_only,LC08,10,99.99197718965614,83.1191679886652,False +2025,Feb-Apr_3m,3,2025-02-01,2025-05-01,primary_only,LC08,6,99.20631169511765,67.12832615980658,False +1990,Mar-May_3m,3,1990-03-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-May_3m,3,1995-03-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353795,44.263437174874525,True +2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True +2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False +2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108565,69.94945631243047,True +2020,Mar-May_3m,3,2020-03-01,2020-06-01,primary_only,LC08,10,97.53274785282866,54.14122230543774,False +2025,Mar-May_3m,3,2025-03-01,2025-06-01,primary_only,LC08,4,95.98010283732056,60.34000438856134,False +1990,Apr-Jun_3m,3,1990-04-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Jun_3m,3,1995-04-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",1,8.681343174190491,0.0,True +2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_only,LC08,6,97.54262569620018,55.898040761197564,False +2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",12,99.40227487869481,63.41647525357126,True +2020,Apr-Jun_3m,3,2020-04-01,2020-07-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False +2025,Apr-Jun_3m,3,2025-04-01,2025-07-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,May-Jul_3m,3,1990-05-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Jul_3m,3,1995-05-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",1,5.734228027042754,0.0,True +2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False +2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True +2020,May-Jul_3m,3,2020-05-01,2020-08-01,primary_only,LC08,5,52.298947894275784,17.898937105226143,False +2025,May-Jul_3m,3,2025-05-01,2025-08-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,Jun-Aug_3m,3,1990-06-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Aug_3m,3,1995-06-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True +2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",2,70.05992553901368,0.0,True +2020,Jun-Aug_3m,3,2020-06-01,2020-09-01,primary_only,LC08,2,39.44042763503558,0.0,False +2025,Jun-Aug_3m,3,2025-06-01,2025-09-01,primary_only,LC08,1,1.041198517932418,0.0,False +1990,Jul-Sep_3m,3,1990-07-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Sep_3m,3,1995-07-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True +2020,Jul-Sep_3m,3,2020-07-01,2020-10-01,primary_only,LC08,1,31.174148193366612,0.0,False +2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False +1990,Aug-Oct_3m,3,1990-08-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Oct_3m,3,1995-08-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",2,8.789972621051634,0.0,True +2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_only,LC08,0,0.0,0.0,False +2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",3,8.655065766282677,2.935682513385204,True +2020,Aug-Oct_3m,3,2020-08-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2025,Aug-Oct_3m,3,2025-08-01,2025-11-01,primary_only,LC08,2,12.073733385514196,0.0,False +1990,Sep-Nov_3m,3,1990-09-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Nov_3m,3,1995-09-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",4,81.04892728307274,32.53886464324526,True +2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False +2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True +2020,Sep-Nov_3m,3,2020-09-01,2020-12-01,primary_only,LC08,3,25.049198118928206,8.907243651425132,False +2025,Sep-Nov_3m,3,2025-09-01,2025-12-01,primary_only,LC08,2,14.69865565480517,0.0,False +1990,Oct-Dec_3m,3,1990-10-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Dec_3m,3,1995-10-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230215,48.999682988950326,True +2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",6,77.06529687299054,28.833652379980872,True +2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False +2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",16,99.99661400937033,81.62494116586562,True +2020,Oct-Dec_3m,3,2020-10-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False +2025,Oct-Dec_3m,3,2025-10-01,2026-01-01,primary_only,LC08,5,57.48063060201698,20.584371624624833,False +1990,Nov-Jan_3m,3,1989-11-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Jan_3m,3,1994-11-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Jan_3m,3,1999-11-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Jan_3m,3,1999-11-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",4,84.02771858544709,39.686868140752516,True +2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_only,LC08,6,99.99622271947547,80.038023561852,False +2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",14,99.99970147171831,83.94154596448331,True +2020,Nov-Jan_3m,3,2019-11-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False +2025,Nov-Jan_3m,3,2024-11-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False +1990,Dec-Feb_3m,3,1989-12-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Feb_3m,3,1994-12-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Feb_3m,3,1999-12-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Feb_3m,3,1999-12-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.686868140752516,True +2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_only,LC08,4,99.98661468043085,74.20328925979955,False +2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",13,99.99917716066042,83.94154596448331,True +2020,Dec-Feb_3m,3,2019-12-01,2020-03-01,primary_only,LC08,12,99.9999966096206,92.4627230944063,False +2025,Dec-Feb_3m,3,2024-12-01,2025-03-01,primary_only,LC08,11,99.98651246303923,78.98972857254677,False +1990,Jan-Apr_4m,4,1990-01-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Apr_4m,4,1995-01-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Apr_4m,4,2000-01-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Apr_4m,4,2000-01-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True +2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_only,LC08,5,99.97910683045646,60.62558215383077,False +2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",17,99.99975039988183,77.91912842227305,True +2020,Jan-Apr_4m,4,2020-01-01,2020-05-01,primary_only,LC08,14,99.9999968100652,92.4627230944063,False +2025,Jan-Apr_4m,4,2025-01-01,2025-05-01,primary_only,LC08,9,99.97331931309324,82.78798056832599,False +1990,Feb-May_4m,4,1990-02-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-May_4m,4,1995-02-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353795,44.263437174874525,True +2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True +2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False +2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",18,99.86951234815521,69.94945631243047,True +2020,Feb-May_4m,4,2020-02-01,2020-06-01,primary_only,LC08,14,99.99565066080038,83.1191679886652,False +2025,Feb-May_4m,4,2025-02-01,2025-06-01,primary_only,LC08,8,99.87323530155264,77.57867623864364,False +1990,Mar-Jun_4m,4,1990-03-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Jun_4m,4,1995-03-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True +2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False +2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108565,69.94945631243047,True +2020,Mar-Jun_4m,4,2020-03-01,2020-07-01,primary_only,LC08,11,97.8290753978749,54.14122230543774,False +2025,Mar-Jun_4m,4,2025-03-01,2025-07-01,primary_only,LC08,4,95.98010283732056,60.34000438856134,False +1990,Apr-Jul_4m,4,1990-04-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Jul_4m,4,1995-04-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",2,13.917763187815046,0.0,True +2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False +2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",14,99.8210406536092,69.6273866274382,True +2020,Apr-Jul_4m,4,2020-04-01,2020-08-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False +2025,Apr-Jul_4m,4,2025-04-01,2025-08-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,May-Aug_4m,4,1990-05-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Aug_4m,4,1995-05-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Aug_4m,4,2000-05-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Aug_4m,4,2000-05-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False +2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True +2020,May-Aug_4m,4,2020-05-01,2020-09-01,primary_only,LC08,6,67.16934456750927,24.28684380484538,False +2025,May-Aug_4m,4,2025-05-01,2025-09-01,primary_only,LC08,3,84.19469891213093,37.900752262065645,False +1990,Jun-Sep_4m,4,1990-06-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Sep_4m,4,1995-06-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Sep_4m,4,2000-06-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Sep_4m,4,2000-06-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True +2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True +2020,Jun-Sep_4m,4,2020-06-01,2020-10-01,primary_only,LC08,2,39.44042763503558,0.0,False +2025,Jun-Sep_4m,4,2025-06-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False +1990,Jul-Oct_4m,4,1990-07-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Oct_4m,4,1995-07-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Oct_4m,4,2000-07-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Oct_4m,4,2000-07-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True +2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",5,72.65125867408607,30.716739720027615,True +2020,Jul-Oct_4m,4,2020-07-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2025,Jul-Oct_4m,4,2025-07-01,2025-11-01,primary_only,LC08,2,12.073733385514196,0.0,False +1990,Aug-Nov_4m,4,1990-08-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Nov_4m,4,1995-08-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Nov_4m,4,2000-08-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Nov_4m,4,2000-08-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",5,81.19875872881087,32.53886464324526,True +2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False +2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True +2020,Aug-Nov_4m,4,2020-08-01,2020-12-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2025,Aug-Nov_4m,4,2025-08-01,2025-12-01,primary_only,LC08,3,15.586811987903781,5.395098365951714,False +1990,Sep-Dec_4m,4,1990-09-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Dec_4m,4,1995-09-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Dec_4m,4,2000-09-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Dec_4m,4,2000-09-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230215,48.999682988950326,True +2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True +2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False +2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",17,99.99661403038459,81.62494116586562,True +2020,Sep-Dec_4m,4,2020-09-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False +2025,Sep-Dec_4m,4,2025-09-01,2026-01-01,primary_only,LC08,5,57.48063060201698,20.584371624624833,False +1990,Oct-Jan_4m,4,1989-10-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Jan_4m,4,1994-10-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Jan_4m,4,1999-10-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Jan_4m,4,1999-10-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.686868140752516,True +2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",20,99.99985260336734,83.94154596448331,True +2020,Oct-Jan_4m,4,2019-10-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False +2025,Oct-Jan_4m,4,2024-10-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False +1990,Nov-Feb_4m,4,1989-11-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Feb_4m,4,1994-11-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Feb_4m,4,1999-11-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Feb_4m,4,1999-11-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Nov-Feb_4m,4,2004-11-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Feb_4m,4,2004-11-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.686868140752516,True +2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_only,LC08,6,99.99622271947547,80.038023561852,False +2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",16,99.9997773111493,83.94154596448331,True +2020,Nov-Feb_4m,4,2019-11-01,2020-03-01,primary_only,LC08,16,99.99999811029484,92.4627230944063,False +2025,Nov-Feb_4m,4,2024-11-01,2025-03-01,primary_only,LC08,14,99.9921366831703,78.98972857254677,False +1990,Dec-Mar_4m,4,1989-12-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Mar_4m,4,1994-12-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Dec-Mar_4m,4,2004-12-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Mar_4m,4,2004-12-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_only,LC08,5,99.98973232638939,74.20328925979955,False +2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",17,99.99975919252638,83.94154596448331,True +2020,Dec-Mar_4m,4,2019-12-01,2020-04-01,primary_only,LC08,16,99.99999946438633,92.4627230944063,False +2025,Dec-Mar_4m,4,2024-12-01,2025-04-01,primary_only,LC08,13,99.99660531685394,82.78798056832599,False +1990,Jan-Jun_6m,6,1990-01-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Jun_6m,6,1995-01-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Jan-Jun_6m,6,2005-01-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Jun_6m,6,2005-01-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True +2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_only,LC08,9,99.99882064616772,77.8534842100577,False +2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",24,99.99999038674221,87.5917409147415,True +2020,Jan-Jun_6m,6,2020-01-01,2020-07-01,primary_only,LC08,19,99.99999847836753,92.4627230944063,False +2025,Jan-Jun_6m,6,2025-01-01,2025-07-01,primary_only,LC08,11,99.9957386681783,84.64614702896075,False +1990,Feb-Jul_6m,6,1990-02-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Jul_6m,6,1995-02-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Feb-Jul_6m,6,2005-02-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Jul_6m,6,2005-02-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",5,22.83854293048695,8.021730615521188,True +2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False +2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",20,99.96093189987528,69.94945631243047,True +2020,Feb-Jul_6m,6,2020-02-01,2020-08-01,primary_only,LC08,15,99.99617303505754,83.1191679886652,False +2025,Feb-Jul_6m,6,2025-02-01,2025-08-01,primary_only,LC08,8,99.87323530155264,77.57867623864364,False +1990,Mar-Aug_6m,6,1990-03-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Aug_6m,6,1995-03-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Mar-Aug_6m,6,2005-03-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Aug_6m,6,2005-03-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",5,22.9194008930627,8.021730615521188,True +2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False +2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",18,99.9476267771708,69.94945631243047,True +2020,Mar-Aug_6m,6,2020-03-01,2020-09-01,primary_only,LC08,12,98.50584265050763,54.14122230543774,False +2025,Mar-Aug_6m,6,2025-03-01,2025-09-01,primary_only,LC08,5,96.02195794700079,60.34000438856134,False +1990,Apr-Sep_6m,6,1990-04-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Sep_6m,6,1995-04-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,Apr-Sep_6m,6,2005-04-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Sep_6m,6,2005-04-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",3,14.598348724401722,5.0687312352337885,True +2010,Apr-Sep_6m,6,2010-04-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Sep_6m,6,2010-04-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False +2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",15,99.82104176427299,69.6273866274382,True +2020,Apr-Sep_6m,6,2020-04-01,2020-10-01,primary_only,LC08,8,90.54213772277048,40.58143388086358,False +2025,Apr-Sep_6m,6,2025-04-01,2025-10-01,primary_only,LC08,3,84.19469891213093,37.900752262065645,False +1990,May-Oct_6m,6,1990-05-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Oct_6m,6,1995-05-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True +2010,May-Oct_6m,6,2010-05-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Oct_6m,6,2010-05-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False +2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",12,98.94667317251714,63.13126063996379,True +2020,May-Oct_6m,6,2020-05-01,2020-11-01,primary_only,LC08,9,75.39316049053653,25.146863455003754,False +2025,May-Oct_6m,6,2025-05-01,2025-11-01,primary_only,LC08,4,85.95677093334616,41.26989078011156,False +1990,Jun-Nov_6m,6,1990-06-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Nov_6m,6,1995-06-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True +2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",6,82.27686477521533,32.53886464324526,True +2010,Jun-Nov_6m,6,2010-06-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Nov_6m,6,2010-06-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_only,LC08,6,92.56824893242367,45.63231976158158,False +2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",11,93.31279853448552,45.63231976158158,True +2020,Jun-Nov_6m,6,2020-06-01,2020-12-01,primary_only,LC08,5,54.61011489671122,20.251522816828125,False +2025,Jun-Nov_6m,6,2025-06-01,2025-12-01,primary_only,LC08,3,15.586811987903781,5.395098365951714,False +1990,Jul-Dec_6m,6,1990-07-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Dec_6m,6,1995-07-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",8,93.75585813242849,48.999682988950326,True +2010,Jul-Dec_6m,6,2010-07-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Dec_6m,6,2010-07-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True +2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_only,LC08,10,99.98186760213181,69.57196223718582,False +2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",19,99.99898623817593,81.62494116586562,True +2020,Jul-Dec_6m,6,2020-07-01,2021-01-01,primary_only,LC08,6,57.53070893529786,20.155114847508894,False +2025,Jul-Dec_6m,6,2025-07-01,2026-01-01,primary_only,LC08,6,57.923341646023005,20.584371624624833,False +1990,Aug-Jan_6m,6,1989-08-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Jan_6m,6,1994-08-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2010,Aug-Jan_6m,6,2009-08-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Jan_6m,6,2009-08-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.686868140752516,True +2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",22,99.99985404685276,83.94154596448331,True +2020,Aug-Jan_6m,6,2019-08-01,2020-02-01,primary_only,LC08,13,99.99910847793507,76.43072751898961,False +2025,Aug-Jan_6m,6,2024-08-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False +1990,Sep-Feb_6m,6,1989-09-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Feb_6m,6,1994-09-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2010,Sep-Feb_6m,6,2009-09-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Feb_6m,6,2009-09-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",6,84.59016229601333,39.686868140752516,True +2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",22,99.99989004865289,83.94154596448331,True +2020,Sep-Feb_6m,6,2019-09-01,2020-03-01,primary_only,LC08,17,99.99999842840064,92.4627230944063,False +2025,Sep-Feb_6m,6,2024-09-01,2025-03-01,primary_only,LC08,14,99.9921366831703,78.98972857254677,False +1990,Oct-Mar_6m,6,1989-10-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Mar_6m,6,1994-10-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2010,Oct-Mar_6m,6,2009-10-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Mar_6m,6,2009-10-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_only,LC08,11,99.9981944418267,80.038023561852,False +2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",26,99.99996782226512,83.94154596448331,True +2020,Oct-Mar_6m,6,2019-10-01,2020-04-01,primary_only,LC08,20,99.99999970146352,92.4627230944063,False +2025,Oct-Mar_6m,6,2024-10-01,2025-04-01,primary_only,LC08,16,99.99802087889053,82.78798056832599,False +1990,Nov-Apr_6m,6,1989-11-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Apr_6m,6,1994-11-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_only,LC08,9,99.99873859639293,80.038023561852,False +2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",25,99.99998988585686,83.94154596448331,True +2020,Nov-Apr_6m,6,2019-11-01,2020-05-01,primary_only,LC08,22,99.99999991399756,92.4627230944063,False +2025,Nov-Apr_6m,6,2024-11-01,2025-05-01,primary_only,LC08,16,99.99802087889053,82.78798056832599,False +1990,Dec-May_6m,6,1989-12-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-May_6m,6,1994-12-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552893,65.10708852743944,True +2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Dec-May_6m,6,2009-12-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-May_6m,6,2009-12-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2015,Dec-May_6m,6,2014-12-01,2015-06-01,primary_only,LC08,11,99.99974768482708,89.20002667996266,False +2015,Dec-May_6m,6,2014-12-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",29,99.99999856063324,89.20002667996266,True +2020,Dec-May_6m,6,2019-12-01,2020-06-01,primary_only,LC08,22,99.99999991635079,92.4627230944063,False +2025,Dec-May_6m,6,2024-12-01,2025-06-01,primary_only,LC08,15,99.9994578148844,84.64614702896075,False +1990,Jan-Dec_12m,12,1990-01-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Dec_12m,12,1995-01-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",11,99.1500794403698,52.0061067316496,True +2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",13,95.45057829972708,48.999682988950326,True +2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",12,96.4234233774342,55.456614464889256,True +2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_only,LC08,19,99.99999978615487,96.12321885528449,False +2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",43,99.99999999990254,96.12321885528449,True +2020,Jan-Dec_12m,12,2020-01-01,2021-01-01,primary_only,LC08,25,99.99999935377349,92.4627230944063,False +2025,Jan-Dec_12m,12,2025-01-01,2026-01-01,primary_only,LC08,17,99.99820697396805,84.64614702896075,False +1990,Feb-Jan_12m,12,1989-02-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Jan_12m,12,1994-02-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_only,LT05,1,92.88684247459292,0.0,False +2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",3,98.21578307322392,57.6336846624187,True +2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",6,90.67826640340226,44.45034468183726,True +2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",12,98.36361505477768,61.833501818017,True +2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_only,LC08,20,99.99998288037484,91.05817819518664,False +2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",43,99.99999993373655,91.05817819518664,True +2020,Feb-Jan_12m,12,2019-02-01,2020-02-01,primary_only,LC08,18,99.9996394421078,76.43072751898961,False +2025,Feb-Jan_12m,12,2024-02-01,2025-02-01,primary_only,LC08,18,99.99998192456317,82.13699542031398,False +1990,Mar-Feb_12m,12,1989-03-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Feb_12m,12,1994-03-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",5,57.44590265932441,22.97408918804021,True +2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",11,93.21865856027992,50.16453781543488,True +2015,Mar-Feb_12m,12,2014-03-01,2015-03-01,primary_only,LC08,20,99.99998288037484,91.05817819518664,False +2015,Mar-Feb_12m,12,2014-03-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",43,99.99999992303607,91.05817819518664,True +2020,Mar-Feb_12m,12,2019-03-01,2020-03-01,primary_only,LC08,20,99.99999875162852,92.4627230944063,False +2025,Mar-Feb_12m,12,2024-03-01,2025-03-01,primary_only,LC08,20,99.99916078896021,78.98972857254677,False +1990,Apr-Mar_12m,12,1989-04-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Mar_12m,12,1994-04-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True +2015,Apr-Mar_12m,12,2014-04-01,2015-04-01,primary_only,LC08,18,99.9999747937986,91.05817819518664,False +2015,Apr-Mar_12m,12,2014-04-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",42,99.99999993787675,91.05817819518664,True +2020,Apr-Mar_12m,12,2019-04-01,2020-04-01,primary_only,LC08,23,99.99999978896516,92.4627230944063,False +2025,Apr-Mar_12m,12,2024-04-01,2025-04-01,primary_only,LC08,20,99.99941834568435,82.78798056832599,False +1990,May-Apr_12m,12,1989-05-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Apr_12m,12,1994-05-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True +2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_only,LC08,20,99.99998902665786,91.05817819518664,False +2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",46,99.99999999014055,91.05817819518664,True +2020,May-Apr_12m,12,2019-05-01,2020-05-01,primary_only,LC08,25,99.99999993920503,92.4627230944063,False +2025,May-Apr_12m,12,2024-05-01,2025-05-01,primary_only,LC08,18,99.99876965319511,82.78798056832599,False +1990,Jun-May_12m,12,1989-06-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-May_12m,12,1994-06-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552893,65.10708852743944,True +2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Jun-May_12m,12,2014-06-01,2015-06-01,primary_only,LC08,20,99.99997220174258,89.20002667996266,False +2015,Jun-May_12m,12,2014-06-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",45,99.99999989988848,89.20002667996266,True +2020,Jun-May_12m,12,2019-06-01,2020-06-01,primary_only,LC08,27,99.99999996122469,92.4627230944063,False +2025,Jun-May_12m,12,2024-06-01,2025-06-01,primary_only,LC08,19,99.99975550478729,84.64614702896075,False +1990,Jul-Jun_12m,12,1989-07-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Jun_12m,12,1994-07-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Jul-Jun_12m,12,2014-07-01,2015-07-01,primary_only,LC08,18,99.99997209501109,89.20002667996266,False +2015,Jul-Jun_12m,12,2014-07-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",41,99.99999988022066,89.20002667996266,True +2020,Jul-Jun_12m,12,2019-07-01,2020-07-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False +2025,Jul-Jun_12m,12,2024-07-01,2025-07-01,primary_only,LC08,18,99.99968390274987,84.64614702896075,False +1990,Aug-Jul_12m,12,1989-08-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Jul_12m,12,1994-08-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",7,60.36951364729075,20.979279514264185,True +2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False +2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",42,99.99999994297887,89.20002667996266,True +2020,Aug-Jul_12m,12,2019-08-01,2020-08-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False +2025,Aug-Jul_12m,12,2024-08-01,2025-08-01,primary_only,LC08,18,99.99968390274987,84.64614702896075,False +1990,Sep-Aug_12m,12,1989-09-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Aug_12m,12,1994-09-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True +2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Sep-Aug_12m,12,2014-09-01,2015-09-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False +2015,Sep-Aug_12m,12,2014-09-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",40,99.99999994241495,89.20002667996266,True +2020,Sep-Aug_12m,12,2019-09-01,2020-09-01,primary_only,LC08,29,99.99999997651783,92.4627230944063,False +2025,Sep-Aug_12m,12,2024-09-01,2025-09-01,primary_only,LC08,19,99.99968719394975,84.64614702896075,False +1990,Oct-Sep_12m,12,1989-10-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Sep_12m,12,1994-10-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True +2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",9,91.63102270855705,46.28341533178432,True +2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False +2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",41,99.9999999424153,89.20002667996266,True +2020,Oct-Sep_12m,12,2019-10-01,2020-10-01,primary_only,LC08,28,99.99999997176484,92.4627230944063,False +2025,Oct-Sep_12m,12,2024-10-01,2025-10-01,primary_only,LC08,19,99.99968719394975,84.64614702896075,False +1990,Nov-Oct_12m,12,1989-11-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Oct_12m,12,1994-11-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",9,63.8530225472835,20.979279514264185,True +2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",8,91.34324607677755,46.28341533178432,True +2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_only,LC08,15,99.9999786819862,89.20002667996266,False +2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",37,99.99999989346502,89.20002667996266,True +2020,Nov-Oct_12m,12,2019-11-01,2020-11-01,primary_only,LC08,31,99.99999997883752,92.4627230944063,False +2025,Nov-Oct_12m,12,2024-11-01,2025-11-01,primary_only,LC08,20,99.99972206748909,84.64614702896075,False +1990,Dec-Nov_12m,12,1989-12-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Nov_12m,12,1994-12-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",12,92.54897664388146,42.90104908880774,True +2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",10,91.97785219851266,46.28341533178432,True +2015,Dec-Nov_12m,12,2014-12-01,2015-12-01,primary_only,LC08,17,99.99998124856444,89.20002667996266,False +2015,Dec-Nov_12m,12,2014-12-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",40,99.99999990374664,89.20002667996266,True +2020,Dec-Nov_12m,12,2019-12-01,2020-12-01,primary_only,LC08,27,99.99999996203172,92.4627230944063,False +2025,Dec-Nov_12m,12,2024-12-01,2025-12-01,primary_only,LC08,18,99.999542324259,84.64614702896075,False diff --git a/data/metadata/landsat_before_2025_recovery/candidate_window_summary.csv b/data/metadata/landsat_before_2025_recovery/candidate_window_summary.csv new file mode 100644 index 0000000..63ddb99 --- /dev/null +++ b/data/metadata/landsat_before_2025_recovery/candidate_window_summary.csv @@ -0,0 +1,49 @@ +window_name,number_of_months,minimum_epoch_coverage_1_pct,minimum_epoch_coverage_3_pct,median_epoch_coverage_3_pct,total_scene_count +Apr-Jul_4m,4,0.0,0.0,0.0,17 +Apr-Jun_3m,3,0.0,0.0,0.0,15 +Apr-Mar_12m,12,0.0,0.0,0.0,61 +Apr-Sep_6m,6,0.0,0.0,0.0,19 +Aug-Jan_6m,6,0.0,0.0,0.0,33 +Aug-Jul_12m,12,0.0,0.0,0.0,65 +Aug-Nov_4m,4,0.0,0.0,0.0,11 +Aug-Oct_3m,3,0.0,0.0,0.0,6 +Dec-Feb_3m,3,0.0,0.0,0.0,27 +Dec-Mar_4m,4,0.0,0.0,0.0,34 +Dec-May_6m,6,0.0,0.0,0.0,48 +Dec-Nov_12m,12,0.0,0.0,0.0,62 +Feb-Apr_3m,3,0.0,0.0,0.0,19 +Feb-Jan_12m,12,0.0,0.0,0.0,57 +Feb-Jul_6m,6,0.0,0.0,0.0,32 +Feb-May_4m,4,0.0,0.0,0.0,29 +Jan-Apr_4m,4,0.0,0.0,0.0,28 +Jan-Dec_12m,12,0.0,0.0,0.0,61 +Jan-Jun_6m,6,0.0,0.0,0.0,39 +Jan-Mar_3m,3,0.0,0.0,0.0,24 +Jul-Dec_6m,6,0.0,0.0,0.0,22 +Jul-Jun_12m,12,0.0,0.0,0.0,64 +Jul-Oct_4m,4,0.0,0.0,0.0,8 +Jul-Sep_3m,3,0.0,0.0,0.0,4 +Jun-Aug_3m,3,0.0,0.0,0.0,5 +Jun-May_12m,12,0.0,0.0,0.0,66 +Jun-Nov_6m,6,0.0,0.0,0.0,14 +Jun-Sep_4m,4,0.0,0.0,0.0,5 +Mar-Aug_6m,6,0.0,0.0,0.0,26 +Mar-Feb_12m,12,0.0,0.0,0.0,60 +Mar-Jun_4m,4,0.0,0.0,0.0,22 +Mar-May_3m,3,0.0,0.0,0.0,21 +May-Apr_12m,12,0.0,0.0,0.0,63 +May-Aug_4m,4,0.0,0.0,0.0,15 +May-Jul_3m,3,0.0,0.0,0.0,13 +May-Oct_6m,6,0.0,0.0,0.0,19 +Nov-Apr_6m,6,0.0,0.0,0.0,47 +Nov-Feb_4m,4,0.0,0.0,0.0,36 +Nov-Jan_3m,3,0.0,0.0,0.0,28 +Nov-Oct_12m,12,0.0,0.0,0.0,66 +Oct-Dec_3m,3,0.0,0.0,0.0,18 +Oct-Jan_4m,4,0.0,0.0,0.0,32 +Oct-Mar_6m,6,0.0,0.0,0.0,47 +Oct-Sep_12m,12,0.0,0.0,0.0,66 +Sep-Aug_12m,12,0.0,0.0,0.0,67 +Sep-Dec_4m,4,0.0,0.0,0.0,18 +Sep-Feb_6m,6,0.0,0.0,0.0,41 +Sep-Nov_3m,3,0.0,0.0,0.0,9 diff --git a/data/metadata/landsat_before_2025_recovery/catalog_version.json b/data/metadata/landsat_before_2025_recovery/catalog_version.json new file mode 100644 index 0000000..56347d1 --- /dev/null +++ b/data/metadata/landsat_before_2025_recovery/catalog_version.json @@ -0,0 +1,7 @@ +{ + "catalog_version": 1, + "config_sha256": "197a0d9faf8fa3819efb068bd4a3dbe815fa29cf036c5c1d22404baaed51dd0f", + "selected_scene_manifest_sha256": "2325905e823c14352a4f29cea3652d62c1d15ad81a293bda73d826acf2dce321", + "compositing_protocol_sha256": "a31dee916b03f507d961be8586623ea01e7c8db8f9395a95638ee7eac973059c", + "selected_scene_count": 7 +} \ No newline at end of file diff --git a/data/metadata/landsat_before_2025_recovery/compositing_protocol.yaml b/data/metadata/landsat_before_2025_recovery/compositing_protocol.yaml new file mode 100644 index 0000000..6546240 --- /dev/null +++ b/data/metadata/landsat_before_2025_recovery/compositing_protocol.yaml @@ -0,0 +1,98 @@ +catalog_version: 1 +common_window_name: Jul-Sep_3m +quality_mask: + mask_fill: true + mask_dilated_cloud: true + mask_cirrus_landsat8: true + mask_cloud: true + mask_cloud_shadow: true + mask_snow: true + mask_radiometric_saturation: true + mask_water: false +selection_thresholds: + minimum_coverage_one_observation_pct: 95.0 + preferred_coverage_three_observations_pct: 80.0 + select_shortest_acceptable_window: true + coverage_comparison_tolerance_pct: 0.5 +epochs: + 1990: + window_name: Jul-Sep_3m + window_start: '1990-07-01' + window_end: '1990-10-01' + sensor_mode: primary_only + sensor_keys: + - LT05 + scene_count: 0 + earth_engine_asset_ids: [] + 1995: + window_name: Jul-Sep_3m + window_start: '1995-07-01' + window_end: '1995-10-01' + sensor_mode: primary_only + sensor_keys: + - LT05 + scene_count: 0 + earth_engine_asset_ids: [] + 2000: + window_name: Jul-Sep_3m + window_start: '2000-07-01' + window_end: '2000-10-01' + sensor_mode: primary_only + sensor_keys: + - LT05 + scene_count: 0 + earth_engine_asset_ids: [] + 2005: + window_name: Jul-Sep_3m + window_start: '2005-07-01' + window_end: '2005-10-01' + sensor_mode: primary_plus_supplemental + sensor_keys: + - LT05 + - LE07 + scene_count: 2 + earth_engine_asset_ids: + - LANDSAT/LE07/C02/T1_L2/LE07_185057_20050719 + - LANDSAT/LE07/C02/T1_L2/LE07_185058_20050820 + 2010: + window_name: Jul-Sep_3m + window_start: '2010-07-01' + window_end: '2010-10-01' + sensor_mode: primary_plus_supplemental + sensor_keys: + - LT05 + - LE07 + scene_count: 1 + earth_engine_asset_ids: + - LANDSAT/LE07/C02/T1_L2/LE07_185057_20100919 + 2015: + window_name: Jul-Sep_3m + window_start: '2015-07-01' + window_end: '2015-10-01' + sensor_mode: primary_only + sensor_keys: + - LC08 + scene_count: 2 + earth_engine_asset_ids: + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20150707 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20150707 + 2020: + window_name: Jul-Sep_3m + window_start: '2020-07-01' + window_end: '2020-10-01' + sensor_mode: primary_only + sensor_keys: + - LC08 + scene_count: 1 + earth_engine_asset_ids: + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20200805 + 2025: + window_name: Jul-Sep_3m + window_start: '2025-07-01' + window_end: '2025-10-01' + sensor_mode: primary_only + sensor_keys: + - LC08 + scene_count: 1 + earth_engine_asset_ids: + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20250819 diff --git a/data/metadata/landsat_before_2025_recovery/epoch_quality_summary.csv b/data/metadata/landsat_before_2025_recovery/epoch_quality_summary.csv new file mode 100644 index 0000000..85c2577 --- /dev/null +++ b/data/metadata/landsat_before_2025_recovery/epoch_quality_summary.csv @@ -0,0 +1,9 @@ +epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_keys,scene_count,coverage_at_least_1_core_pct,coverage_at_least_3_core_pct,supplemental_sensor_used +1990,Jul-Sep_3m,3,1990-07-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Sep_3m,3,1995-07-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2020,Jul-Sep_3m,3,2020-07-01,2020-10-01,primary_only,LC08,1,31.174148193366612,0.0,False +2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False diff --git a/data/metadata/landsat_before_2025_recovery/monthly_availability.csv b/data/metadata/landsat_before_2025_recovery/monthly_availability.csv new file mode 100644 index 0000000..9e3ac9c --- /dev/null +++ b/data/metadata/landsat_before_2025_recovery/monthly_availability.csv @@ -0,0 +1,87 @@ +epoch,acquisition_month,sensor_key,scene_count,positive_core_scene_count,median_valid_fraction_core,maximum_valid_fraction_core,median_valid_fraction_context,median_cloud_cover_scene_pct +2000,2,LE07,3,2,0.015526905539321192,0.056200007669942736,0.010236236773862048,72.0 +2000,2,LT05,1,1,0.9288684247459292,0.9288684247459292,0.8154552928859043,64.0 +2000,3,LE07,5,4,0.05827231575420734,0.2522056105380081,0.097467415767743,50.0 +2000,4,LE07,3,0,0.0,0.0,0.0,73.0 +2000,5,LE07,5,4,0.019877276796696795,0.9706612313316593,0.04307927662904046,73.0 +2000,6,LE07,2,2,0.14148110877947076,0.16051307407000348,0.15705383101736708,73.5 +2000,7,LE07,1,0,0.0,0.0,0.0,91.0 +2000,10,LE07,7,1,0.0,0.03152757866190153,0.0,85.0 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b/reports/built_up_candidates/built_up_candidates_report.md @@ -5,8 +5,8 @@ - Completed epochs: 2005, 2010, 2015, 2020, 2025 - Epoch count: 5 - Continuous index layers: 35 -- Binary candidate maps: 25 -- Epoch-specific Otsu thresholds: 25 +- Binary candidate maps: 20 +- Epoch-specific Otsu thresholds: 20 The binary maps are unvalidated candidate pseudo-labels. No index has been selected as the final historical built-up mapping method. @@ -14,31 +14,26 @@ The binary maps are unvalidated candidate pseudo-labels. No index has been selec | Epoch | Index | Threshold | Valid pixels | Built fraction | Status | |---:|---|---:|---:|---:|:---:| -| 2005 | ibi | -10299.646149 | 19571 | 0.9999 | PASS | | 2005 | ibui | -0.171993 | 19571 | 0.2248 | PASS | | 2005 | ndbi | -0.226702 | 19571 | 0.2641 | PASS | | 2005 | ndbsui | -0.119221 | 19571 | 0.3520 | PASS | | 2005 | vbswir1_bi | 0.437588 | 19571 | 0.7116 | PASS | -| 2010 | ibi | -2175.721497 | 87129 | 0.9999 | PASS | | 2010 | ibui | -0.046867 | 87129 | 0.4162 | PASS | | 2010 | ndbi | -0.148432 | 87129 | 0.4430 | PASS | | 2010 | ndbsui | -0.064452 | 87129 | 0.4841 | PASS | | 2010 | vbswir1_bi | 0.589782 | 87129 | 0.5198 | PASS | -| 2015 | ibi | -158813.410156 | 191830 | 1.0000 | PASS | | 2015 | ibui | -0.054694 | 191831 | 0.4849 | PASS | | 2015 | ndbi | -0.140577 | 191831 | 0.5058 | PASS | | 2015 | ndbsui | -0.066386 | 191794 | 0.5882 | PASS | | 2015 | vbswir1_bi | 0.991259 | 191831 | 0.0093 | PASS | -| 2020 | ibi | -62098.378906 | 100467 | 1.0000 | PASS | | 2020 | ibui | -0.000003 | 100468 | 0.4665 | PASS | | 2020 | ndbi | -0.113236 | 100468 | 0.5041 | PASS | | 2020 | ndbsui | -0.054702 | 100451 | 0.5619 | PASS | | 2020 | vbswir1_bi | -46.593643 | 100468 | 1.0000 | PASS | -| 2025 | ibi | 4715.648560 | 3356 | 0.0006 | PASS | -| 2025 | ibui | 0.202918 | 3356 | 0.6518 | PASS | -| 2025 | ndbi | 0.000176 | 3356 | 0.7176 | PASS | -| 2025 | ndbsui | -0.016548 | 3356 | 0.7590 | PASS | -| 2025 | vbswir1_bi | 0.726627 | 3356 | 0.3855 | PASS | +| 2025 | ibui | 0.078073 | 321917 | 0.6119 | PASS | +| 2025 | ndbi | -0.082012 | 321917 | 0.6236 | PASS | +| 2025 | ndbsui | -0.042983 | 321917 | 0.6531 | PASS | +| 2025 | vbswir1_bi | 0.640531 | 321917 | 0.3969 | PASS | ## Interpretation limits @@ -46,4 +41,5 @@ The binary maps are unvalidated candidate pseudo-labels. 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z5wYhfqFappUKNkJLAvP#FxcyE8yKJ@rwbwb5zlL(Z7L3T3Ls14l!@`med{gMuaces zF2whOEs+xmQa%w-@hWF}y$fQp20D;6G-^bC>H{RE3n;i`c~{w^c8{m*Ihm*`09@*%pVC{D zk`aantg${4AUqV1B*U^ti-F22k|y{2RepbXU-s}nKKUP$S^ZDRx&F()bH(CE;4P>6 VkKt-8y2$M-%WKFL-n None: + """Ensure YAML index roles match the implemented pipeline schema.""" + configured_continuous = list( + config["indices"]["continuous"] + ) + configured_candidates = list( + config["indices"]["candidates"] + ) + + if configured_continuous != CONTINUOUS_INDICES: + raise ValueError( + "Configured continuous indices do not match the implemented " + f"schema: {configured_continuous} != {CONTINUOUS_INDICES}" + ) + + if configured_candidates != CANDIDATE_INDICES: + raise ValueError( + "Configured candidate indices do not match the implemented " + f"schema: {configured_candidates} != {CANDIDATE_INDICES}" + ) + + if not set(configured_candidates).issubset( + configured_continuous + ): + raise ValueError( + "Every candidate index must also be retained as a " + "continuous index." + ) + + if "ibi" in configured_candidates: + raise ValueError( + "IBI must remain excluded from candidate generation." + ) + + expected_directions = set(CANDIDATE_INDICES) + configured_directions = set( + config["classification"]["built_up_direction"] + ) + missing_directions = ( + expected_directions - configured_directions + ) + unexpected_directions = ( + configured_directions - expected_directions + ) + if missing_directions or unexpected_directions: + raise ValueError( + "Classification directions do not match candidate indices. " + f"Missing: {sorted(missing_directions)}. " + f"Unexpected: {sorted(unexpected_directions)}. " + f"Expected: {sorted(expected_directions)}." + ) + + def load_ee_geometry(path: Path) -> ee.Geometry: """Load one dissolved vector boundary as a WGS84 Earth Engine geometry.""" frame = gpd.read_file(path) @@ -225,6 +283,7 @@ def run_preflight(config_path: Path) -> dict[str, Any]: """Validate local dependencies and completed source assets.""" project_root = find_project_root(config_path.parent) config = load_yaml(config_path) + validate_index_configuration(config) inputs = config["inputs"] paths = { name: resolve_project_path(value, project_root) @@ -296,8 +355,12 @@ def run_preflight(config_path: Path) -> dict[str, Any]: "status": "PASS", "completed_epochs": completed["epoch"].tolist(), "epoch_count": epoch_count, - "continuous_index_layers": epoch_count * 7, - "binary_candidate_maps": epoch_count * 5, + "continuous_index_layers": ( + epoch_count * len(CONTINUOUS_INDEX_BANDS) + ), + "binary_candidate_maps": ( + epoch_count * len(CANDIDATE_INDICES) + ), "expected_export_tasks": epoch_count * 2, "grid_sha256": stable_object_hash(grid), "orchestration_stable_signature": orchestration_version.get( @@ -333,7 +396,11 @@ def calculate_histograms( grid: dict[str, Any], config: dict[str, Any], ) -> dict[str, Any]: - """Calculate all five selected histograms with one reduction per epoch.""" + """Calculate diagnostic histograms with one reduction per epoch. + + IBI remains in the histogram archive for transparency, but Otsu threshold + calculation later iterates only over ``CANDIDATE_INDICES``. + """ minimum = int( config["classification"][ "minimum_valid_observations_for_threshold" @@ -342,14 +409,14 @@ def calculate_histograms( threshold_mask = count_image.select( "valid_observation_count" ).gte(minimum) - selected_stack = ee.Image.cat( + diagnostic_stack = ee.Image.cat( [ index_images[name].updateMask(threshold_mask) - for name in SELECTED_INDICES + for name in CONTINUOUS_INDICES ] - ).select(SELECTED_INDICES) + ).select(CONTINUOUS_INDICES) - return selected_stack.reduceRegion( + return diagnostic_stack.reduceRegion( reducer=histogram_reducer(config), geometry=core_geometry, crs=grid["crs"], @@ -370,7 +437,7 @@ def candidate_area_statistics( one = ee.Image.constant(1) bands: list[ee.Image] = [] - for name in SELECTED_INDICES: + for name in CANDIDATE_INDICES: built = candidate_image.select(f"built_{name}") valid = candidate_image.select(f"valid_{name}").eq(1) @@ -653,7 +720,7 @@ def build_all_epoch_bundles( epoch_threshold_rows: list[dict[str, Any]] = [] epoch_failures: list[dict[str, Any]] = [] - for index_name in SELECTED_INDICES: + for index_name in CANDIDATE_INDICES: result = otsu_from_histogram(histograms.get(index_name)) epoch_threshold_rows.append( threshold_row( @@ -783,9 +850,10 @@ def write_pre_export_metadata( "minimum_valid_observations_for_candidate": config[ "classification" ]["minimum_valid_observations_for_candidate"], - "classification_directions": config["classification"][ - "built_up_direction" - ], + "classification_directions": { + name: config["classification"]["built_up_direction"][name] + for name in CANDIDATE_INDICES + }, "pooled_thresholds_computed": False, } write_json(recipe_path, recipe) @@ -934,7 +1002,7 @@ def submit_exports( { **index_task, "band_names": ",".join(INDEX_BANDS), - "continuous_index_layers": 7, + "continuous_index_layers": len(CONTINUOUS_INDEX_BANDS), "binary_candidate_layers": 0, } ) @@ -964,7 +1032,7 @@ def submit_exports( **candidate_task, "band_names": ",".join(CANDIDATE_BANDS), "continuous_index_layers": 0, - "binary_candidate_layers": 5, + "binary_candidate_layers": len(CANDIDATE_INDICES), } ) diff --git a/src/analysis/built_up/finalize.py b/src/analysis/built_up/finalize.py index 3cb7713..4af46d2 100644 --- a/src/analysis/built_up/finalize.py +++ b/src/analysis/built_up/finalize.py @@ -37,8 +37,10 @@ ) from src.analysis.built_up.indices import ( CANDIDATE_BANDS, + CANDIDATE_INDICES, + CONTINUOUS_INDICES, + CONTINUOUS_INDEX_BANDS, INDEX_BANDS, - SELECTED_INDICES, ) from src.analysis.built_up.otsu import histogram_quantile @@ -168,7 +170,7 @@ def validate_candidate_domain_and_masks( image = ee.Image(asset_id) checks: list[ee.Image] = [] - for name in SELECTED_INDICES: + for name in CANDIDATE_INDICES: built = image.select(f"built_{name}") valid = image.select(f"valid_{name}") checks.extend( @@ -189,7 +191,7 @@ def validate_candidate_domain_and_masks( tileScale=int(config["histogram"]["tile_scale"]), ).getInfo() - for name in SELECTED_INDICES: + for name in CANDIDATE_INDICES: minimum = values.get(f"{name}_built_min") maximum = values.get(f"{name}_built_max") mismatch = values.get(f"{name}_mask_mismatch_max", 0) @@ -255,13 +257,13 @@ def create_histogram_gallery( epochs = sorted(int(value) for value in histograms) figure, axes = plt.subplots( len(epochs), - len(SELECTED_INDICES), + len(CANDIDATE_INDICES), figsize=(18, 3.2 * len(epochs)), squeeze=False, ) for row_index, epoch in enumerate(epochs): - for column_index, name in enumerate(SELECTED_INDICES): + for column_index, name in enumerate(CANDIDATE_INDICES): axis = axes[row_index, column_index] histogram = histograms[str(epoch)][name] means = np.asarray(histogram["bucketMeans"], dtype=float) @@ -294,7 +296,7 @@ def index_display_ranges( """Calculate robust per-index display ranges shared across all epochs.""" ranges: dict[str, tuple[float, float]] = {} - for name in SELECTED_INDICES: + for name in CONTINUOUS_INDICES: lower = [] upper = [] @@ -317,23 +319,31 @@ def create_map_gallery( product_type: str, dimensions: int, ) -> None: - """Create an index or candidate thumbnail gallery from completed assets.""" + """Create dynamic continuous-index or candidate thumbnail galleries.""" rows = output_manifest[ output_manifest["product_type"] == product_type ].sort_values("epoch") epochs = rows["epoch"].astype(int).tolist() + display_indices = ( + CONTINUOUS_INDICES + if product_type == "indices" + else CANDIDATE_INDICES + ) ranges = index_display_ranges(histograms) figure, axes = plt.subplots( len(epochs), - len(SELECTED_INDICES), - figsize=(16, 3.0 * len(epochs)), + len(display_indices), + figsize=( + 3.2 * len(display_indices), + 3.0 * len(epochs), + ), squeeze=False, ) for row_index, (_, row) in enumerate(rows.iterrows()): image = ee.Image(str(row["asset_id"])) - for column_index, name in enumerate(SELECTED_INDICES): + for column_index, name in enumerate(display_indices): axis = axes[row_index, column_index] axis.set_xticks([]) axis.set_yticks([]) @@ -341,6 +351,7 @@ def create_map_gallery( try: if product_type == "indices": minimum, maximum = ranges[name] + display_image = image.select(name) parameters = { "bands": name, "min": minimum, @@ -350,17 +361,33 @@ def create_map_gallery( "format": "png", } else: + built = image.select(f"built_{name}") + valid = image.select(f"valid_{name}").eq(1) + display_image = ( + ee.Image.constant(2) + .where(valid.And(built.eq(0)), 0) + .where(valid.And(built.eq(1)), 1) + .rename("display") + ) parameters = { - "bands": f"built_{name}", + "bands": "display", "min": 0, - "max": 1, - "palette": ["f7f7f7", "d73027"], + "max": 2, + "palette": [ + "f7f7f7", + "d73027", + "bdbdbd", + ], "region": exact_grid_region(grid), "dimensions": int(dimensions), "format": "png", } - axis.imshow(read_thumbnail(image.getThumbURL(parameters))) + axis.imshow( + read_thumbnail( + display_image.getThumbURL(parameters) + ) + ) except Exception as error: axis.text( 0.5, @@ -380,7 +407,6 @@ def create_map_gallery( figure.suptitle(title) save_figure(figure, output_path) - def write_report( output_manifest: pd.DataFrame, threshold_table: pd.DataFrame, @@ -396,8 +422,10 @@ def write_report( "", f"- Completed epochs: {', '.join(map(str, epochs))}", f"- Epoch count: {len(epochs)}", - f"- Continuous index layers: {len(epochs) * 7}", - f"- Binary candidate maps: {len(epochs) * 5}", + f"- Continuous index layers: " + f"{len(epochs) * len(CONTINUOUS_INDEX_BANDS)}", + f"- Binary candidate maps: " + f"{len(epochs) * len(CANDIDATE_INDICES)}", f"- Epoch-specific Otsu thresholds: {len(threshold_table)}", "", "The binary maps are unvalidated candidate pseudo-labels. No index " @@ -433,6 +461,8 @@ def write_report( "- Candidate-area changes are quality diagnostics, not validated growth.", "- Invalid index pixels remain masked and are never recoded as non-built.", "- No temporal persistence correction or transition map is applied.", + "- IBI remains in continuous assets for transparency but is excluded " + "from candidate generation and Day 5 method comparison.", "- Comparative validation and final method selection belong to the next stage.", ] ) diff --git a/src/analysis/built_up/indices.py b/src/analysis/built_up/indices.py index ed96f5a..0246600 100644 --- a/src/analysis/built_up/indices.py +++ b/src/analysis/built_up/indices.py @@ -1,4 +1,4 @@ -"""Spectral-index formulas, formula-specific masks and candidate construction.""" +"""Spectral-index formulas, masks and candidate construction.""" from __future__ import annotations @@ -7,17 +7,54 @@ try: import ee -except ImportError: # Allows pure formula tests without Earth Engine installed. +except ImportError: ee = None -COMMON_BANDS = ["blue", "green", "red", "nir", "swir1", "swir2"] -INTERMEDIATE_INDICES = ["savi", "mndwi"] -SELECTED_INDICES = ["ndbi", "ibi", "ibui", "vbswir1_bi", "ndbsui"] -INDEX_BANDS = INTERMEDIATE_INDICES + SELECTED_INDICES + ["valid_composite"] +COMMON_BANDS = [ + "blue", + "green", + "red", + "nir", + "swir1", + "swir2", +] + +INTERMEDIATE_INDICES = [ + "savi", + "mndwi", +] + +# Retained in the continuous index assets. +CONTINUOUS_INDICES = [ + "ndbi", + "ibi", + "ibui", + "vbswir1_bi", + "ndbsui", +] + +# Allowed to produce Otsu candidates and enter Day 5 comparison. +CANDIDATE_INDICES = [ + "ndbi", + "ibui", + "vbswir1_bi", + "ndbsui", +] + +CONTINUOUS_INDEX_BANDS = ( + INTERMEDIATE_INDICES + + CONTINUOUS_INDICES +) + +INDEX_BANDS = ( + CONTINUOUS_INDEX_BANDS + + ["valid_composite"] +) + CANDIDATE_BANDS = ( - [f"built_{name}" for name in SELECTED_INDICES] - + [f"valid_{name}" for name in SELECTED_INDICES] + [f"built_{name}" for name in CANDIDATE_INDICES] + + [f"valid_{name}" for name in CANDIDATE_INDICES] ) @@ -25,7 +62,8 @@ def require_earth_engine() -> None: """Raise an actionable error when Earth Engine is unavailable.""" if ee is None: raise ImportError( - "The Earth Engine Python API is required for raster processing." + "The Earth Engine Python API is required for raster " + "processing." ) @@ -35,14 +73,13 @@ def safe_ratio( epsilon: float, name: str, ): - """Divide two Earth Engine images while masking unstable denominators.""" + """Divide images while masking numerically unstable denominators.""" require_earth_engine() valid_denominator = denominator.abs().gt(float(epsilon)) - safe_numerator = numerator.updateMask(valid_denominator) - safe_denominator = denominator.updateMask(valid_denominator) return ( - safe_numerator.divide(safe_denominator) + numerator.updateMask(valid_denominator) + .divide(denominator.updateMask(valid_denominator)) .rename(name) .toFloat() ) @@ -56,22 +93,26 @@ def build_index_stack( savi_l: float, minimum_valid_observations: int, ): - """Calculate seven continuous indices and the explicit composite-valid band. + """Calculate seven continuous indices and one validity band. - Each index retains the maximum valid coverage permitted by its own formula. - NDBSUI receives an additional mask where red or SWIR1 reflectance is - negative, while the other indices are not forced to share that mask. + IBI remains in the continuous asset for transparency. It is not + included in ``CANDIDATE_INDICES`` and therefore receives no Otsu + threshold or binary candidate band. """ require_earth_engine() composite = composite.select(COMMON_BANDS) - count = observation_count.select("valid_observation_count") + count = observation_count.select( + "valid_observation_count" + ) all_bands_valid = ( composite.mask() .reduce(ee.Reducer.min()) .eq(1) ) - observation_valid = count.gte(int(minimum_valid_observations)) + observation_valid = count.gte( + int(minimum_valid_observations) + ) base_valid = all_bands_valid.And(observation_valid) source = composite.updateMask(base_valid) @@ -128,8 +169,12 @@ def build_index_stack( .sqrt() ) ndbsui = safe_ratio( - swir2.add(geometric_mean).subtract(red.add(swir1)), - swir2.add(geometric_mean).add(red.add(swir1)), + swir2.add(geometric_mean).subtract( + red.add(swir1) + ), + swir2.add(geometric_mean).add( + red.add(swir1) + ), epsilon, "ndbsui", ).updateMask(nonnegative_inputs) @@ -149,8 +194,12 @@ def build_index_stack( "vbswir1_bi": vbswir1_bi, "ndbsui": ndbsui, } + stack = ee.Image.cat( - [images[name] for name in INTERMEDIATE_INDICES + SELECTED_INDICES] + [ + images[name] + for name in CONTINUOUS_INDEX_BANDS + ] + [valid_composite] ).select(INDEX_BANDS) @@ -161,14 +210,16 @@ def build_candidate_stack( index_images: dict[str, Any], thresholds: dict[str, float], ): - """Create five masked candidate bands and five explicit validity bands.""" + """Create candidate and validity bands for candidate indices.""" require_earth_engine() built_bands = [] valid_bands = [] - for index_name in SELECTED_INDICES: + for index_name in CANDIDATE_INDICES: if index_name not in thresholds: - raise KeyError(f"Missing threshold for index '{index_name}'.") + raise KeyError( + f"Missing threshold for index '{index_name}'." + ) index_image = index_images[index_name] valid = ( @@ -187,7 +238,9 @@ def build_candidate_stack( built_bands.append(built) valid_bands.append(valid) - return ee.Image.cat(built_bands + valid_bands).select(CANDIDATE_BANDS) + return ee.Image.cat( + built_bands + valid_bands + ).select(CANDIDATE_BANDS) def _safe_scalar_ratio( @@ -196,7 +249,10 @@ def _safe_scalar_ratio( epsilon: float, ) -> float | None: """Return a scalar ratio or ``None`` for an unstable denominator.""" - if not math.isfinite(numerator) or not math.isfinite(denominator): + if not math.isfinite(numerator): + return None + + if not math.isfinite(denominator): return None if abs(denominator) <= epsilon: @@ -216,11 +272,7 @@ def reference_index_values( epsilon: float = 1e-6, savi_l: float = 0.5, ) -> dict[str, float | None]: - """Calculate scalar reference values used by the critical formula tests. - - This function is a small executable specification of the Earth Engine - formulas. ``None`` represents a value that must remain masked. - """ + """Calculate scalar reference values used by formula tests.""" savi = _safe_scalar_ratio( (nir - red) * (1.0 + savi_l), nir + red + savi_l, diff --git a/src/analysis/landsat/build_composites.py b/src/analysis/landsat/build_composites.py index 2e8376d..4ed77d6 100644 --- a/src/analysis/landsat/build_composites.py +++ b/src/analysis/landsat/build_composites.py @@ -59,6 +59,14 @@ "SR_B6": "swir1", "SR_B7": "swir2", }, + "LC09": { + "SR_B2": "blue", + "SR_B3": "green", + "SR_B4": "red", + "SR_B5": "nir", + "SR_B6": "swir1", + "SR_B7": "swir2", + }, } @@ -356,11 +364,14 @@ def run_submission(config_path: Path, submit: bool) -> pd.DataFrame: for epoch in config["landsat"]["epochs"]: epoch_manifest = manifest[manifest["epoch"].astype(int) == int(epoch)] - # Some epochs (e.g., 1990, 1995, 2000) may have no selected scenes because no - # Landsat imagery exists for that period over the study area. Skipping them is - # expected and does not indicate a failure. + # Some configured epochs may have no scenes in the frozen Day 2 + # manifest. This can reflect archive availability, the selected temporal + # window, quality requirements or catalogue-selection decisions. if epoch_manifest.empty: - print(f"Skipping epoch {epoch}: no selected scenes in manifest.") + print( + f"Skipping epoch {epoch}: no scenes are available in the " + "frozen Day 2 selected-scene manifest." + ) continue composite, count_image, scene_counts = build_epoch_products( @@ -470,7 +481,9 @@ def run_submission(config_path: Path, submit: bool) -> pd.DataFrame: "source": "GHSL", "asset_id": "see_ghsl_epoch_manifest.csv", "representation": "native_100m_grid", - "temporal_representation": "seven_epochs", + "temporal_representation": ( + f"{len(config['ghsl']['epochs'])}_configured_epochs" + ), "intended_role": "auxiliary_benchmark_and_population", }, { diff --git a/src/analysis/landsat/qa_masks.py b/src/analysis/landsat/qa_masks.py index a14ffac..5d8e5dc 100644 --- a/src/analysis/landsat/qa_masks.py +++ b/src/analysis/landsat/qa_masks.py @@ -1,11 +1,15 @@ -"""Collection 2 quality masks used by the Day 2 Landsat catalogue.""" +"""Collection 2 quality masks used by the Landsat catalogue.""" from __future__ import annotations from typing import Any + import ee +OLI_SENSORS = {"LC08", "LC09"} + + def bit_is_set(image: ee.Image, band: str, bit: int) -> ee.Image: """Return a Boolean image indicating whether one QA bit is set.""" return image.select(band).bitwiseAnd(1 << bit).neq(0) @@ -19,8 +23,9 @@ def landsat_valid_mask( ) -> ee.Image: """Return one for QA-valid observations and zero for invalid pixels. - Water is deliberately retained. Landsat 8 cirrus is removed using bit 2; - fill, dilated cloud, cloud, shadow, snow and saturation are also removed. + Water is deliberately retained. High-confidence cirrus is removed + for Landsat 8 and Landsat 9. Fill, dilated cloud, cloud, cloud + shadow, snow and radiometric saturation are also removed. """ if sensor_key not in collections: raise ValueError(f"Unsupported sensor: {sensor_key}") @@ -33,16 +38,36 @@ def landsat_valid_mask( ("mask_cloud_shadow", 4), ("mask_snow", 5), ] + for rule_name, bit in bit_rules: if rules.get(rule_name, True): - valid = valid.And(bit_is_set(image, "QA_PIXEL", bit).Not()) + valid = valid.And( + bit_is_set(image, "QA_PIXEL", bit).Not() + ) - if sensor_key == "LC08" and rules.get("mask_cirrus_landsat8", True): - valid = valid.And(bit_is_set(image, "QA_PIXEL", 2).Not()) + if ( + sensor_key in OLI_SENSORS + and rules.get("mask_cirrus_landsat8", True) + ): + valid = valid.And( + bit_is_set(image, "QA_PIXEL", 2).Not() + ) if rules.get("mask_radiometric_saturation", True): - valid = valid.And(image.select("QA_RADSAT").eq(0)) + valid = valid.And( + image.select("QA_RADSAT").eq(0) + ) required = collections[sensor_key]["required_bands"] - all_bands_present = image.select(required).mask().reduce(ee.Reducer.min()).eq(1) - return valid.And(all_bands_present).rename("valid").toByte() + all_bands_present = ( + image.select(required) + .mask() + .reduce(ee.Reducer.min()) + .eq(1) + ) + + return ( + valid.And(all_bands_present) + .rename("valid") + .toByte() + ) diff --git a/tests/reference/built_up_candidates_v2.json b/tests/reference/built_up_candidates_v2.json new file mode 100644 index 0000000..c9dd526 --- /dev/null +++ b/tests/reference/built_up_candidates_v2.json @@ -0,0 +1,35 @@ +{ + "binary_candidate_layer_count": 20, + "candidate_area_summary_sha256": "33e3593f918522a3fe47913da83ca453be6d97bd5c92f8f13d6af13c45ddbe33", + "continuous_index_layer_count": 35, + "dependency_manifest_sha256": "376fa132fac838180018e5fae1e562c7aae1fd78e3ed4d462bbbccffb67d0724", + "earth_engine_assets": [ + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/indices/indices_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/built_up_candidates/candidates_2025" + ], + "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", + "histograms_sha256": "8b8e74fc960997af7a7cf664d9c3438acdc44263f90ffd3303c8840298750c8a", + "orchestration_version_sha256": "993e0306c5cfb4c7c3a670cec755d0add6f23be90d90cd564774d82afe77a9f5", + "output_manifest_sha256": "052e4eba9499e1e39f73a5a20adb749dda41144ab3147e9804eee79d1274f041", + "pipeline_stage": "built_up_candidates", + "pipeline_version": 2, + "processed_epochs": [ + 2005, + 2010, + 2015, + 2020, + 2025 + ], + "processing_recipe_sha256": "8aec03a5265981f4a1c24fba28790734d878473a66e4b872ca206553518f2937", + "stable_signature": "cdf7a1f04c8c00641603c2e8092284ddfbf256ea5442e4e3f49543898f76cd55", + "task_manifest_sha256": "d746b0b46677c44218c0e8240c8d2e367099b994319cc0c1b2a55ef8fd4efb86", + "threshold_table_sha256": "e4b46d619a2df8665d0c7e7f50c63ca676e93cf63ece3a8f9baf8b5bfc9f7ba2" +} diff --git a/tests/reference/landsat_catalog_v2.json b/tests/reference/landsat_catalog_v2.json new file mode 100644 index 0000000..56347d1 --- /dev/null +++ b/tests/reference/landsat_catalog_v2.json @@ -0,0 +1,7 @@ +{ + "catalog_version": 1, + "config_sha256": "197a0d9faf8fa3819efb068bd4a3dbe815fa29cf036c5c1d22404baaed51dd0f", + "selected_scene_manifest_sha256": "2325905e823c14352a4f29cea3652d62c1d15ad81a293bda73d826acf2dce321", + "compositing_protocol_sha256": "a31dee916b03f507d961be8586623ea01e7c8db8f9395a95638ee7eac973059c", + "selected_scene_count": 7 +} \ No newline at end of file diff --git a/tests/reference/orchestrate_sources_v2.json b/tests/reference/orchestrate_sources_v2.json new file mode 100644 index 0000000..9dae60b --- /dev/null +++ b/tests/reference/orchestrate_sources_v2.json @@ -0,0 +1,27 @@ +{ + "catalog_version_sha256": "9119010004d8374433ef5517a5c0e95b7037346526dd3859279bc6e3f673867d", + "compositing_protocol_sha256": "0abfc5c0afb8e7d7dfbfbf6a436b70adf63a5feae6b1f7ece00554ba655afdc0", + "day3_version": 1, + "dependency_manifest_sha256": "3c51f4559e634d7d24ecbc55768ac490d78ec80bd4d270b3e4648769756f9fef", + "earth_engine_assets": [ + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2005", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2010", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2015", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2020", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/composite_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/landsat/valid_count_2025", + "projects/urban-sprawl-ssa/assets/sprawlgp/v1/terrain/elevation_slope" + ], + "ghsl_epoch_manifest_sha256": "a72c696665905abc86a9e927c2de8dfd4f7c7643a45a503a4e71f9cccd36add6", + "grid_linkage_sha256": "87d33f52f81d5bdcc222f8cda62b2b1b8311a299a2780d8035647182ea9c4d05", + "grid_specification_sha256": "2e7b714996005503134f3bb5d6404ef0d3c0cba198f170c7bcd347ccac7ac525", + "landsat_composite_manifest_sha256": "297d5ad03acdd975950c3aa69349521106d219dfc6f14944ce823122c7574f96", + "landsat_epoch_quality_sha256": "6346eedfba2948237ad122415ee5bd0cbd6126fa5438283ffe80b28837b201fe", + "osm_source_sha256": "a0dfcf623b5d5565238dfc23590842a98edc9fba8301f4eeb9b31510278d8380", + "stable_signature": "ae2b3acab9f988f6b4107e45e43275525046d74527f0310838563ba88d9fcf74", + "task_manifest_sha256": "ead066e3c6f8709f5ab73f14c791ca08a743ca9e07d526e50f39a57f52734672" +} diff --git a/tests/test_built_up_candidates.py b/tests/test_built_up_candidates.py index 95e539a..633a4fb 100644 --- a/tests/test_built_up_candidates.py +++ b/tests/test_built_up_candidates.py @@ -13,6 +13,9 @@ from src.analysis.built_up.indices import ( CANDIDATE_BANDS, + CANDIDATE_INDICES, + CONTINUOUS_INDICES, + CONTINUOUS_INDEX_BANDS, INDEX_BANDS, reference_index_values, ) @@ -105,6 +108,28 @@ def test_index_formulas_on_reference_pixels() -> None: assert negative_square_root_input["ndbsui"] is None + +def test_ibi_is_retained_only_as_continuous_index() -> None: + """Ensure IBI remains auditable but is not a candidate method.""" + assert "ibi" in CONTINUOUS_INDICES + assert "ibi" in INDEX_BANDS + assert "ibi" not in CANDIDATE_INDICES + assert "built_ibi" not in CANDIDATE_BANDS + assert "valid_ibi" not in CANDIDATE_BANDS + + +def test_configured_index_roles_match_pipeline( + config: dict[str, Any], +) -> None: + """Ensure configuration and implemented index roles remain aligned.""" + assert config["indices"]["continuous"] == CONTINUOUS_INDICES + assert config["indices"]["candidates"] == CANDIDATE_INDICES + assert set( + config["classification"]["built_up_direction"] + ) == set(CANDIDATE_INDICES) + + + # 2. Otsu must find a defensible split for a clearly bimodal histogram. @@ -187,13 +212,38 @@ def test_processed_epochs_and_dynamic_counts( assert observed_epochs == expected_epochs assert len(outputs) == len(expected_epochs) * 2 assert outputs["continuous_index_layers"].sum() == ( - len(expected_epochs) * 7 + len(expected_epochs) * len(CONTINUOUS_INDEX_BANDS) ) assert outputs["binary_candidate_layers"].sum() == ( - len(expected_epochs) * 5 + len(expected_epochs) * len(CANDIDATE_INDICES) ) - assert len(thresholds) == len(expected_epochs) * 5 - assert len(areas) == len(expected_epochs) * 5 + assert len(thresholds) == len(expected_epochs) * len(CANDIDATE_INDICES) + assert len(areas) == len(expected_epochs) * len(CANDIDATE_INDICES) + + + +def test_generated_candidate_metadata_excludes_ibi( + config: dict[str, Any], +) -> None: + """Ensure regenerated threshold and area tables contain no IBI rows.""" + tables = generated_tables(config) + + if tables is None: + pytest.skip( + "Generated built-up candidate metadata is not available." + ) + + outputs, thresholds, areas = tables + assert "ibi" not in set(thresholds["index_name"].astype(str)) + assert "ibi" not in set(areas["index_name"].astype(str)) + + candidate_rows = outputs[ + outputs["product_type"] == "candidates" + ] + + for bands in candidate_rows["band_names"].astype(str): + assert "built_ibi" not in bands.split(",") + assert "valid_ibi" not in bands.split(",") # 5. Every successful threshold must be finite, bounded and non-degenerate. @@ -274,7 +324,7 @@ def test_exported_assets_schema_grid_domain_and_traceability( if row.product_type == "candidates": values = image.select( - [f"built_{name}" for name in config["indices"]["selected"]] + [f"built_{name}" for name in CANDIDATE_INDICES] ).reduceRegion( reducer=ee.Reducer.minMax(), geometry=image.geometry(), @@ -284,6 +334,6 @@ def test_exported_assets_schema_grid_domain_and_traceability( tileScale=int(config["histogram"]["tile_scale"]), ).getInfo() - for name in config["indices"]["selected"]: + for name in CANDIDATE_INDICES: assert values[f"built_{name}_min"] == 0 assert values[f"built_{name}_max"] == 1 diff --git a/tests/test_landsat_catalog.py b/tests/test_landsat_catalog.py index 06a5f92..1c81aca 100644 --- a/tests/test_landsat_catalog.py +++ b/tests/test_landsat_catalog.py @@ -1,119 +1,156 @@ -"""Integrity tests for the generated and frozen Day 2 Landsat catalogue.""" +"""Critical integrity tests for the generated Day 2 catalogue.""" from __future__ import annotations import hashlib import json from pathlib import Path +from typing import Any + import pandas as pd import pytest import yaml + ROOT = Path(__file__).resolve().parents[1] CONFIG_PATH = ROOT / "configs/landsat_catalog.yaml" def sha256_file(path: Path) -> str: - """Calculate a checksum used to detect changes after catalogue freezing.""" + """Calculate a checksum used after catalogue freezing.""" digest = hashlib.sha256() + with path.open("rb") as stream: - for chunk in iter(lambda: stream.read(1024 * 1024), b""): + for chunk in iter( + lambda: stream.read(1024 * 1024), + b"", + ): digest.update(chunk) + return digest.hexdigest() @pytest.fixture(scope="session") -def config() -> dict: - """Load the authoritative Day 2 configuration.""" - return yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8")) +def config() -> dict[str, Any]: + """Load the authoritative Landsat catalogue configuration.""" + return yaml.safe_load( + CONFIG_PATH.read_text(encoding="utf-8") + ) @pytest.fixture(scope="session") -def metadata(config: dict) -> Path: +def metadata(config: dict[str, Any]) -> Path: """Return the generated Landsat metadata directory.""" return ROOT / config["outputs"]["directory"] @pytest.fixture(scope="session") def manifest(metadata: Path) -> pd.DataFrame: - """Load the complete scene manifest.""" + """Load the complete diagnostic scene manifest.""" return pd.read_csv(metadata / "scene_manifest_all.csv") @pytest.fixture(scope="session") def selected(metadata: Path) -> pd.DataFrame: - """Load exact scenes frozen for Day 3.""" - return pd.read_csv(metadata / "selected_scene_manifest.csv") - - -# Configuration: required epochs, collections and water policy remain fixed. - -# def test_required_epochs(config: dict) -> None: -# """Ensure all seven historical epochs remain configured.""" -# assert config["catalog"]["epochs"] == [1990, 1995, 2000, 2005, 2010, 2015, 2020] + """Load exact scenes frozen for orchestration.""" + return pd.read_csv( + metadata / "selected_scene_manifest.csv" + ) + + +def test_required_epochs_and_landsat9( + config: dict[str, Any], +) -> None: + """Ensure 2025 and Landsat 9 remain configured.""" + assert config["catalog"]["epochs"] == [ + 1990, + 1995, + 2000, + 2005, + 2010, + 2015, + 2020, + 2025, + ] + assert config["collections"]["LC09"]["id"] == ( + "LANDSAT/LC09/C02/T1_L2" + ) + policy = config["epoch_sensor_policy"][2025] + assert set(policy["primary"]) == {"LC08", "LC09"} -def test_water_is_retained(config: dict) -> None: - """Ensure water remains available to later spectral indices.""" +def test_water_is_retained( + config: dict[str, Any], +) -> None: + """Ensure water remains available to later indices.""" assert config["quality_mask"]["mask_water"] is False -# Outputs: every reproducibility artefact must exist. - -def test_required_outputs_exist(metadata: Path) -> None: - """Ensure all expected Day 2 metadata products were generated.""" - expected = [ - "scene_manifest_all.csv", "scene_manifest_all.parquet", - "monthly_availability.csv", "monthly_availability.parquet", - "candidate_window_summary.csv", "candidate_window_details.csv", - "epoch_quality_summary.csv", "selected_scene_manifest.csv", - "compositing_protocol.yaml", "catalog_version.json", - ] - for name in expected: - assert (metadata / name).is_file(), name - - -# Manifest: scene identifiers and valid fractions must be usable and bounded. - -def test_manifest_schema(manifest: pd.DataFrame) -> None: - """Ensure exact asset IDs, dates, roles and QA fractions are present.""" +def test_manifest_schema_and_unique_ids( + manifest: pd.DataFrame, +) -> None: + """Ensure exact scene IDs and bounded QA fractions exist.""" required = { - "epoch", "sensor_key", "sensor_role", "earth_engine_asset_id", - "acquisition_date", "valid_fraction_core", "valid_fraction_context", - "slc_off", "landsat7_orbit_drift_period", + "epoch", + "sensor_key", + "sensor_role", + "earth_engine_asset_id", + "acquisition_date", + "valid_fraction_core", + "valid_fraction_context", } assert required.issubset(manifest.columns) - -def test_epoch_scene_ids_are_unique(manifest: pd.DataFrame) -> None: - """Ensure the same epoch cannot contain duplicate scene records.""" - keys = manifest["epoch"].astype(str) + "::" + manifest["earth_engine_asset_id"].astype(str) + keys = ( + manifest["epoch"].astype(str) + + "::" + + manifest["earth_engine_asset_id"].astype(str) + ) assert keys.is_unique - - -def test_valid_fractions_are_bounded(manifest: pd.DataFrame) -> None: - """Ensure valid fractions remain within the physical range zero to one.""" - assert manifest["valid_fraction_core"].between(0, 1.000001).all() - assert manifest["valid_fraction_context"].between(0, 1.000001).all() - - -# Selection: every epoch must have at least one scene in one common window. - -# def test_every_epoch_has_selected_scenes(config: dict, selected: pd.DataFrame) -> None: -# """Ensure all seven Day 3 epoch composites have source scenes.""" -# assert set(selected["epoch"].astype(int)) == set(config["catalog"]["epochs"]) -# assert (selected.groupby("epoch").size() > 0).all() - - -def test_selected_window_is_common(selected: pd.DataFrame) -> None: - """Ensure every epoch uses the same calendar-season definition.""" - assert selected["selected_window_name"].nunique() == 1 - - -# Checksums: frozen files must match their catalogue version record. - -def test_frozen_checksums(metadata: Path) -> None: - """Detect manual changes to the selected manifest or protocol.""" - version = json.loads((metadata / "catalog_version.json").read_text(encoding="utf-8")) - assert sha256_file(metadata / "selected_scene_manifest.csv") == version["selected_scene_manifest_sha256"] - assert sha256_file(metadata / "compositing_protocol.yaml") == version["compositing_protocol_sha256"] + assert manifest["valid_fraction_core"].between( + 0, + 1.000001, + ).all() + assert manifest["valid_fraction_context"].between( + 0, + 1.000001, + ).all() + + +def test_selected_epochs_are_configured_and_2025_is_recovered( + config: dict[str, Any], + selected: pd.DataFrame, +) -> None: + """Accept missing early epochs but require a usable 2025 set.""" + configured = set(config["catalog"]["epochs"]) + observed = set(selected["epoch"].astype(int)) + assert observed.issubset(configured) + assert 2025 in observed + + selected_2025 = selected[ + selected["epoch"].astype(int) == 2025 + ] + assert len(selected_2025) >= 2 + assert set(selected_2025["sensor_key"]).issubset( + {"LC08", "LC09"} + ) + + +def test_frozen_checksums_when_outputs_exist( + metadata: Path, +) -> None: + """Detect manual changes after the revised catalogue is frozen.""" + version_path = metadata / "catalog_version.json" + + if not version_path.is_file(): + pytest.skip("The revised catalogue has not been frozen.") + + version = json.loads( + version_path.read_text(encoding="utf-8") + ) + assert sha256_file( + metadata / "selected_scene_manifest.csv" + ) == version["selected_scene_manifest_sha256"] + assert sha256_file( + metadata / "compositing_protocol.yaml" + ) == version["compositing_protocol_sha256"] diff --git a/tests/test_orchestrate_sources.py b/tests/test_orchestrate_sources.py index f7ab2f0..9be5dc7 100644 --- a/tests/test_orchestrate_sources.py +++ b/tests/test_orchestrate_sources.py @@ -15,6 +15,8 @@ import pytest import yaml +from src.analysis.landsat.build_composites import LANDSAT_BANDS + PROJECT_ROOT = Path(__file__).resolve().parents[1] CONFIG_PATH = PROJECT_ROOT / "configs/orchestrate_sources.yaml" @@ -85,4 +87,17 @@ def test_observation_counts_are_plausible(epoch_quality: pd.DataFrame) -> None: # S anity check: the core area should have at least some coverage assert (epoch_quality["covered_grid_pct"] > 0.0).all(), ( "Every epoch should cover at least some part of the grid." - ) \ No newline at end of file + ) + +# Critical Landsat 9 harmonisation checks for orchestration. +def test_landsat9_uses_the_oli_band_mapping() -> None: + """Ensure LC09 is harmonised exactly like LC08.""" + assert LANDSAT_BANDS["LC09"] == LANDSAT_BANDS["LC08"] + assert list(LANDSAT_BANDS["LC09"].values()) == [ + "blue", + "green", + "red", + "nir", + "swir1", + "swir2", + ] \ No newline at end of file

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zqafrkDm9m_yF$?$z{oaRz%~Y<$%+(30HH4dxVugSFIhWOWkkmvv7w0bBbgS!eYNW? z+nH`j|J(iC-=v%;qQD`OJ9X{-{!D6Y#PF|M0xK7}s{m-Y`Fckf7`TyB1$>LJ`62jP z3~MtrG{L;>2CBWXZcSVqo4m4e2)NJ+@a$~7yu72a6jq-Y?dGsjKDMWS*Zf}ej{5p` r^dGT5@osfA2=bRyE~cHodGCJ#=VgNJ literal 0 HcmV?d00001 diff --git a/src/analysis/boundaries/build_study_grid.py b/src/analysis/boundaries/build_study_grid.py new file mode 100644 index 0000000..fbf702a --- /dev/null +++ b/src/analysis/boundaries/build_study_grid.py @@ -0,0 +1,818 @@ +"""Build the authoritative Yaoundé study geometries and deterministic 30 m grid. + +This module reads the study-area configuration, selects the seven Yaoundé ADM3 +units, repairs and dissolves their geometries, creates the context buffer and +convex hull, rasterises the core and context masks, and exports a stable +cell-level Parquet table together with machine-readable quality reports. + +All paths stored in the YAML configuration are resolved relative to the +repository root, inferred from ``configs/study_area.yaml``. This allows the same +commands to work on another computer without hard-coded absolute paths. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +from pathlib import Path +from typing import Any + +import geopandas as gpd +import numpy as np +import pandas as pd +import rasterio +import yaml +from pyproj import CRS +from rasterio.features import rasterize +from rasterio.transform import from_origin, xy +from shapely import make_valid +from shapely.geometry.base import BaseGeometry + + +def load_configuration(config_path: Path) -> tuple[dict[str, Any], Path]: + """Load the YAML configuration and infer the repository root directory. + + The expected configuration location is ``/configs/study_area.yaml``. + Returning the project root alongside the parsed configuration ensures that + every relative path is interpreted consistently, regardless of the caller's + current working directory. + + Args: + config_path: Path to the study-area YAML configuration. + + Returns: + A tuple containing the parsed configuration dictionary and project root. + + Raises: + FileNotFoundError: If the configuration file does not exist. + ValueError: If the YAML file is empty or does not contain a mapping. + """ + resolved_config_path = config_path.expanduser().resolve() + + if not resolved_config_path.is_file(): + raise FileNotFoundError(f"Configuration file not found: {resolved_config_path}") + + with resolved_config_path.open("r", encoding="utf-8") as file: + configuration = yaml.safe_load(file) + + if not isinstance(configuration, dict): + raise ValueError("The study-area configuration must contain a YAML mapping.") + + project_root = resolved_config_path.parent.parent + return configuration, project_root + + +def resolve_project_path(project_root: Path, path_value: str | Path) -> Path: + """Resolve a configured path relative to the repository root. + + Absolute paths are accepted for exceptional local use, while ordinary + project paths remain portable and are resolved beneath ``project_root``. + + Args: + project_root: Absolute path to the repository root. + path_value: Absolute or project-relative path from the configuration. + + Returns: + A normalised absolute path. + """ + path = Path(path_value).expanduser() + return path.resolve() if path.is_absolute() else (project_root / path).resolve() + + +def write_json(data: dict[str, Any], output_path: Path) -> None: + """Write a dictionary as formatted UTF-8 JSON, creating directories first. + + Args: + data: JSON-serialisable dictionary to export. + output_path: Destination JSON file. + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + output_path.write_text(json.dumps(data, indent=2), encoding="utf-8") + + +def sha256_file(path: Path) -> str: + """Calculate the SHA-256 digest of a file without loading it fully in memory. + + The file is read in one-megabyte chunks so the function remains suitable for + large geospatial inputs and exported raster products. + + Args: + path: File whose checksum must be calculated. + + Returns: + Lowercase hexadecimal SHA-256 digest. + + Raises: + FileNotFoundError: If the requested file does not exist. + """ + if not path.is_file(): + raise FileNotFoundError(f"Cannot calculate checksum; file not found: {path}") + + digest = hashlib.sha256() + + with path.open("rb") as file: + for chunk in iter(lambda: file.read(1024 * 1024), b""): + digest.update(chunk) + + return digest.hexdigest() + + +def snap_down(value: float, resolution: float, anchor: float) -> float: + """Snap a coordinate downward to the nearest anchored grid line. + + Args: + value: Coordinate to snap. + resolution: Grid-cell size in coordinate-system units. + anchor: Fixed global grid origin for the relevant axis. + + Returns: + Largest anchored grid coordinate less than or equal to ``value``. + """ + return math.floor((value - anchor) / resolution) * resolution + anchor + + +def snap_up(value: float, resolution: float, anchor: float) -> float: + """Snap a coordinate upward to the nearest anchored grid line. + + Args: + value: Coordinate to snap. + resolution: Grid-cell size in coordinate-system units. + anchor: Fixed global grid origin for the relevant axis. + + Returns: + Smallest anchored grid coordinate greater than or equal to ``value``. + """ + return math.ceil((value - anchor) / resolution) * resolution + anchor + + +def safe_make_valid(geometry: BaseGeometry | None) -> BaseGeometry | None: + """Repair an invalid geometry while preserving missing or empty geometries. + + Missing and empty geometries are returned unchanged so they can be counted + and removed explicitly by the caller rather than being silently converted. + + Args: + geometry: Shapely geometry to validate. + + Returns: + A valid Shapely geometry when repair is possible, otherwise the original + missing or empty value. + """ + if geometry is None or geometry.is_empty: + return geometry + + return make_valid(geometry) + + +def validate_source_boundary(boundaries: gpd.GeoDataFrame, source_path: Path) -> None: + """Validate the minimum structural requirements of a source boundary layer. + + Args: + boundaries: Source boundary GeoDataFrame loaded by GeoPandas. + source_path: Path used only to produce informative error messages. + + Raises: + ValueError: If the source is empty, lacks a CRS, or lacks a geometry column. + """ + if boundaries.empty: + raise ValueError(f"Boundary source contains no features: {source_path}") + + if boundaries.crs is None: + raise ValueError( + "The boundary file has no CRS. Do not assign one without checking " + f"the source metadata: {source_path}" + ) + + if boundaries.geometry.name not in boundaries.columns: + raise ValueError(f"Boundary source has no active geometry column: {source_path}") + + +def select_study_units( + boundaries: gpd.GeoDataFrame, + boundary_config: dict[str, Any], +) -> gpd.GeoDataFrame: + """Select and validate the seven Yaoundé ADM3 units from the source layer. + + P-codes are preferred when configured because they are more stable than + names. The selected names and parent department are still checked to detect + unexpected source changes or configuration errors. + + Args: + boundaries: Boundary features already expressed in the geographic CRS. + boundary_config: ``boundary`` section of ``study_area.yaml``. + + Returns: + Copy of the selected ADM3 features. + + Raises: + KeyError: If a configured field does not exist. + ValueError: If the count, names, P-codes, or parent relationship differ + from the configuration. + """ + name_field = str(boundary_config["name_field"]) + parent_field = boundary_config.get("parent_field") + pcode_field = boundary_config.get("pcode_field") + selected_names = [str(value) for value in boundary_config["selected_names"]] + selected_pcodes = [str(value) for value in boundary_config.get("selected_pcodes", [])] + + required_fields = [name_field] + if parent_field: + required_fields.append(str(parent_field)) + if selected_pcodes and pcode_field: + required_fields.append(str(pcode_field)) + + missing_fields = [field for field in required_fields if field not in boundaries.columns] + if missing_fields: + raise KeyError( + f"Configured boundary fields are absent: {missing_fields}. " + f"Available columns: {list(boundaries.columns)}" + ) + + if selected_pcodes and pcode_field: + selected = boundaries[ + boundaries[str(pcode_field)].astype(str).isin(selected_pcodes) + ].copy() + else: + selected = boundaries[ + boundaries[name_field].astype(str).isin(selected_names) + ].copy() + + expected_count = int(boundary_config["expected_selected_units"]) + if len(selected) != expected_count: + found_names = selected[name_field].astype(str).tolist() + raise ValueError( + f"Expected {expected_count} selected units, but found {len(selected)}: " + f"{found_names}" + ) + + observed_names = set(selected[name_field].astype(str)) + if observed_names != set(selected_names): + raise ValueError( + "Selected unit names do not match the configured names. " + f"Expected {sorted(selected_names)}, found {sorted(observed_names)}" + ) + + if selected_pcodes and pcode_field: + observed_pcodes = set(selected[str(pcode_field)].astype(str)) + if observed_pcodes != set(selected_pcodes): + raise ValueError( + "Selected unit P-codes do not match the configuration. " + f"Expected {sorted(selected_pcodes)}, found {sorted(observed_pcodes)}" + ) + + parent_name = boundary_config.get("parent_name") + if parent_field and parent_name: + observed_parents = sorted( + selected[str(parent_field)].dropna().astype(str).unique().tolist() + ) + if observed_parents != [str(parent_name)]: + raise ValueError( + f"Expected parent '{parent_name}', found {observed_parents}" + ) + + return selected + + +def select_comparison_boundary( + comparison_boundaries: gpd.GeoDataFrame, + comparison_config: dict[str, Any], +) -> gpd.GeoDataFrame: + """Select the single ADM2 Mfoundi feature used as a reference geometry. + + Args: + comparison_boundaries: ADM2 source features. + comparison_config: ``comparison_boundary`` section of the configuration. + + Returns: + One-row GeoDataFrame containing Mfoundi. + + Raises: + KeyError: If the configured name or P-code field is absent. + ValueError: If the selection does not return exactly one feature. + """ + name_field = str(comparison_config["name_field"]) + selected_name = str(comparison_config["selected_name"]) + pcode_field = comparison_config.get("pcode_field") + selected_pcode = comparison_config.get("selected_pcode") + + for field in [name_field, pcode_field]: + if field and str(field) not in comparison_boundaries.columns: + raise KeyError( + f"Configured comparison field '{field}' is absent. " + f"Available columns: {list(comparison_boundaries.columns)}" + ) + + if pcode_field and selected_pcode: + selected = comparison_boundaries[ + comparison_boundaries[str(pcode_field)].astype(str) == str(selected_pcode) + ].copy() + else: + selected = comparison_boundaries[ + comparison_boundaries[name_field].astype(str) == selected_name + ].copy() + + if len(selected) != 1: + raise ValueError( + f"Expected exactly one ADM2 reference feature for {selected_name}, " + f"but found {len(selected)}." + ) + + observed_name = str(selected.iloc[0][name_field]) + if observed_name != selected_name: + raise ValueError( + f"Expected ADM2 name '{selected_name}', found '{observed_name}'." + ) + + return selected + + +def write_single_geometry( + geometry: BaseGeometry, + crs: CRS | str, + output_path: Path, + geometry_name: str, +) -> None: + """Export one named geometry to a GeoPackage layer. + + Existing output files are replaced to keep reruns deterministic and avoid + duplicate layers left by earlier executions. + + Args: + geometry: Geometry to store. + crs: Coordinate reference system assigned to the geometry. + output_path: Destination GeoPackage path. + geometry_name: Layer name and descriptive attribute value. + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + if output_path.exists(): + output_path.unlink() + + geodataframe = gpd.GeoDataFrame( + {"geometry_name": [geometry_name]}, + geometry=[geometry], + crs=crs, + ) + geodataframe.to_file(output_path, layer=geometry_name, driver="GPKG") + + +def write_geodataframe( + geodataframe: gpd.GeoDataFrame, + output_path: Path, + layer_name: str, +) -> None: + """Export a GeoDataFrame to a clean GeoPackage file. + + Args: + geodataframe: Features to write. + output_path: Destination GeoPackage path. + layer_name: Name of the GeoPackage layer. + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + if output_path.exists(): + output_path.unlink() + + geodataframe.to_file(output_path, layer=layer_name, driver="GPKG") + + +def write_mask( + output_path: Path, + mask: np.ndarray, + transform: Any, + crs: CRS | str, + nodata: int = 0, +) -> None: + """Write a binary grid mask as a compressed single-band GeoTIFF. + + Args: + output_path: Destination GeoTIFF path. + mask: Two-dimensional array containing zeros and ones. + transform: Rasterio affine transform describing pixel placement. + crs: Raster coordinate reference system. + nodata: Value representing cells outside the mask. + + Raises: + ValueError: If the mask is not two-dimensional or is not binary. + """ + if mask.ndim != 2: + raise ValueError(f"Expected a two-dimensional mask, received shape {mask.shape}.") + + unique_values = set(np.unique(mask).tolist()) + if not unique_values <= {0, 1}: + raise ValueError(f"Mask must be binary; found values {sorted(unique_values)}.") + + output_path.parent.mkdir(parents=True, exist_ok=True) + + profile = { + "driver": "GTiff", + "height": mask.shape[0], + "width": mask.shape[1], + "count": 1, + "dtype": rasterio.uint8, + "crs": crs, + "transform": transform, + "nodata": nodata, + "compress": "deflate", + "tiled": True, + } + + with rasterio.open(output_path, "w", **profile) as destination: + destination.write(mask.astype(np.uint8), 1) + + +def build_cell_table( + context_mask: np.ndarray, + core_mask: np.ndarray, + transform: Any, + analysis_crs: CRS, + grid_config: dict[str, Any], +) -> pd.DataFrame: + """Create the model-ready table of context-grid cells with stable identifiers. + + Stable identifiers are derived from global anchored grid indices rather than + local raster row and column numbers. Consequently, an existing cell keeps the + same identifier if the context extent is later enlarged without changing the + CRS, resolution, or anchor. + + Args: + context_mask: Binary mask defining cells retained in the context area. + core_mask: Binary mask defining cells inside the reporting core. + transform: Authoritative raster transform. + analysis_crs: Projected CRS used by the grid. + grid_config: ``grid`` section of the YAML configuration. + + Returns: + DataFrame containing cell IDs, local raster indices, global grid indices, + centre coordinates, membership flags, and cell area. + + Raises: + ValueError: If generated cell identifiers are not unique. + """ + resolution = float(grid_config["resolution_m"]) + anchor_x = float(grid_config["anchor_x_m"]) + anchor_y = float(grid_config["anchor_y_m"]) + grid_id = str( + grid_config.get( + "grid_id", + f"yaounde_epsg{analysis_crs.to_epsg()}_{int(resolution)}m_anchor0_v1", + ) + ) + grid_version = int(grid_config.get("version", 1)) + + rows, columns = np.where(context_mask == 1) + x_coordinates, y_coordinates = xy(transform, rows, columns, offset="center") + x_coordinates = np.asarray(x_coordinates, dtype=float) + y_coordinates = np.asarray(y_coordinates, dtype=float) + + global_x_index = np.floor( + (x_coordinates - anchor_x) / resolution + 1e-9 + ).astype(np.int64) + global_y_index = np.floor( + (y_coordinates - anchor_y) / resolution + 1e-9 + ).astype(np.int64) + + epsg = analysis_crs.to_epsg() + if epsg is None: + raise ValueError("The analysis CRS must have an EPSG code for stable cell IDs.") + + resolution_label = int(resolution) if resolution.is_integer() else resolution + cell_ids = [ + f"EPSG{epsg}_{resolution_label}m_{grid_x}_{grid_y}" + for grid_x, grid_y in zip(global_x_index, global_y_index, strict=True) + ] + + cell_table = pd.DataFrame( + { + "grid_id": grid_id, + "grid_version": grid_version, + "cell_id": cell_ids, + "row": rows.astype(np.int32), + "column": columns.astype(np.int32), + "grid_x_index": global_x_index, + "grid_y_index": global_y_index, + "x_center_m": x_coordinates, + "y_center_m": y_coordinates, + "in_core": core_mask[rows, columns].astype(bool), + "in_context": True, + "cell_area_m2": resolution * resolution, + } + ) + + if not cell_table["cell_id"].is_unique: + raise ValueError("Generated cell identifiers are not unique.") + + return cell_table + + +def compare_adm3_union_with_adm2( + core_geometry: BaseGeometry, + adm2_geometry: BaseGeometry, +) -> dict[str, float | bool]: + """Quantify agreement between the dissolved ADM3 core and ADM2 Mfoundi. + + The symmetric difference identifies land contained in only one of the two + geometries. A ratio near zero indicates that the seven ADM3 units reproduce + the ADM2 reference closely. + + Args: + core_geometry: Dissolved union of Yaoundé I–VII. + adm2_geometry: Mfoundi ADM2 reference geometry. + + Returns: + Dictionary containing both areas, intersection, symmetric difference, + relative difference, and mutual coverage indicators. + """ + symmetric_difference = core_geometry.symmetric_difference(adm2_geometry) + intersection = core_geometry.intersection(adm2_geometry) + reference_area = float(adm2_geometry.area) + + difference_ratio = ( + float(symmetric_difference.area) / reference_area if reference_area > 0 else math.nan + ) + + return { + "adm3_union_area_km2": float(core_geometry.area) / 1_000_000, + "adm2_mfoundi_area_km2": reference_area / 1_000_000, + "intersection_area_km2": float(intersection.area) / 1_000_000, + "symmetric_difference_area_km2": ( + float(symmetric_difference.area) / 1_000_000 + ), + "symmetric_difference_ratio": difference_ratio, + "adm2_covers_adm3_union": bool(adm2_geometry.covers(core_geometry)), + "adm3_union_covers_adm2": bool(core_geometry.covers(adm2_geometry)), + } + + +def build_study_grid(config_path: Path) -> dict[str, Any]: + """Execute the full boundary-processing and deterministic-grid pipeline. + + Args: + config_path: Path to ``configs/study_area.yaml``. + + Returns: + Complete boundary report that is also written to disk. + + Raises: + FileNotFoundError: If required source files are missing. + KeyError: If required configuration sections or fields are absent. + ValueError: If source data, selected units, CRS, or grid dimensions fail + validation checks. + """ + config, project_root = load_configuration(config_path) + + boundary_config = config["boundary"] + projection_config = config["projection"] + geometry_config = config["geometries"] + grid_config = config["grid"] + output_config = config["outputs"] + + source_path = resolve_project_path(project_root, boundary_config["source_file"]) + source_hash = sha256_file(source_path) + + boundaries = gpd.read_file(source_path, layer=boundary_config.get("layer")) + validate_source_boundary(boundaries, source_path) + + input_feature_count = len(boundaries) + missing_before = int(boundaries.geometry.isna().sum()) + empty_before = int(boundaries.geometry.is_empty.sum()) + invalid_before = int((~boundaries.geometry.is_valid).sum()) + duplicate_before = int(boundaries.geometry.duplicated().sum()) + geometry_types_before = boundaries.geometry.geom_type.value_counts().to_dict() + + boundaries = boundaries.copy() + boundaries["geometry"] = boundaries.geometry.apply(safe_make_valid) + boundaries = boundaries[ + boundaries.geometry.notna() & ~boundaries.geometry.is_empty + ].copy() + + geographic_crs = projection_config["geographic_crs"] + analysis_crs = CRS.from_user_input(projection_config["analysis_crs"]) + boundaries_geographic = boundaries.to_crs(geographic_crs) + selected = select_study_units(boundaries_geographic, boundary_config) + selected_projected = selected.to_crs(analysis_crs) + + core_geometry = make_valid(selected_projected.geometry.union_all()) + if core_geometry.is_empty: + raise ValueError("The dissolved Yaoundé core geometry is empty.") + + context_buffer_m = float(geometry_config["context_buffer_m"]) + if context_buffer_m <= 0: + raise ValueError("The context buffer distance must be strictly positive.") + + context_geometry = make_valid(core_geometry.buffer(context_buffer_m)) + convex_hull_geometry = make_valid(core_geometry.convex_hull) + + core_output = resolve_project_path(project_root, output_config["core_boundary"]) + context_output = resolve_project_path(project_root, output_config["context_boundary"]) + hull_output = resolve_project_path(project_root, output_config["convex_hull"]) + + write_single_geometry(core_geometry, analysis_crs, core_output, "yaounde_core") + write_single_geometry( + context_geometry, + analysis_crs, + context_output, + "yaounde_context", + ) + write_single_geometry( + convex_hull_geometry, + analysis_crs, + hull_output, + "yaounde_convex_hull", + ) + + comparison_metrics: dict[str, Any] | None = None + if boundary_config.get("comparison_file") and config.get("comparison_boundary"): + comparison_path = resolve_project_path( + project_root, + boundary_config["comparison_file"], + ) + comparison_boundaries = gpd.read_file(comparison_path) + validate_source_boundary(comparison_boundaries, comparison_path) + selected_comparison = select_comparison_boundary( + comparison_boundaries, + config["comparison_boundary"], + ).to_crs(analysis_crs) + comparison_geometry = make_valid(selected_comparison.geometry.union_all()) + comparison_metrics = compare_adm3_union_with_adm2( + core_geometry, + comparison_geometry, + ) + + reference_output_value = output_config.get("adm2_reference_boundary") + if reference_output_value: + reference_output = resolve_project_path(project_root, reference_output_value) + write_geodataframe( + selected_comparison, + reference_output, + "mfoundi_adm2_reference", + ) + + comparison_report_value = output_config.get("boundary_comparison_metrics") + if comparison_report_value: + comparison_report_path = resolve_project_path( + project_root, + comparison_report_value, + ) + write_json(comparison_metrics, comparison_report_path) + + resolution = float(grid_config["resolution_m"]) + anchor_x = float(grid_config["anchor_x_m"]) + anchor_y = float(grid_config["anchor_y_m"]) + nodata = int(grid_config.get("nodata", 0)) + + if resolution <= 0: + raise ValueError("Grid resolution must be strictly positive.") + + xmin, ymin, xmax, ymax = context_geometry.bounds + snapped_xmin = snap_down(xmin, resolution, anchor_x) + snapped_ymin = snap_down(ymin, resolution, anchor_y) + snapped_xmax = snap_up(xmax, resolution, anchor_x) + snapped_ymax = snap_up(ymax, resolution, anchor_y) + + width = int(round((snapped_xmax - snapped_xmin) / resolution)) + height = int(round((snapped_ymax - snapped_ymin) / resolution)) + if width <= 0 or height <= 0: + raise ValueError(f"Invalid raster dimensions: width={width}, height={height}.") + + transform = from_origin(snapped_xmin, snapped_ymax, resolution, resolution) + output_shape = (height, width) + + context_mask = rasterize( + [(context_geometry, 1)], + out_shape=output_shape, + transform=transform, + fill=0, + all_touched=False, + dtype="uint8", + ) + core_mask = rasterize( + [(core_geometry, 1)], + out_shape=output_shape, + transform=transform, + fill=0, + all_touched=False, + dtype="uint8", + ) + + if not np.all(core_mask <= context_mask): + raise ValueError("The rasterised core mask is not fully contained in the context mask.") + + context_mask_output = resolve_project_path(project_root, output_config["context_mask"]) + core_mask_output = resolve_project_path(project_root, output_config["core_mask"]) + grid_template_output = resolve_project_path(project_root, output_config["grid_template"]) + + write_mask(context_mask_output, context_mask, transform, analysis_crs, nodata) + write_mask(core_mask_output, core_mask, transform, analysis_crs, nodata) + write_mask(grid_template_output, context_mask, transform, analysis_crs, nodata) + + cell_table = build_cell_table( + context_mask, + core_mask, + transform, + analysis_crs, + grid_config, + ) + cell_output = resolve_project_path(project_root, output_config["cell_table"]) + cell_output.parent.mkdir(parents=True, exist_ok=True) + cell_table.to_parquet(cell_output, index=False) + + vector_core_area_m2 = float(core_geometry.area) + raster_core_area_m2 = float(core_mask.sum() * resolution**2) + relative_area_difference = ( + abs(raster_core_area_m2 - vector_core_area_m2) / vector_core_area_m2 + ) + + grid_id = str(cell_table["grid_id"].iloc[0]) + grid_version = int(cell_table["grid_version"].iloc[0]) + grid_specification = { + "grid_id": grid_id, + "grid_version": grid_version, + "crs": analysis_crs.to_string(), + "epsg": analysis_crs.to_epsg(), + "resolution_m": resolution, + "anchor_x_m": anchor_x, + "anchor_y_m": anchor_y, + "extent": { + "xmin": snapped_xmin, + "ymin": snapped_ymin, + "xmax": snapped_xmax, + "ymax": snapped_ymax, + }, + "width": width, + "height": height, + "transform": list(transform)[:6], + "inclusion_rule": str(grid_config["inclusion_rule"]), + "extent_geometry": str(grid_config["extent_geometry"]), + "context_buffer_m": context_buffer_m, + "context_cell_count": int(context_mask.sum()), + "core_cell_count": int(core_mask.sum()), + "cell_id_pattern": f"EPSG{analysis_crs.to_epsg()}_{resolution:g}m__", + } + + boundary_report: dict[str, Any] = { + "source_file": str(source_path.relative_to(project_root)), + "source_sha256": source_hash, + "input_crs": str(boundaries.crs), + "analysis_crs": analysis_crs.to_string(), + "input_feature_count": input_feature_count, + "selected_feature_count": len(selected), + "selected_names": selected[str(boundary_config["name_field"])].astype(str).tolist(), + "selected_pcodes": ( + selected[str(boundary_config["pcode_field"])].astype(str).tolist() + if boundary_config.get("pcode_field") + else [] + ), + "parent_name": boundary_config.get("parent_name"), + "missing_geometry_count_before_fix": missing_before, + "empty_geometry_count_before_fix": empty_before, + "invalid_geometry_count_before_fix": invalid_before, + "duplicate_geometry_count_before_fix": duplicate_before, + "geometry_types_before_fix": geometry_types_before, + "geometry_fix_operation": str(boundary_config["geometry_fix"]), + "invalid_core_after_fix": bool(not core_geometry.is_valid), + "core_area_vector_km2": vector_core_area_m2 / 1_000_000, + "core_area_rasterised_km2": raster_core_area_m2 / 1_000_000, + "relative_vector_raster_area_difference": relative_area_difference, + "context_area_km2": float(context_geometry.area) / 1_000_000, + "convex_hull_area_km2": float(convex_hull_geometry.area) / 1_000_000, + "core_within_context": bool(context_geometry.covers(core_geometry)), + "adm2_adm3_comparison": comparison_metrics, + "grid_specification": grid_specification, + } + + grid_spec_path = resolve_project_path(project_root, output_config["grid_specification"]) + boundary_report_path = resolve_project_path(project_root, output_config["boundary_report"]) + write_json(grid_specification, grid_spec_path) + write_json(boundary_report, boundary_report_path) + + return boundary_report + + +def parse_arguments() -> argparse.Namespace: + """Parse command-line arguments for the study-grid construction command. + + Returns: + Namespace containing the required ``--config`` path. + """ + parser = argparse.ArgumentParser( + description="Build the Yaoundé study geometries and deterministic 30 m grid." + ) + parser.add_argument( + "--config", + required=True, + type=Path, + help="Path to configs/study_area.yaml.", + ) + return parser.parse_args() + + +def main() -> None: + """Run the pipeline from the command line and print the final JSON report.""" + arguments = parse_arguments() + report = build_study_grid(arguments.config) + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/src/analysis/boundaries/inspect_boundaries.py b/src/analysis/boundaries/inspect_boundaries.py new file mode 100644 index 0000000..fd6554b --- /dev/null +++ b/src/analysis/boundaries/inspect_boundaries.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +import argparse +import json +import unicodedata +from pathlib import Path + +import geopandas as gpd +import pandas as pd + + +def normalise_text(value: object) -> str: + """Return lowercase text without accents for robust name searching.""" + if value is None: + return "" + + text = str(value) + text = unicodedata.normalize("NFKD", text) + text = "".join(char for char in text if not unicodedata.combining(char)) + return text.casefold().strip() + + +def inspect_boundary(path: Path) -> dict: + path = path.expanduser().resolve() + + if not path.exists(): + raise FileNotFoundError( + f"Boundary file not found: {path}\n" + f"Current working directory: {Path.cwd()}\n" + "Run the command from the repository root and verify the input path." + ) + + if not path.is_file(): + raise ValueError(f"Boundary input is not a file: {path}") + + gdf = gpd.read_file(path) + + report = { + "file": str(path), + "feature_count": len(gdf), + "crs": str(gdf.crs), + "columns": list(gdf.columns), + "geometry_types": gdf.geometry.geom_type.value_counts().to_dict(), + "missing_geometry_count": int(gdf.geometry.isna().sum()), + "empty_geometry_count": int(gdf.geometry.is_empty.sum()), + "invalid_geometry_count": int((~gdf.geometry.is_valid).sum()), + "duplicate_geometry_count": int(gdf.geometry.duplicated().sum()), + "bounds": list(map(float, gdf.total_bounds)), + } + + print("\n=== BASIC INFORMATION ===") + print(json.dumps(report, indent=2)) + + print("\n=== ATTRIBUTE COLUMNS ===") + for column in gdf.columns: + if column != gdf.geometry.name: + print(f"\n{column}:") + print(gdf[column].dropna().astype(str).head(15).tolist()) + + print("\n=== POSSIBLE YAOUNDE / MFOUNDI RECORDS ===") + + searchable_columns = [ + column + for column in gdf.columns + if column != gdf.geometry.name + and ( + pd.api.types.is_string_dtype(gdf[column]) + or gdf[column].dtype == object + ) + ] + + matched_rows: set[int] = set() + + for index, row in gdf.iterrows(): + combined = " | ".join( + normalise_text(row[column]) + for column in searchable_columns + ) + + if "yaounde" in combined or "mfoundi" in combined: + matched_rows.add(index) + + if not matched_rows: + print("No matching record found.") + else: + display_columns = searchable_columns + [gdf.geometry.name] + print(gdf.loc[sorted(matched_rows), display_columns].drop( + columns=[gdf.geometry.name] + ).to_string()) + + return report + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", required=True, type=Path) + parser.add_argument( + "--report", + default=Path("data/metadata/source_boundary_inspection.json"), + type=Path, + ) + args = parser.parse_args() + + report = inspect_boundary(args.input) + + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text( + json.dumps(report, indent=2), + encoding="utf-8", + ) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/analysis/boundaries/visualize_study_grid.py b/src/analysis/boundaries/visualize_study_grid.py new file mode 100644 index 0000000..9947d8d --- /dev/null +++ b/src/analysis/boundaries/visualize_study_grid.py @@ -0,0 +1,510 @@ +"""Generate reproducible visual quality-control figures for the Day 1 grid. + +The figures compare the selected Yaoundé ADM3 units with ADM2 Mfoundi, display +all constructed study geometries, show the rasterised core and context masks, +and provide a detailed grid zoom with example stable cell identifiers. + +The script is intended for reproducible quality assurance. QGIS may still be +used for interactive inspection, but these exported figures document the exact +outputs produced by the Python pipeline. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import geopandas as gpd +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import rasterio +import yaml + + +def load_configuration(config_path: Path) -> tuple[dict[str, Any], Path]: + """Load the YAML configuration and infer the repository root. + + Args: + config_path: Path to ``configs/study_area.yaml``. + + Returns: + Parsed configuration and absolute repository-root path. + + Raises: + FileNotFoundError: If the configuration file does not exist. + ValueError: If the YAML document is empty or invalid. + """ + resolved_config_path = config_path.expanduser().resolve() + if not resolved_config_path.is_file(): + raise FileNotFoundError(f"Configuration file not found: {resolved_config_path}") + + with resolved_config_path.open("r", encoding="utf-8") as file: + configuration = yaml.safe_load(file) + + if not isinstance(configuration, dict): + raise ValueError("The study-area configuration must contain a YAML mapping.") + + return configuration, resolved_config_path.parent.parent + + +def resolve_project_path(project_root: Path, path_value: str | Path) -> Path: + """Resolve an absolute or project-relative configured path. + + Args: + project_root: Absolute path to the repository root. + path_value: Path value read from the YAML configuration. + + Returns: + Absolute normalised path. + """ + path = Path(path_value).expanduser() + return path.resolve() if path.is_absolute() else (project_root / path).resolve() + + +def save_figure(figure: plt.Figure, output_path: Path) -> None: + """Save a Matplotlib figure as a PNG and release its memory. + + Args: + figure: Figure to save. + output_path: Destination PNG path. + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(output_path, dpi=200, bbox_inches="tight") + plt.close(figure) + + +def select_configured_features( + geodataframe: gpd.GeoDataFrame, + name_field: str, + selected_names: list[str], + pcode_field: str | None = None, + selected_pcodes: list[str] | None = None, +) -> gpd.GeoDataFrame: + """Select configured administrative features using P-codes or names. + + P-codes are used when both the field and values are available. Names remain + the fallback so the visualisation script also works with simpler boundary + configurations. + + Args: + geodataframe: Source administrative features. + name_field: Field containing administrative-unit names. + selected_names: Exact names expected in the source. + pcode_field: Optional field containing stable administrative P-codes. + selected_pcodes: Optional P-codes to select. + + Returns: + Copy of the selected features. + + Raises: + KeyError: If the required selection field is absent. + ValueError: If no matching feature is found. + """ + if pcode_field and selected_pcodes: + if pcode_field not in geodataframe.columns: + raise KeyError(f"Configured P-code field is absent: {pcode_field}") + selected = geodataframe[ + geodataframe[pcode_field].astype(str).isin(selected_pcodes) + ].copy() + else: + if name_field not in geodataframe.columns: + raise KeyError(f"Configured name field is absent: {name_field}") + selected = geodataframe[ + geodataframe[name_field].astype(str).isin(selected_names) + ].copy() + + if selected.empty: + raise ValueError("The configured administrative selection returned no features.") + + return selected + + +def load_visualisation_inputs( + config: dict[str, Any], + project_root: Path, +) -> dict[str, Any]: + """Load all vector, raster, and cell-table inputs needed by the figures. + + Args: + config: Parsed study-area configuration. + project_root: Absolute repository-root path. + + Returns: + Dictionary containing selected ADM3 units, ADM2 Mfoundi, constructed + geometries, raster masks, raster metadata, and the cell table. + """ + boundary = config["boundary"] + comparison = config["comparison_boundary"] + outputs = config["outputs"] + analysis_crs = config["projection"]["analysis_crs"] + + adm3 = gpd.read_file(resolve_project_path(project_root, boundary["source_file"])) + selected_adm3 = select_configured_features( + adm3, + name_field=str(boundary["name_field"]), + selected_names=[str(value) for value in boundary["selected_names"]], + pcode_field=boundary.get("pcode_field"), + selected_pcodes=[str(value) for value in boundary.get("selected_pcodes", [])], + ).to_crs(analysis_crs) + + adm2 = gpd.read_file(resolve_project_path(project_root, boundary["comparison_file"])) + selected_adm2 = select_configured_features( + adm2, + name_field=str(comparison["name_field"]), + selected_names=[str(comparison["selected_name"])], + pcode_field=comparison.get("pcode_field"), + selected_pcodes=( + [str(comparison["selected_pcode"])] + if comparison.get("selected_pcode") + else None + ), + ).to_crs(analysis_crs) + + if len(selected_adm2) != 1: + raise ValueError(f"Expected one Mfoundi ADM2 feature, found {len(selected_adm2)}.") + + core = gpd.read_file(resolve_project_path(project_root, outputs["core_boundary"])) + context = gpd.read_file(resolve_project_path(project_root, outputs["context_boundary"])) + hull = gpd.read_file(resolve_project_path(project_root, outputs["convex_hull"])) + cells = pd.read_parquet(resolve_project_path(project_root, outputs["cell_table"])) + + core_mask_path = resolve_project_path(project_root, outputs["core_mask"]) + context_mask_path = resolve_project_path(project_root, outputs["context_mask"]) + + with rasterio.open(context_mask_path) as raster: + context_mask = raster.read(1) + raster_bounds = raster.bounds + raster_transform = raster.transform + raster_crs = raster.crs + + with rasterio.open(core_mask_path) as raster: + core_mask = raster.read(1) + + return { + "selected_adm3": selected_adm3, + "selected_adm2": selected_adm2, + "core": core, + "context": context, + "hull": hull, + "cells": cells, + "core_mask": core_mask, + "context_mask": context_mask, + "raster_bounds": raster_bounds, + "raster_transform": raster_transform, + "raster_crs": raster_crs, + } + + +def plot_boundary_overview(inputs: dict[str, Any], output_path: Path) -> None: + """Plot selected ADM3 units and all constructed study geometries together. + + The overview makes it possible to inspect whether the seven subdivisions form + a coherent core, whether Mfoundi agrees with that core, and how the context + buffer and convex hull differ from the reporting boundary. + + Args: + inputs: Dictionary returned by :func:`load_visualisation_inputs`. + output_path: Destination PNG path. + """ + figure, axis = plt.subplots(figsize=(10, 10)) + + inputs["context"].boundary.plot( + ax=axis, + linewidth=1.2, + linestyle=":", + label="5 km context buffer", + ) + inputs["hull"].boundary.plot( + ax=axis, + linewidth=1.2, + linestyle="--", + label="Convex hull", + ) + inputs["selected_adm2"].boundary.plot( + ax=axis, + linewidth=2.5, + label="ADM2 Mfoundi", + ) + inputs["selected_adm3"].boundary.plot( + ax=axis, + linewidth=1.0, + label="Selected ADM3 units", + ) + inputs["core"].boundary.plot( + ax=axis, + linewidth=2.0, + label="Dissolved Yaoundé core", + ) + + axis.set_title("Yaoundé study boundaries") + axis.set_xlabel("Easting (m)") + axis.set_ylabel("Northing (m)") + axis.set_aspect("equal") + axis.legend() + save_figure(figure, output_path) + + +def plot_adm2_adm3_comparison( + inputs: dict[str, Any], + output_path: Path, +) -> dict[str, float]: + """Plot and quantify the difference between ADM3 union and ADM2 Mfoundi. + + Args: + inputs: Dictionary returned by :func:`load_visualisation_inputs`. + output_path: Destination PNG path. + + Returns: + Area metrics describing the overlap and symmetric difference. + """ + adm3_union = inputs["selected_adm3"].geometry.union_all() + adm2_geometry = inputs["selected_adm2"].geometry.union_all() + symmetric_difference = adm3_union.symmetric_difference(adm2_geometry) + reference_area = float(adm2_geometry.area) + difference_ratio = ( + float(symmetric_difference.area) / reference_area if reference_area > 0 else math.nan + ) + + figure, axis = plt.subplots(figsize=(10, 10)) + inputs["selected_adm2"].boundary.plot( + ax=axis, + linewidth=2.5, + label="ADM2 Mfoundi", + ) + inputs["selected_adm3"].boundary.plot( + ax=axis, + linewidth=1.0, + label="Seven ADM3 units", + ) + + if not symmetric_difference.is_empty: + difference_frame = gpd.GeoDataFrame( + {"comparison": ["symmetric_difference"]}, + geometry=[symmetric_difference], + crs=inputs["selected_adm2"].crs, + ) + difference_frame.plot(ax=axis, alpha=0.5, label="Difference") + + axis.set_title( + "ADM3 union compared with ADM2 Mfoundi\n" + f"Symmetric difference ratio: {difference_ratio:.6%}" + ) + axis.set_xlabel("Easting (m)") + axis.set_ylabel("Northing (m)") + axis.set_aspect("equal") + axis.legend() + save_figure(figure, output_path) + + return { + "adm3_union_area_km2": float(adm3_union.area) / 1_000_000, + "mfoundi_area_km2": reference_area / 1_000_000, + "symmetric_difference_area_km2": ( + float(symmetric_difference.area) / 1_000_000 + ), + "symmetric_difference_ratio": difference_ratio, + } + + +def plot_core_context_masks(inputs: dict[str, Any], output_path: Path) -> None: + """Display the complete 30 m context and core masks over the vector core. + + Args: + inputs: Dictionary returned by :func:`load_visualisation_inputs`. + output_path: Destination PNG path. + """ + bounds = inputs["raster_bounds"] + raster_extent = [bounds.left, bounds.right, bounds.bottom, bounds.top] + + figure, axis = plt.subplots(figsize=(10, 10)) + axis.imshow( + np.ma.masked_where(inputs["context_mask"] == 0, inputs["context_mask"]), + extent=raster_extent, + origin="upper", + alpha=0.4, + ) + axis.imshow( + np.ma.masked_where(inputs["core_mask"] == 0, inputs["core_mask"]), + extent=raster_extent, + origin="upper", + alpha=0.8, + ) + inputs["core"].boundary.plot(ax=axis, linewidth=1.5) + + axis.set_title("30 m context and core masks") + axis.set_xlabel("Easting (m)") + axis.set_ylabel("Northing (m)") + axis.set_aspect("equal") + save_figure(figure, output_path) + + +def choose_boundary_zoom_point(core_geometry: Any) -> tuple[float, float]: + """Choose a reproducible point on the core boundary for the grid-detail view. + + A boundary point is preferable to the polygon centroid because it allows the + zoomed figure to show both included and excluded grid cells around the exact + administrative edge. + + Args: + core_geometry: Dissolved Yaoundé core geometry. + + Returns: + Easting and northing of a deterministic point on the exterior boundary. + """ + boundary = core_geometry.boundary + point = boundary.interpolate(0.25, normalized=True) + return float(point.x), float(point.y) + + +def plot_grid_alignment_zoom( + inputs: dict[str, Any], + grid_config: dict[str, Any], + output_path: Path, +) -> None: + """Plot a detailed 30 m grid zoom and annotate example stable cell IDs. + + The figure demonstrates that grid lines are anchored globally and that cell + identity comes from global x/y grid indices, not from mutable local raster row + and column positions. + + Args: + inputs: Dictionary returned by :func:`load_visualisation_inputs`. + grid_config: ``grid`` section of ``study_area.yaml``. + output_path: Destination PNG path. + """ + resolution = float(grid_config["resolution_m"]) + anchor_x = float(grid_config["anchor_x_m"]) + anchor_y = float(grid_config["anchor_y_m"]) + half_window = float(grid_config.get("visual_zoom_half_window_m", 300.0)) + + core_geometry = inputs["core"].geometry.union_all() + centre_x, centre_y = choose_boundary_zoom_point(core_geometry) + xmin, xmax = centre_x - half_window, centre_x + half_window + ymin, ymax = centre_y - half_window, centre_y + half_window + + first_x = math.floor((xmin - anchor_x) / resolution) * resolution + anchor_x + first_y = math.floor((ymin - anchor_y) / resolution) * resolution + anchor_y + + figure, axis = plt.subplots(figsize=(11, 10)) + inputs["core"].boundary.plot(ax=axis, linewidth=2.0) + + for x_value in np.arange(first_x, xmax + resolution, resolution): + axis.axvline(x_value, linewidth=0.35) + for y_value in np.arange(first_y, ymax + resolution, resolution): + axis.axhline(y_value, linewidth=0.35) + + visible_cells = inputs["cells"][ + inputs["cells"]["x_center_m"].between(xmin, xmax) + & inputs["cells"]["y_center_m"].between(ymin, ymax) + ].copy() + + if not visible_cells.empty: + label_count = min(9, len(visible_cells)) + label_indices = np.linspace(0, len(visible_cells) - 1, label_count).astype(int) + labelled_cells = visible_cells.iloc[label_indices] + + for row in labelled_cells.itertuples(index=False): + short_label = f"{row.grid_x_index}_{row.grid_y_index}" + axis.text( + row.x_center_m, + row.y_center_m, + short_label, + ha="center", + va="center", + fontsize=6, + ) + + axis.set_xlim(xmin, xmax) + axis.set_ylim(ymin, ymax) + axis.set_title( + "Authoritative 30 m grid and example global cell indices\n" + f"Anchor: ({anchor_x:.0f}, {anchor_y:.0f})" + ) + axis.set_xlabel("Easting (m)") + axis.set_ylabel("Northing (m)") + axis.set_aspect("equal") + save_figure(figure, output_path) + + +def generate_visual_report(config_path: Path) -> dict[str, Any]: + """Generate all Day 1 visual QA figures and comparison metrics. + + Args: + config_path: Path to the study-area YAML configuration. + + Returns: + Dictionary listing the output directory and ADM2/ADM3 comparison metrics. + """ + config, project_root = load_configuration(config_path) + outputs = config["outputs"] + inputs = load_visualisation_inputs(config, project_root) + + output_directory = resolve_project_path( + project_root, + outputs.get("visual_report_dir", "reports/day1"), + ) + output_directory.mkdir(parents=True, exist_ok=True) + + plot_boundary_overview(inputs, output_directory / "01_boundary_overview.png") + comparison_metrics = plot_adm2_adm3_comparison( + inputs, + output_directory / "02_adm2_adm3_comparison.png", + ) + plot_core_context_masks(inputs, output_directory / "03_core_context_masks.png") + plot_grid_alignment_zoom( + inputs, + config["grid"], + output_directory / "04_grid_alignment_zoom.png", + ) + + comparison_output = outputs.get("boundary_comparison_metrics") + if comparison_output: + comparison_path = resolve_project_path(project_root, comparison_output) + comparison_path.parent.mkdir(parents=True, exist_ok=True) + comparison_path.write_text( + json.dumps(comparison_metrics, indent=2), + encoding="utf-8", + ) + + return { + "visual_report_directory": str(output_directory.relative_to(project_root)), + "generated_figures": [ + "01_boundary_overview.png", + "02_adm2_adm3_comparison.png", + "03_core_context_masks.png", + "04_grid_alignment_zoom.png", + ], + "comparison": comparison_metrics, + } + + +def parse_arguments() -> argparse.Namespace: + """Parse command-line arguments for visual report generation. + + Returns: + Namespace containing the required ``--config`` path. + """ + parser = argparse.ArgumentParser( + description="Generate Day 1 boundary and grid quality-control figures." + ) + parser.add_argument( + "--config", + required=True, + type=Path, + help="Path to configs/study_area.yaml.", + ) + return parser.parse_args() + + +def main() -> None: + """Generate the visual report and print a concise JSON output summary.""" + arguments = parse_arguments() + report = generate_visual_report(arguments.config) + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tests/reference/grid_v1.json b/tests/reference/grid_v1.json new file mode 100644 index 0000000..e73f5a4 --- /dev/null +++ b/tests/reference/grid_v1.json @@ -0,0 +1,31 @@ +{ + "grid_id": "yaounde_epsg32632_30m_anchor0_v1", + "grid_version": 1, + "crs": "EPSG:32632", + "epsg": 32632, + "resolution_m": 30.0, + "anchor_x_m": 0.0, + "anchor_y_m": 0.0, + "extent": { + "xmin": 762570.0, + "ymin": 405810.0, + "xmax": 790950.0, + "ymax": 444150.0 + }, + "width": 946, + "height": 1278, + "transform": [ + 30.0, + 0.0, + 762570.0, + 0.0, + -30.0, + 444150.0 + ], + "inclusion_rule": "pixel_center", + "extent_geometry": "context_buffer", + "context_buffer_m": 5000.0, + "context_cell_count": 879666, + "core_cell_count": 321909, + "cell_id_pattern": "EPSG32632_30m__" +} \ No newline at end of file diff --git a/tests/test_study_grid.py b/tests/test_study_grid.py new file mode 100644 index 0000000..73f0079 --- /dev/null +++ b/tests/test_study_grid.py @@ -0,0 +1,482 @@ +"""Quality-assurance tests for the Yaoundé boundaries and deterministic grid. + +These tests combine unit-style checks of grid invariants with integration checks +that read the generated GeoPackage, GeoTIFF, Parquet, JSON, and source boundary +files. Run ``make build-grid`` before executing this module. +""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +from typing import Any + +import geopandas as gpd +import numpy as np +import pandas as pd +import pytest +import rasterio +import yaml +from pyproj import CRS + + +PROJECT_ROOT = Path(__file__).resolve().parents[1] +CONFIG_PATH = PROJECT_ROOT / "configs/study_area.yaml" + + +def project_path(path_value: str | Path) -> Path: + """Resolve a test input or output path relative to the repository root. + + Args: + path_value: Absolute or repository-relative path. + + Returns: + Absolute normalised path. + """ + path = Path(path_value).expanduser() + return path.resolve() if path.is_absolute() else (PROJECT_ROOT / path).resolve() + + +def sha256_file(path: Path) -> str: + """Calculate a file's SHA-256 checksum in memory-efficient chunks. + + Args: + path: File to hash. + + Returns: + Lowercase hexadecimal digest. + """ + digest = hashlib.sha256() + with path.open("rb") as file: + for chunk in iter(lambda: file.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +@pytest.fixture(scope="session") +def config() -> dict[str, Any]: + """Load the authoritative study-area configuration once per test session.""" + with CONFIG_PATH.open("r", encoding="utf-8") as file: + loaded_config = yaml.safe_load(file) + + if not isinstance(loaded_config, dict): + raise ValueError("The study-area configuration must contain a YAML mapping.") + + return loaded_config + + +@pytest.fixture(scope="session") +def boundary_report(config: dict[str, Any]) -> dict[str, Any]: + """Load the generated boundary-processing report once per test session.""" + report_path = project_path(config["outputs"]["boundary_report"]) + with report_path.open("r", encoding="utf-8") as file: + return json.load(file) + + +@pytest.fixture(scope="session") +def grid_specification(config: dict[str, Any]) -> dict[str, Any]: + """Load the generated authoritative grid specification.""" + specification_path = project_path(config["outputs"]["grid_specification"]) + with specification_path.open("r", encoding="utf-8") as file: + return json.load(file) + + +@pytest.fixture(scope="session") +def core(config: dict[str, Any]) -> gpd.GeoDataFrame: + """Load the dissolved Yaoundé reporting core.""" + return gpd.read_file(project_path(config["outputs"]["core_boundary"])) + + +@pytest.fixture(scope="session") +def context(config: dict[str, Any]) -> gpd.GeoDataFrame: + """Load the buffered context geometry used for feature construction.""" + return gpd.read_file(project_path(config["outputs"]["context_boundary"])) + + +@pytest.fixture(scope="session") +def hull(config: dict[str, Any]) -> gpd.GeoDataFrame: + """Load the convex-hull sensitivity geometry.""" + return gpd.read_file(project_path(config["outputs"]["convex_hull"])) + + +@pytest.fixture(scope="session") +def cell_table(config: dict[str, Any]) -> pd.DataFrame: + """Load the model-ready table describing every context-grid cell.""" + return pd.read_parquet(project_path(config["outputs"]["cell_table"])) + + +# Source and provenance: required inputs must exist and remain traceable. + + +def test_source_and_metadata_files_exist(config: dict[str, Any]) -> None: + """Verify that source, comparison, metadata, and checksum files exist.""" + boundary = config["boundary"] + + assert project_path(boundary["source_file"]).is_file() + assert project_path(boundary["comparison_file"]).is_file() + assert project_path(boundary["metadata_file"]).is_file() + assert project_path(boundary["checksum_file"]).is_file() + + +def test_source_checksum_matches_generated_report( + config: dict[str, Any], + boundary_report: dict[str, Any], +) -> None: + """Verify that the reported source checksum matches the current ADM3 file.""" + source_path = project_path(config["boundary"]["source_file"]) + assert sha256_file(source_path) == boundary_report["source_sha256"] + + +# Administrative structure: Mfoundi and Yaoundé I–VII must be unambiguous. + + +def test_configured_administrative_fields_exist(config: dict[str, Any]) -> None: + """Verify that all configured name, parent, and P-code fields exist.""" + boundary = config["boundary"] + comparison = config["comparison_boundary"] + adm3 = gpd.read_file(project_path(boundary["source_file"])) + adm2 = gpd.read_file(project_path(boundary["comparison_file"])) + + for field in [boundary["name_field"], boundary["parent_field"], boundary["pcode_field"]]: + assert field in adm3.columns + + for field in [comparison["name_field"], comparison["pcode_field"]]: + assert field in adm2.columns + + +def test_exactly_seven_yaounde_units_are_selected(config: dict[str, Any]) -> None: + """Verify the seven configured Yaoundé ADM3 names, P-codes, and parent.""" + boundary = config["boundary"] + adm3 = gpd.read_file(project_path(boundary["source_file"])) + selected = adm3[ + adm3[boundary["pcode_field"]].astype(str).isin(boundary["selected_pcodes"]) + ].copy() + + assert len(selected) == int(boundary["expected_selected_units"]) + assert set(selected[boundary["name_field"]].astype(str)) == set( + boundary["selected_names"] + ) + assert set(selected[boundary["pcode_field"]].astype(str)) == set( + boundary["selected_pcodes"] + ) + assert set(selected[boundary["parent_field"]].astype(str)) == { + str(boundary["parent_name"]) + } + + +def test_mfoundi_exists_once_in_adm2(config: dict[str, Any]) -> None: + """Verify that the configured ADM2 P-code identifies exactly one Mfoundi.""" + boundary = config["boundary"] + comparison = config["comparison_boundary"] + adm2 = gpd.read_file(project_path(boundary["comparison_file"])) + selected = adm2[ + adm2[comparison["pcode_field"]].astype(str) + == str(comparison["selected_pcode"]) + ] + + assert len(selected) == 1 + assert str(selected.iloc[0][comparison["name_field"]]) == str( + comparison["selected_name"] + ) + + +# Source geometry quality: missing, empty, invalid, and duplicate data are forbidden. + + +def test_source_geometry_quality(config: dict[str, Any]) -> None: + """Verify source geometry completeness, validity, uniqueness, and polygon type.""" + adm3 = gpd.read_file(project_path(config["boundary"]["source_file"])) + + assert int(adm3.geometry.isna().sum()) == 0 + assert int(adm3.geometry.is_empty.sum()) == 0 + assert int((~adm3.geometry.is_valid).sum()) == 0 + assert int(adm3.geometry.duplicated().sum()) == 0 + assert set(adm3.geometry.geom_type.unique()) <= {"Polygon", "MultiPolygon"} + + +def test_geometry_repair_is_documented( + config: dict[str, Any], + boundary_report: dict[str, Any], +) -> None: + """Verify that the configured geometry-repair policy appears in the report.""" + expected_operation = str(config["boundary"]["geometry_fix"]) + assert boundary_report["geometry_fix_operation"] == expected_operation + assert "invalid_geometry_count_before_fix" in boundary_report + assert "invalid_core_after_fix" in boundary_report + + +# ADM2/ADM3 consistency: the seven-unit union should closely reproduce Mfoundi. + + +def test_adm3_union_matches_adm2_mfoundi(config: dict[str, Any]) -> None: + """Verify that ADM3 union and ADM2 Mfoundi differ by less than the tolerance.""" + boundary = config["boundary"] + comparison = config["comparison_boundary"] + analysis_crs = config["projection"]["analysis_crs"] + + adm3 = gpd.read_file(project_path(boundary["source_file"])).to_crs(analysis_crs) + selected_adm3 = adm3[ + adm3[boundary["pcode_field"]].astype(str).isin(boundary["selected_pcodes"]) + ] + + adm2 = gpd.read_file(project_path(boundary["comparison_file"])).to_crs(analysis_crs) + selected_adm2 = adm2[ + adm2[comparison["pcode_field"]].astype(str) + == str(comparison["selected_pcode"]) + ] + + adm3_union = selected_adm3.geometry.union_all() + adm2_geometry = selected_adm2.geometry.union_all() + difference_ratio = ( + adm3_union.symmetric_difference(adm2_geometry).area / adm2_geometry.area + ) + maximum_ratio = float( + config.get("quality_thresholds", {}).get( + "adm2_adm3_max_symmetric_difference_ratio", + 0.02, + ) + ) + + assert difference_ratio < maximum_ratio + + +# Projection: raw inputs stay geographic and processed outputs use metric UTM. + + +def test_source_is_preserved_in_geographic_crs(config: dict[str, Any]) -> None: + """Verify that the raw ADM3 source retains its original EPSG:4326 CRS.""" + source = gpd.read_file(project_path(config["boundary"]["source_file"])) + expected_epsg = CRS.from_user_input( + config["projection"]["geographic_crs"] + ).to_epsg() + + assert source.crs is not None + assert source.crs.to_epsg() == expected_epsg == 4326 + + +def test_processed_geometries_use_analysis_crs( + config: dict[str, Any], + core: gpd.GeoDataFrame, + context: gpd.GeoDataFrame, + hull: gpd.GeoDataFrame, +) -> None: + """Verify that every processed geometry uses projected metre-based UTM 32N.""" + expected_crs = CRS.from_user_input(config["projection"]["analysis_crs"]) + + assert expected_crs.to_epsg() == 32632 + assert expected_crs.is_projected + assert expected_crs.axis_info[0].unit_name.lower() == "metre" + assert core.crs.to_epsg() == expected_crs.to_epsg() + assert context.crs.to_epsg() == expected_crs.to_epsg() + assert hull.crs.to_epsg() == expected_crs.to_epsg() + + +# Constructed geometries: outputs must exist, be valid, and preserve nesting. + + +def test_constructed_geometry_files_exist(config: dict[str, Any]) -> None: + """Verify that the core, context, and convex-hull GeoPackages were created.""" + outputs = config["outputs"] + assert project_path(outputs["core_boundary"]).is_file() + assert project_path(outputs["context_boundary"]).is_file() + assert project_path(outputs["convex_hull"]).is_file() + + +def test_constructed_geometries_are_valid( + core: gpd.GeoDataFrame, + context: gpd.GeoDataFrame, + hull: gpd.GeoDataFrame, +) -> None: + """Verify that all constructed geometries are non-empty and spatially valid.""" + for geodataframe in [core, context, hull]: + assert geodataframe.geometry.is_valid.all() + assert not geodataframe.geometry.is_empty.any() + + +def test_context_completely_covers_core( + core: gpd.GeoDataFrame, + context: gpd.GeoDataFrame, +) -> None: + """Verify that the context buffer fully covers the reporting core.""" + assert context.geometry.iloc[0].covers(core.geometry.iloc[0]) + + +# Raster definition: all grid products must share one 30 m snapped transform. + + +def test_grid_rasters_share_the_same_definition(config: dict[str, Any]) -> None: + """Verify that template, core mask, and context mask share one exact grid.""" + outputs = config["outputs"] + raster_paths = [ + project_path(outputs["grid_template"]), + project_path(outputs["core_mask"]), + project_path(outputs["context_mask"]), + ] + specifications = [] + + for raster_path in raster_paths: + assert raster_path.is_file() + with rasterio.open(raster_path) as raster: + specifications.append( + ( + raster.crs, + raster.transform, + raster.width, + raster.height, + raster.res, + ) + ) + + assert all(specification == specifications[0] for specification in specifications) + + +def test_grid_resolution_anchor_and_extent(config: dict[str, Any]) -> None: + """Verify 30 m resolution and snapping to the configured global anchor.""" + grid = config["grid"] + resolution = float(grid["resolution_m"]) + anchor_x = float(grid["anchor_x_m"]) + anchor_y = float(grid["anchor_y_m"]) + raster_path = project_path(config["outputs"]["grid_template"]) + + with rasterio.open(raster_path) as raster: + assert raster.res == (resolution, resolution) + assert np.isclose((raster.transform.c - anchor_x) % resolution, 0) + assert np.isclose((raster.transform.f - anchor_y) % resolution, 0) + assert np.isclose((raster.bounds.left - anchor_x) % resolution, 0) + assert np.isclose((raster.bounds.right - anchor_x) % resolution, 0) + assert np.isclose((raster.bounds.bottom - anchor_y) % resolution, 0) + assert np.isclose((raster.bounds.top - anchor_y) % resolution, 0) + + +def test_core_mask_is_contained_in_context_mask(config: dict[str, Any]) -> None: + """Verify binary mask values and ensure every core cell is a context cell.""" + with rasterio.open(project_path(config["outputs"]["core_mask"])) as raster: + core_mask = raster.read(1) + with rasterio.open(project_path(config["outputs"]["context_mask"])) as raster: + context_mask = raster.read(1) + + assert set(np.unique(core_mask)) <= {0, 1} + assert set(np.unique(context_mask)) <= {0, 1} + assert np.all(core_mask <= context_mask) + + +def test_pixel_center_rule_is_documented(config: dict[str, Any]) -> None: + """Verify that the raster inclusion rule is explicitly fixed to pixel centre.""" + assert config["grid"]["inclusion_rule"] == "pixel_center" + + +# Cell identity: global indices must reproduce coordinates and stable identifiers. + + +def test_cell_identifiers_are_unique(cell_table: pd.DataFrame) -> None: + """Verify that every retained context cell has one unique stable identifier.""" + assert cell_table["cell_id"].is_unique + + +def test_cell_ids_are_derived_from_global_grid_indices( + config: dict[str, Any], + cell_table: pd.DataFrame, +) -> None: + """Reconstruct cell centres and IDs from global anchored grid indices.""" + resolution = float(config["grid"]["resolution_m"]) + anchor_x = float(config["grid"]["anchor_x_m"]) + anchor_y = float(config["grid"]["anchor_y_m"]) + epsg = CRS.from_user_input(config["projection"]["analysis_crs"]).to_epsg() + resolution_label = int(resolution) if resolution.is_integer() else resolution + + expected_x = anchor_x + (cell_table["grid_x_index"] + 0.5) * resolution + expected_y = anchor_y + (cell_table["grid_y_index"] + 0.5) * resolution + expected_ids = ( + f"EPSG{epsg}_{resolution_label}m_" + + cell_table["grid_x_index"].astype(str) + + "_" + + cell_table["grid_y_index"].astype(str) + ) + + assert np.allclose(cell_table["x_center_m"], expected_x) + assert np.allclose(cell_table["y_center_m"], expected_y) + assert (cell_table["cell_id"].astype(str) == expected_ids).all() + + +def test_cell_table_matches_raster_masks( + config: dict[str, Any], + cell_table: pd.DataFrame, +) -> None: + """Verify table row counts, core flags, and 900 m² area against masks.""" + with rasterio.open(project_path(config["outputs"]["core_mask"])) as raster: + core_mask = raster.read(1) + with rasterio.open(project_path(config["outputs"]["context_mask"])) as raster: + context_mask = raster.read(1) + + resolution = float(config["grid"]["resolution_m"]) + assert len(cell_table) == int(context_mask.sum()) + assert int(cell_table["in_core"].sum()) == int(core_mask.sum()) + assert (cell_table["cell_area_m2"] == resolution**2).all() + + +# Frozen reference: once accepted, grid v1 must not change unintentionally. + + +# def test_grid_matches_frozen_reference( +# config: dict[str, Any], +# grid_specification: dict[str, Any], +# ) -> None: +# """Compare the generated grid with the committed reference specification. + +# The test is skipped until ``grid.reference_specification`` is configured and +# the accepted JSON reference has been committed. Once present, any unintended +# change in CRS, resolution, anchor, extent, transform, dimensions, or cell +# counts causes the test to fail. +# """ +# reference_value = config["grid"].get("reference_specification") +# if not reference_value: +# pytest.skip("No frozen grid reference is configured yet.") + +# reference_path = project_path(reference_value) +# if not reference_path.is_file(): +# pytest.skip(f"Frozen grid reference does not exist yet: {reference_path}") + +# with reference_path.open("r", encoding="utf-8") as file: +# reference = json.load(file) + +# frozen_fields = [ +# "grid_id", +# "grid_version", +# "crs", +# "resolution_m", +# "anchor_x_m", +# "anchor_y_m", +# "extent", +# "width", +# "height", +# "transform", +# "inclusion_rule", +# "context_buffer_m", +# "context_cell_count", +# "core_cell_count", +# ] + +# for field in frozen_fields: +# assert grid_specification[field] == reference[field] + + +# Area consistency: rasterisation must preserve the vector core area closely. + + +def test_vector_raster_area_difference_is_small( + config: dict[str, Any], + boundary_report: dict[str, Any], +) -> None: + """Verify that vector and rasterised core areas differ below tolerance.""" + maximum_difference = float( + config.get("quality_thresholds", {}).get( + "max_vector_raster_area_difference_ratio", + 0.02, + ) + ) + observed_difference = float( + boundary_report["relative_vector_raster_area_difference"] + ) + + assert observed_difference < maximum_difference From 619a69b0dfe0ebdc6769a05c79f529ed3bf5f446 Mon Sep 17 00:00:00 2001 From: Omer-alt Date: Fri, 24 Jul 2026 14:54:59 +0100 Subject: [PATCH 2/5] Add reproducible Landsat catalogue and compositing protocol Catalogue Landsat 5, 7 and 8 scenes, calculate QA-valid coverage, evaluate common compositing windows, freeze exact scene identifiers, and add Day 2 reports and integrity tests. --- .gitignore | 31 +- Makefile | 27 +- README.md | 67 +- configs/landsat_catalog.yaml | 62 ++ .../landsat/candidate_window_details.csv | 577 +++++++++++++++ .../landsat/candidate_window_summary.csv | 49 ++ data/metadata/landsat/catalog_version.json | 7 + .../landsat/compositing_protocol.yaml | 98 +++ .../landsat/epoch_quality_summary.csv | 9 + .../metadata/landsat/monthly_availability.csv | 87 +++ .../landsat/monthly_availability.parquet | Bin 0 -> 9448 bytes data/metadata/landsat/scene_manifest_all.csv | 522 ++++++++++++++ .../landsat/scene_manifest_all.parquet | Bin 0 -> 57404 bytes .../landsat/selected_scene_manifest.csv | 8 + pyproject.toml | 1 + .../day2/01_scene_count_by_epoch_sensor.png | Bin 0 -> 46586 bytes .../day2/02_monthly_scene_availability.png | Bin 0 -> 102388 bytes reports/day2/03_monthly_valid_coverage.png | Bin 0 -> 85908 bytes .../day2/04_candidate_window_comparison.png | Bin 0 -> 164694 bytes reports/day2/05_selected_window_summary.png | Bin 0 -> 56614 bytes reports/day2/day2_landsat_report.md | 7 + src/analysis/landsat/__init__.py | 0 src/analysis/landsat/build_catalog.py | 654 ++++++++++++++++++ src/analysis/landsat/common.py | 104 +++ src/analysis/landsat/qa_masks.py | 48 ++ src/analysis/landsat/visualize_catalog.py | 77 +++ tests/reference/landsat_catalog_v1.json | 7 + tests/test_landsat_catalog.py | 119 ++++ 28 files changed, 2550 insertions(+), 11 deletions(-) create mode 100644 configs/landsat_catalog.yaml create mode 100644 data/metadata/landsat/candidate_window_details.csv create mode 100644 data/metadata/landsat/candidate_window_summary.csv create mode 100644 data/metadata/landsat/catalog_version.json create mode 100644 data/metadata/landsat/compositing_protocol.yaml create mode 100644 data/metadata/landsat/epoch_quality_summary.csv create mode 100644 data/metadata/landsat/monthly_availability.csv create mode 100644 data/metadata/landsat/monthly_availability.parquet create mode 100644 data/metadata/landsat/scene_manifest_all.csv create mode 100644 data/metadata/landsat/scene_manifest_all.parquet create mode 100644 data/metadata/landsat/selected_scene_manifest.csv create mode 100644 reports/day2/01_scene_count_by_epoch_sensor.png create mode 100644 reports/day2/02_monthly_scene_availability.png create mode 100644 reports/day2/03_monthly_valid_coverage.png create mode 100644 reports/day2/04_candidate_window_comparison.png create mode 100644 reports/day2/05_selected_window_summary.png create mode 100644 reports/day2/day2_landsat_report.md create mode 100644 src/analysis/landsat/__init__.py create mode 100644 src/analysis/landsat/build_catalog.py create mode 100644 src/analysis/landsat/common.py create mode 100644 src/analysis/landsat/qa_masks.py create mode 100644 src/analysis/landsat/visualize_catalog.py create mode 100644 tests/reference/landsat_catalog_v1.json create mode 100644 tests/test_landsat_catalog.py diff --git a/.gitignore b/.gitignore index 1fd9b41..8dda560 100644 --- a/.gitignore +++ b/.gitignore @@ -62,14 +62,31 @@ ENV/ tmp/ temp/ -# Large files -*.tif -*.parquet -*.gpkg -cmr_admin_boundaries.geojson.zip - # Virtual environments .venv/ .venvt/ venv/ -env/ \ No newline at end of file +env/ + +# Raw downloaded geospatial data +data/raw/boundaries/**/*.zip +data/raw/boundaries/**/*.geojson +data/raw/boundaries/**/*.shp +data/raw/boundaries/**/*.shx +data/raw/boundaries/**/*.dbf +data/raw/boundaries/**/*.prj +data/raw/boundaries/**/*.gpkg +data/raw/boundaries/hdx_cod_ab_cmr/extracted/ + +# Generated geospatial outputs +data/processed/**/*.tif +data/processed/**/*.tiff +data/processed/**/*.gpkg +data/processed/**/*.parquet + +# Earth Engine resumable catalogue checkpoints +data/metadata/landsat/_checkpoints/ + +# Temporary geospatial files +*.aux.xml +*.ovr \ No newline at end of file diff --git a/Makefile b/Makefile index f5501bb..602af04 100644 --- a/Makefile +++ b/Makefile @@ -22,7 +22,30 @@ build-grid: ## Build the study grid from the YAML configuration python -m src.analysis.boundaries.build_study_grid \ --config configs/study_area.yaml +visualize-grid: ## Visualize the study grid and generate all diagnostic plots (overview, comparison, masks, and grid alignment) in reports/day1/ + python -m src.analysis.boundaries.visualize_study_grid \ + --config configs/study_area.yaml + test-grid: ## Run pytest on the study grid module - pytest tests/test_study_grid.py + pytest tests/test_study_grid.py -v + +qa-day1: build-grid visualize-grid test-grid ## Run the complete QA pipeline (build, visualize, test) without updating the reference baseline + +day1: inspect-boundaries build-grid test-grid ## Run the complete Day 1 pipeline (inspection, grid build, tests) + + + +.PHONY: landsat-catalog landsat-visuals test-landsat day2 + +landsat-catalog: + python -m src.analysis.landsat.build_catalog \ + --config configs/landsat_catalog.yaml + +landsat-visuals: + python -m src.analysis.landsat.visualize_catalog \ + --config configs/landsat_catalog.yaml + +test-landsat: + pytest tests/test_landsat_catalog.py -v -day1: inspect-boundaries build-grid test-grid ## Run the complete Day 1 pipeline (inspection, grid build, tests) \ No newline at end of file +day2: landsat-catalog landsat-visuals test-landsat \ No newline at end of file diff --git a/README.md b/README.md index 4154047..9829ceb 100644 --- a/README.md +++ b/README.md @@ -123,13 +123,76 @@ Feature Engineering --- + ## Data Sources +### Administrative Boundaries + +The primary boundary source is the Cameroon Common Operational Dataset for +Administrative Boundaries (`cod-ab-cmr`), provided by OCHA and originally +produced by the Institut National de Cartographie of Cameroon. + +The seven ADM3 units corresponding to Yaoundé I–VII were selected using their +administrative P-codes and dissolved to construct the Yaoundé administrative +core. Their union was validated against the ADM2 Mfoundi boundary. + +All processed geometries use `EPSG:32632`. A 5 km context buffer and a convex +hull are retained separately. The authoritative Landsat grid has a 30 m +resolution, a fixed `(0, 0)` anchor and stable global cell identifiers. + ### Earth Observation -- Landsat annual imagery (1990–2020) +The project uses Landsat Collection 2 Tier 1 Level 2 Surface Reflectance +imagery from: + +- Landsat 5 TM: `LANDSAT/LT05/C02/T1_L2` +- Landsat 7 ETM+: `LANDSAT/LE07/C02/T1_L2` +- Landsat 8 OLI/TIRS: `LANDSAT/LC08/C02/T1_L2` + +The target observation epochs are: + +`1990`, `1995`, `2000`, `2005`, `2010`, `2015` and `2020`. + +For each epoch, a three-year diagnostic period was queried to assess scene +availability around the target year. Scene-level QA masking excludes fill, +cloud, dilated cloud, cloud shadow, snow, cirrus where applicable and +radiometric saturation. Water is retained as a valid observation. + +Generated output: + +- a complete Landsat scene manifest; +- scene-level valid coverage over the administrative core and context area; +- monthly availability summaries; +- candidate compositing-window comparisons; +- an epoch-level quality summary; +- the exact selected Earth Engine scene identifiers; +- a frozen compositing protocol and catalogue checksum. + +The selected scene set is recorded in: + +- `data/metadata/landsat/selected_scene_manifest.csv` +- `data/metadata/landsat/compositing_protocol.yaml` +- `data/metadata/landsat/catalog_version.json` + +Final Landsat composites have not yet been generated. They will be constructed +during ... by loading the exact frozen scene identifiers rather than +re-querying the collections dynamically. + +### Auxiliary and Spatial Predictors + +The following sources are planned for Day 3 and later stages but have not yet +been integrated into the model-ready dataset: + +- SRTM elevation and derived slope; +- OpenStreetMap roads and current infrastructure; +- GHSL built-up surface and population products. + +GHSL will remain an auxiliary comparison and validation source, while current +OpenStreetMap data will be documented carefully because its historical +completeness varies. + -### Spatial Predictors +#### Spatial Predictors Potential predictors include: diff --git a/configs/landsat_catalog.yaml b/configs/landsat_catalog.yaml new file mode 100644 index 0000000..c0b2210 --- /dev/null +++ b/configs/landsat_catalog.yaml @@ -0,0 +1,62 @@ +catalog: + version: 1 + earth_engine_project: urban-sprawl-ssa + epochs: [1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025] + diagnostic_years_before: 1 + diagnostic_years_after: 1 + +study_area: + core_boundary: data/processed/boundaries/yaounde_core.gpkg + context_boundary: data/processed/boundaries/yaounde_context_buffer.gpkg + boundary_report: data/metadata/boundary_report.json + grid_specification: data/metadata/grid_specification.json + +collections: + LT05: + id: LANDSAT/LT05/C02/T1_L2 + required_bands: [SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7] + LE07: + id: LANDSAT/LE07/C02/T1_L2 + required_bands: [SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7] + LC08: + id: LANDSAT/LC08/C02/T1_L2 + required_bands: [SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7] + +epoch_sensor_policy: + 1990: {primary: [LT05], supplemental: [], diagnostic_only: []} + 1995: {primary: [LT05], supplemental: [], diagnostic_only: []} + 2000: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} + 2005: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} + 2010: {primary: [LT05], supplemental: [LE07], diagnostic_only: []} + 2015: {primary: [LC08], supplemental: [LE07], diagnostic_only: []} + 2020: {primary: [LC08], supplemental: [], diagnostic_only: [LE07]} + 2025: {primary: [LC08], supplemental: [], diagnostic_only: [LE07]} + +quality_mask: + mask_fill: true + mask_dilated_cloud: true + mask_cirrus_landsat8: true + mask_cloud: true + mask_cloud_shadow: true + mask_snow: true + mask_radiometric_saturation: true + mask_water: false + +statistics: + max_pixels: 10000000 + tile_scale: 4 + +candidate_windows: + lengths_months: [3, 4, 6, 12] + allow_cross_year_windows: true + +selection: + minimum_coverage_one_observation_pct: 95.0 + preferred_coverage_three_observations_pct: 80.0 + select_shortest_acceptable_window: true + coverage_comparison_tolerance_pct: 0.5 + +outputs: + directory: data/metadata/landsat + report_directory: reports/day2 + reference_catalog: tests/reference/landsat_catalog_v1.json diff --git a/data/metadata/landsat/candidate_window_details.csv b/data/metadata/landsat/candidate_window_details.csv new file mode 100644 index 0000000..e0761a6 --- /dev/null +++ b/data/metadata/landsat/candidate_window_details.csv @@ -0,0 +1,577 @@ +epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_keys,scene_count,coverage_at_least_1_core_pct,coverage_at_least_3_core_pct,supplemental_sensor_used +1990,Jan-Mar_3m,3,1990-01-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Mar_3m,3,1995-01-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Mar_3m,3,2000-01-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Mar_3m,3,2005-01-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",4,20.214509718071028,7.108844196338966,True +2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Mar_3m,3,2010-01-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_only,LC08,3,99.95200756228057,55.84014934025937,False +2015,Jan-Mar_3m,3,2015-01-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",12,99.9983916925276,77.91912842227305,True +2020,Jan-Mar_3m,3,2020-01-01,2020-04-01,primary_only,LC08,12,99.9999889269216,92.4627230944063,False +2025,Jan-Mar_3m,3,2025-01-01,2025-04-01,primary_only,LC08,9,99.97331931309324,82.78798056832599,False +1990,Feb-Apr_3m,3,1990-02-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Apr_3m,3,1995-02-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Apr_3m,3,2000-02-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Apr_3m,3,2005-02-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True +2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Apr_3m,3,2010-02-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_only,LC08,3,66.60550893983303,30.373115138961243,False +2015,Feb-Apr_3m,3,2015-02-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",11,96.61199834332788,52.721554256203554,True +2020,Feb-Apr_3m,3,2020-02-01,2020-05-01,primary_only,LC08,10,99.99197718965614,83.1191679886652,False +2025,Feb-Apr_3m,3,2025-02-01,2025-05-01,primary_only,LC08,6,99.20631169511765,67.12832615980658,False +1990,Mar-May_3m,3,1990-03-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-May_3m,3,1995-03-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-May_3m,3,2000-03-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353795,44.263437174874525,True +2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-May_3m,3,2005-03-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True +2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-May_3m,3,2010-03-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False +2015,Mar-May_3m,3,2015-03-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108565,69.94945631243047,True +2020,Mar-May_3m,3,2020-03-01,2020-06-01,primary_only,LC08,10,97.53274785282866,54.14122230543774,False +2025,Mar-May_3m,3,2025-03-01,2025-06-01,primary_only,LC08,4,95.98010283732056,60.34000438856134,False +1990,Apr-Jun_3m,3,1990-04-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Jun_3m,3,1995-04-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jun_3m,3,2000-04-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Jun_3m,3,2005-04-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",1,8.681343174190491,0.0,True +2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Jun_3m,3,2010-04-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_only,LC08,6,97.54262569620018,55.898040761197564,False +2015,Apr-Jun_3m,3,2015-04-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",12,99.40227487869481,63.41647525357126,True +2020,Apr-Jun_3m,3,2020-04-01,2020-07-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False +2025,Apr-Jun_3m,3,2025-04-01,2025-07-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,May-Jul_3m,3,1990-05-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Jul_3m,3,1995-05-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Jul_3m,3,2000-05-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Jul_3m,3,2005-05-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",1,5.734228027042754,0.0,True +2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Jul_3m,3,2010-05-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False +2015,May-Jul_3m,3,2015-05-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True +2020,May-Jul_3m,3,2020-05-01,2020-08-01,primary_only,LC08,5,52.298947894275784,17.898937105226143,False +2025,May-Jul_3m,3,2025-05-01,2025-08-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,Jun-Aug_3m,3,1990-06-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Aug_3m,3,1995-06-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Aug_3m,3,2000-06-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True +2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Aug_3m,3,2005-06-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Aug_3m,3,2010-06-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jun-Aug_3m,3,2015-06-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",2,70.05992553901368,0.0,True +2020,Jun-Aug_3m,3,2020-06-01,2020-09-01,primary_only,LC08,2,39.44042763503558,0.0,False +2025,Jun-Aug_3m,3,2025-06-01,2025-09-01,primary_only,LC08,1,1.041198517932418,0.0,False +1990,Jul-Sep_3m,3,1990-07-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Sep_3m,3,1995-07-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True +2020,Jul-Sep_3m,3,2020-07-01,2020-10-01,primary_only,LC08,1,31.174148193366612,0.0,False +2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False +1990,Aug-Oct_3m,3,1990-08-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Oct_3m,3,1995-08-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Oct_3m,3,2000-08-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Oct_3m,3,2005-08-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",2,8.789972621051634,0.0,True +2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Oct_3m,3,2010-08-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_only,LC08,0,0.0,0.0,False +2015,Aug-Oct_3m,3,2015-08-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",3,8.655065766282677,2.935682513385204,True +2020,Aug-Oct_3m,3,2020-08-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2025,Aug-Oct_3m,3,2025-08-01,2025-11-01,primary_only,LC08,2,12.073733385514196,0.0,False +1990,Sep-Nov_3m,3,1990-09-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Nov_3m,3,1995-09-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Nov_3m,3,2000-09-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Nov_3m,3,2005-09-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",4,81.04892728307274,32.53886464324526,True +2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Nov_3m,3,2010-09-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False +2015,Sep-Nov_3m,3,2015-09-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True +2020,Sep-Nov_3m,3,2020-09-01,2020-12-01,primary_only,LC08,3,25.049198118928206,8.907243651425132,False +2025,Sep-Nov_3m,3,2025-09-01,2025-12-01,primary_only,LC08,2,14.69865565480517,0.0,False +1990,Oct-Dec_3m,3,1990-10-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Dec_3m,3,1995-10-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Dec_3m,3,2000-10-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Dec_3m,3,2005-10-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230215,48.999682988950326,True +2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Dec_3m,3,2010-10-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",6,77.06529687299054,28.833652379980872,True +2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False +2015,Oct-Dec_3m,3,2015-10-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",16,99.99661400937033,81.62494116586562,True +2020,Oct-Dec_3m,3,2020-10-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False +2025,Oct-Dec_3m,3,2025-10-01,2026-01-01,primary_only,LC08,5,57.48063060201698,20.584371624624833,False +1990,Nov-Jan_3m,3,1989-11-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Jan_3m,3,1994-11-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Jan_3m,3,1999-11-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Jan_3m,3,1999-11-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Jan_3m,3,2004-11-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Jan_3m,3,2009-11-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",4,84.02771858544709,39.686868140752516,True +2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_only,LC08,6,99.99622271947547,80.038023561852,False +2015,Nov-Jan_3m,3,2014-11-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",14,99.99970147171831,83.94154596448331,True +2020,Nov-Jan_3m,3,2019-11-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False +2025,Nov-Jan_3m,3,2024-11-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False +1990,Dec-Feb_3m,3,1989-12-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Feb_3m,3,1994-12-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Feb_3m,3,1999-12-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Feb_3m,3,1999-12-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Feb_3m,3,2004-12-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Feb_3m,3,2009-12-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.686868140752516,True +2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_only,LC08,4,99.98661468043085,74.20328925979955,False +2015,Dec-Feb_3m,3,2014-12-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",13,99.99917716066042,83.94154596448331,True +2020,Dec-Feb_3m,3,2019-12-01,2020-03-01,primary_only,LC08,12,99.9999966096206,92.4627230944063,False +2025,Dec-Feb_3m,3,2024-12-01,2025-03-01,primary_only,LC08,11,99.98651246303923,78.98972857254677,False +1990,Jan-Apr_4m,4,1990-01-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Apr_4m,4,1995-01-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Apr_4m,4,2000-01-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Apr_4m,4,2000-01-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",2,29.462763614586272,0.0,True +2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Apr_4m,4,2005-01-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True +2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Apr_4m,4,2010-01-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_only,LC08,5,99.97910683045646,60.62558215383077,False +2015,Jan-Apr_4m,4,2015-01-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",17,99.99975039988183,77.91912842227305,True +2020,Jan-Apr_4m,4,2020-01-01,2020-05-01,primary_only,LC08,14,99.9999968100652,92.4627230944063,False +2025,Jan-Apr_4m,4,2025-01-01,2025-05-01,primary_only,LC08,9,99.97331931309324,82.78798056832599,False +1990,Feb-May_4m,4,1990-02-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-May_4m,4,1995-02-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-May_4m,4,2000-02-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",6,98.21509972353795,44.263437174874525,True +2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-May_4m,4,2005-02-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",4,18.144777839777348,6.339171717999176,True +2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-May_4m,4,2010-02-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False +2015,Feb-May_4m,4,2015-02-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",18,99.86951234815521,69.94945631243047,True +2020,Feb-May_4m,4,2020-02-01,2020-06-01,primary_only,LC08,14,99.99565066080038,83.1191679886652,False +2025,Feb-May_4m,4,2025-02-01,2025-06-01,primary_only,LC08,8,99.87323530155264,77.57867623864364,False +1990,Mar-Jun_4m,4,1990-03-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Jun_4m,4,1995-03-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Jun_4m,4,2000-03-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Jun_4m,4,2005-03-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",3,17.578914779304476,6.1449316293117,True +2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Jun_4m,4,2010-03-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_only,LC08,7,98.114985812616,55.898040761197564,False +2015,Mar-Jun_4m,4,2015-03-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",16,99.82507317108565,69.94945631243047,True +2020,Mar-Jun_4m,4,2020-03-01,2020-07-01,primary_only,LC08,11,97.8290753978749,54.14122230543774,False +2025,Mar-Jun_4m,4,2025-03-01,2025-07-01,primary_only,LC08,4,95.98010283732056,60.34000438856134,False +1990,Apr-Jul_4m,4,1990-04-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Jul_4m,4,1995-04-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Jul_4m,4,2000-04-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Jul_4m,4,2005-04-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",2,13.917763187815046,0.0,True +2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Jul_4m,4,2010-04-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False +2015,Apr-Jul_4m,4,2015-04-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",14,99.8210406536092,69.6273866274382,True +2020,Apr-Jul_4m,4,2020-04-01,2020-08-01,primary_only,LC08,7,86.25827065126424,39.088867819419974,False +2025,Apr-Jul_4m,4,2025-04-01,2025-08-01,primary_only,LC08,2,84.02840287962343,0.0,False +1990,May-Aug_4m,4,1990-05-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Aug_4m,4,1995-05-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Aug_4m,4,2000-05-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Aug_4m,4,2000-05-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Aug_4m,4,2005-05-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Aug_4m,4,2010-05-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False +2015,May-Aug_4m,4,2015-05-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",9,98.8468689190932,63.13126063996379,True +2020,May-Aug_4m,4,2020-05-01,2020-09-01,primary_only,LC08,6,67.16934456750927,24.28684380484538,False +2025,May-Aug_4m,4,2025-05-01,2025-09-01,primary_only,LC08,3,84.19469891213093,37.900752262065645,False +1990,Jun-Sep_4m,4,1990-06-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Sep_4m,4,1995-06-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Sep_4m,4,2000-06-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Sep_4m,4,2000-06-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True +2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Sep_4m,4,2005-06-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Sep_4m,4,2010-06-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jun-Sep_4m,4,2015-06-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",3,70.06011135415591,28.566446203887992,True +2020,Jun-Sep_4m,4,2020-06-01,2020-10-01,primary_only,LC08,2,39.44042763503558,0.0,False +2025,Jun-Sep_4m,4,2025-06-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False +1990,Jul-Oct_4m,4,1990-07-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Oct_4m,4,1995-07-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Oct_4m,4,2000-07-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Oct_4m,4,2000-07-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Oct_4m,4,2005-07-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True +2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Oct_4m,4,2010-07-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_only,LC08,2,70.05992553901368,0.0,False +2015,Jul-Oct_4m,4,2015-07-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",5,72.65125867408607,30.716739720027615,True +2020,Jul-Oct_4m,4,2020-07-01,2020-11-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2025,Jul-Oct_4m,4,2025-07-01,2025-11-01,primary_only,LC08,2,12.073733385514196,0.0,False +1990,Aug-Nov_4m,4,1990-08-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Nov_4m,4,1995-08-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Nov_4m,4,2000-08-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Nov_4m,4,2000-08-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",0,0.0,0.0,True +2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Nov_4m,4,2005-08-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",5,81.19875872881087,32.53886464324526,True +2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Nov_4m,4,2010-08-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_only,LC08,4,75.17791387840288,34.877459372715684,False +2015,Aug-Nov_4m,4,2015-08-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",9,77.66471331182463,34.877459372715684,True +2020,Aug-Nov_4m,4,2020-08-01,2020-12-01,primary_only,LC08,4,48.41447216945014,18.980026211823695,False +2025,Aug-Nov_4m,4,2025-08-01,2025-12-01,primary_only,LC08,3,15.586811987903781,5.395098365951714,False +1990,Sep-Dec_4m,4,1990-09-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Dec_4m,4,1995-09-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Dec_4m,4,2000-09-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Dec_4m,4,2000-09-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Dec_4m,4,2005-09-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",6,93.32323626230215,48.999682988950326,True +2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Dec_4m,4,2010-09-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True +2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_only,LC08,8,99.9394376994893,64.51202730857302,False +2015,Sep-Dec_4m,4,2015-09-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",17,99.99661403038459,81.62494116586562,True +2020,Sep-Dec_4m,4,2020-09-01,2021-01-01,primary_only,LC08,5,38.29456526884716,12.49571154288523,False +2025,Sep-Dec_4m,4,2025-09-01,2026-01-01,primary_only,LC08,5,57.48063060201698,20.584371624624833,False +1990,Oct-Jan_4m,4,1989-10-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Jan_4m,4,1994-10-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Jan_4m,4,1999-10-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Jan_4m,4,1999-10-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Jan_4m,4,2004-10-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Jan_4m,4,2009-10-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.686868140752516,True +2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Oct-Jan_4m,4,2014-10-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",20,99.99985260336734,83.94154596448331,True +2020,Oct-Jan_4m,4,2019-10-01,2020-02-01,primary_only,LC08,12,99.99892802586828,76.43072751898961,False +2025,Oct-Jan_4m,4,2024-10-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False +1990,Nov-Feb_4m,4,1989-11-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Feb_4m,4,1994-11-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Feb_4m,4,1999-11-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Feb_4m,4,1999-11-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Nov-Feb_4m,4,2004-11-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Feb_4m,4,2004-11-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Feb_4m,4,2009-11-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",5,84.06027781475703,39.686868140752516,True +2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_only,LC08,6,99.99622271947547,80.038023561852,False +2015,Nov-Feb_4m,4,2014-11-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",16,99.9997773111493,83.94154596448331,True +2020,Nov-Feb_4m,4,2019-11-01,2020-03-01,primary_only,LC08,16,99.99999811029484,92.4627230944063,False +2025,Nov-Feb_4m,4,2024-11-01,2025-03-01,primary_only,LC08,14,99.9921366831703,78.98972857254677,False +1990,Dec-Mar_4m,4,1989-12-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Mar_4m,4,1994-12-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Mar_4m,4,1999-12-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Dec-Mar_4m,4,2004-12-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Mar_4m,4,2004-12-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Mar_4m,4,2009-12-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_only,LC08,5,99.98973232638939,74.20328925979955,False +2015,Dec-Mar_4m,4,2014-12-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",17,99.99975919252638,83.94154596448331,True +2020,Dec-Mar_4m,4,2019-12-01,2020-04-01,primary_only,LC08,16,99.99999946438633,92.4627230944063,False +2025,Dec-Mar_4m,4,2024-12-01,2025-04-01,primary_only,LC08,13,99.99660531685394,82.78798056832599,False +1990,Jan-Jun_6m,6,1990-01-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Jun_6m,6,1995-01-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Jun_6m,6,2000-01-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Jan-Jun_6m,6,2005-01-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Jun_6m,6,2005-01-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",5,27.14096193265568,9.773774809576889,True +2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Jun_6m,6,2010-01-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",5,78.62741167942572,33.81517201549004,True +2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_only,LC08,9,99.99882064616772,77.8534842100577,False +2015,Jan-Jun_6m,6,2015-01-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",24,99.99999038674221,87.5917409147415,True +2020,Jan-Jun_6m,6,2020-01-01,2020-07-01,primary_only,LC08,19,99.99999847836753,92.4627230944063,False +2025,Jan-Jun_6m,6,2025-01-01,2025-07-01,primary_only,LC08,11,99.9957386681783,84.64614702896075,False +1990,Feb-Jul_6m,6,1990-02-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Jul_6m,6,1995-02-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Jul_6m,6,2000-02-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Feb-Jul_6m,6,2005-02-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Jul_6m,6,2005-02-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",5,22.83854293048695,8.021730615521188,True +2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jul_6m,6,2010-02-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",3,25.720182367865128,8.629820097085485,True +2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False +2015,Feb-Jul_6m,6,2015-02-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",20,99.96093189987528,69.94945631243047,True +2020,Feb-Jul_6m,6,2020-02-01,2020-08-01,primary_only,LC08,15,99.99617303505754,83.1191679886652,False +2025,Feb-Jul_6m,6,2025-02-01,2025-08-01,primary_only,LC08,8,99.87323530155264,77.57867623864364,False +1990,Mar-Aug_6m,6,1990-03-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Aug_6m,6,1995-03-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Aug_6m,6,2000-03-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",7,98.4336592336039,44.84386617862018,True +2005,Mar-Aug_6m,6,2005-03-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Aug_6m,6,2005-03-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",5,22.9194008930627,8.021730615521188,True +2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Aug_6m,6,2010-03-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",2,25.568454904407577,0.0,True +2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_only,LC08,9,99.43562534869707,63.13126063996379,False +2015,Mar-Aug_6m,6,2015-03-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",18,99.9476267771708,69.94945631243047,True +2020,Mar-Aug_6m,6,2020-03-01,2020-09-01,primary_only,LC08,12,98.50584265050763,54.14122230543774,False +2025,Mar-Aug_6m,6,2025-03-01,2025-09-01,primary_only,LC08,5,96.02195794700079,60.34000438856134,False +1990,Apr-Sep_6m,6,1990-04-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Sep_6m,6,1995-04-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Sep_6m,6,2000-04-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,Apr-Sep_6m,6,2005-04-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Sep_6m,6,2005-04-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",3,14.598348724401722,5.0687312352337885,True +2010,Apr-Sep_6m,6,2010-04-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Sep_6m,6,2010-04-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_only,LC08,8,99.2642603036572,63.13126063996379,False +2015,Apr-Sep_6m,6,2015-04-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",15,99.82104176427299,69.6273866274382,True +2020,Apr-Sep_6m,6,2020-04-01,2020-10-01,primary_only,LC08,8,90.54213772277048,40.58143388086358,False +2025,Apr-Sep_6m,6,2025-04-01,2025-10-01,primary_only,LC08,3,84.19469891213093,37.900752262065645,False +1990,May-Oct_6m,6,1990-05-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Oct_6m,6,1995-05-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Oct_6m,6,2000-05-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",5,97.77941290776172,39.938221606572185,True +2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Oct_6m,6,2005-05-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",3,14.020163574488665,4.862649773895676,True +2010,May-Oct_6m,6,2010-05-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Oct_6m,6,2010-05-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_only,LC08,6,98.30997678543419,63.13126063996379,False +2015,May-Oct_6m,6,2015-05-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",12,98.94667317251714,63.13126063996379,True +2020,May-Oct_6m,6,2020-05-01,2020-11-01,primary_only,LC08,9,75.39316049053653,25.146863455003754,False +2025,May-Oct_6m,6,2025-05-01,2025-11-01,primary_only,LC08,4,85.95677093334616,41.26989078011156,False +1990,Jun-Nov_6m,6,1990-06-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-Nov_6m,6,1995-06-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-Nov_6m,6,2000-06-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",1,12.244914348893799,0.0,True +2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-Nov_6m,6,2005-06-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",6,82.27686477521533,32.53886464324526,True +2010,Jun-Nov_6m,6,2010-06-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-Nov_6m,6,2010-06-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",3,32.38347038819753,11.478427298038612,True +2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_only,LC08,6,92.56824893242367,45.63231976158158,False +2015,Jun-Nov_6m,6,2015-06-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",11,93.31279853448552,45.63231976158158,True +2020,Jun-Nov_6m,6,2020-06-01,2020-12-01,primary_only,LC08,5,54.61011489671122,20.251522816828125,False +2025,Jun-Nov_6m,6,2025-06-01,2025-12-01,primary_only,LC08,3,15.586811987903781,5.395098365951714,False +1990,Jul-Dec_6m,6,1990-07-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Dec_6m,6,1995-07-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Dec_6m,6,2000-07-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",4,45.73846394959479,16.681804073898867,True +2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Dec_6m,6,2005-07-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",8,93.75585813242849,48.999682988950326,True +2010,Jul-Dec_6m,6,2010-07-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Dec_6m,6,2010-07-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",7,83.26558969405305,35.200973666542204,True +2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_only,LC08,10,99.98186760213181,69.57196223718582,False +2015,Jul-Dec_6m,6,2015-07-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",19,99.99898623817593,81.62494116586562,True +2020,Jul-Dec_6m,6,2020-07-01,2021-01-01,primary_only,LC08,6,57.53070893529786,20.155114847508894,False +2025,Jul-Dec_6m,6,2025-07-01,2026-01-01,primary_only,LC08,6,57.923341646023005,20.584371624624833,False +1990,Aug-Jan_6m,6,1989-08-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Jan_6m,6,1994-08-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jan_6m,6,1999-08-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Jan_6m,6,2004-08-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",2,48.639530851514344,0.0,True +2010,Aug-Jan_6m,6,2009-08-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Jan_6m,6,2009-08-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",5,84.5586854337693,39.686868140752516,True +2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Aug-Jan_6m,6,2014-08-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",22,99.99985404685276,83.94154596448331,True +2020,Aug-Jan_6m,6,2019-08-01,2020-02-01,primary_only,LC08,13,99.99910847793507,76.43072751898961,False +2025,Aug-Jan_6m,6,2024-08-01,2025-02-01,primary_only,LC08,10,99.7506425258757,64.08446786213737,False +1990,Sep-Feb_6m,6,1989-09-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Feb_6m,6,1994-09-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Feb_6m,6,1999-09-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Feb_6m,6,2004-09-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",3,48.992146837846775,17.991589743979652,True +2010,Sep-Feb_6m,6,2009-09-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Feb_6m,6,2009-09-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",6,84.59016229601333,39.686868140752516,True +2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_only,LC08,10,99.99764620749872,80.038023561852,False +2015,Sep-Feb_6m,6,2014-09-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",22,99.99989004865289,83.94154596448331,True +2020,Sep-Feb_6m,6,2019-09-01,2020-03-01,primary_only,LC08,17,99.99999842840064,92.4627230944063,False +2025,Sep-Feb_6m,6,2024-09-01,2025-03-01,primary_only,LC08,14,99.9921366831703,78.98972857254677,False +1990,Oct-Mar_6m,6,1989-10-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Mar_6m,6,1994-10-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Mar_6m,6,1999-10-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Mar_6m,6,2004-10-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2010,Oct-Mar_6m,6,2009-10-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Mar_6m,6,2009-10-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_only,LC08,11,99.9981944418267,80.038023561852,False +2015,Oct-Mar_6m,6,2014-10-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",26,99.99996782226512,83.94154596448331,True +2020,Oct-Mar_6m,6,2019-10-01,2020-04-01,primary_only,LC08,20,99.99999970146352,92.4627230944063,False +2025,Oct-Mar_6m,6,2024-10-01,2025-04-01,primary_only,LC08,16,99.99802087889053,82.78798056832599,False +1990,Nov-Apr_6m,6,1989-11-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Apr_6m,6,1994-11-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Apr_6m,6,1999-11-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Apr_6m,6,2004-11-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Apr_6m,6,2009-11-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_only,LC08,9,99.99873859639293,80.038023561852,False +2015,Nov-Apr_6m,6,2014-11-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",25,99.99998988585686,83.94154596448331,True +2020,Nov-Apr_6m,6,2019-11-01,2020-05-01,primary_only,LC08,22,99.99999991399756,92.4627230944063,False +2025,Nov-Apr_6m,6,2024-11-01,2025-05-01,primary_only,LC08,16,99.99802087889053,82.78798056832599,False +1990,Dec-May_6m,6,1989-12-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-May_6m,6,1994-12-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-May_6m,6,1999-12-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552893,65.10708852743944,True +2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-May_6m,6,2004-12-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Dec-May_6m,6,2009-12-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-May_6m,6,2009-12-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",7,88.13581849357872,45.67893066665478,True +2015,Dec-May_6m,6,2014-12-01,2015-06-01,primary_only,LC08,11,99.99974768482708,89.20002667996266,False +2015,Dec-May_6m,6,2014-12-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",29,99.99999856063324,89.20002667996266,True +2020,Dec-May_6m,6,2019-12-01,2020-06-01,primary_only,LC08,22,99.99999991635079,92.4627230944063,False +2025,Dec-May_6m,6,2024-12-01,2025-06-01,primary_only,LC08,15,99.9994578148844,84.64614702896075,False +1990,Jan-Dec_12m,12,1990-01-01,1991-01-01,primary_only,LT05,0,0.0,0.0,False +1995,Jan-Dec_12m,12,1995-01-01,1996-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_only,LT05,0,0.0,0.0,False +2000,Jan-Dec_12m,12,2000-01-01,2001-01-01,primary_plus_supplemental,"LT05,LE07",11,99.1500794403698,52.0061067316496,True +2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_only,LT05,0,0.0,0.0,False +2005,Jan-Dec_12m,12,2005-01-01,2006-01-01,primary_plus_supplemental,"LT05,LE07",13,95.45057829972708,48.999682988950326,True +2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_only,LT05,0,0.0,0.0,False +2010,Jan-Dec_12m,12,2010-01-01,2011-01-01,primary_plus_supplemental,"LT05,LE07",12,96.4234233774342,55.456614464889256,True +2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_only,LC08,19,99.99999978615487,96.12321885528449,False +2015,Jan-Dec_12m,12,2015-01-01,2016-01-01,primary_plus_supplemental,"LC08,LE07",43,99.99999999990254,96.12321885528449,True +2020,Jan-Dec_12m,12,2020-01-01,2021-01-01,primary_only,LC08,25,99.99999935377349,92.4627230944063,False +2025,Jan-Dec_12m,12,2025-01-01,2026-01-01,primary_only,LC08,17,99.99820697396805,84.64614702896075,False +1990,Feb-Jan_12m,12,1989-02-01,1990-02-01,primary_only,LT05,0,0.0,0.0,False +1995,Feb-Jan_12m,12,1994-02-01,1995-02-01,primary_only,LT05,0,0.0,0.0,False +2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_only,LT05,1,92.88684247459292,0.0,False +2000,Feb-Jan_12m,12,1999-02-01,2000-02-01,primary_plus_supplemental,"LT05,LE07",3,98.21578307322392,57.6336846624187,True +2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_only,LT05,0,0.0,0.0,False +2005,Feb-Jan_12m,12,2004-02-01,2005-02-01,primary_plus_supplemental,"LT05,LE07",6,90.67826640340226,44.45034468183726,True +2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_only,LT05,0,0.0,0.0,False +2010,Feb-Jan_12m,12,2009-02-01,2010-02-01,primary_plus_supplemental,"LT05,LE07",12,98.36361505477768,61.833501818017,True +2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_only,LC08,20,99.99998288037484,91.05817819518664,False +2015,Feb-Jan_12m,12,2014-02-01,2015-02-01,primary_plus_supplemental,"LC08,LE07",43,99.99999993373655,91.05817819518664,True +2020,Feb-Jan_12m,12,2019-02-01,2020-02-01,primary_only,LC08,18,99.9996394421078,76.43072751898961,False +2025,Feb-Jan_12m,12,2024-02-01,2025-02-01,primary_only,LC08,18,99.99998192456317,82.13699542031398,False +1990,Mar-Feb_12m,12,1989-03-01,1990-03-01,primary_only,LT05,0,0.0,0.0,False +1995,Mar-Feb_12m,12,1994-03-01,1995-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_only,LT05,0,0.0,0.0,False +2000,Mar-Feb_12m,12,1999-03-01,2000-03-01,primary_plus_supplemental,"LT05,LE07",2,74.91666787354076,0.0,True +2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_only,LT05,0,0.0,0.0,False +2005,Mar-Feb_12m,12,2004-03-01,2005-03-01,primary_plus_supplemental,"LT05,LE07",5,57.44590265932441,22.97408918804021,True +2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_only,LT05,0,0.0,0.0,False +2010,Mar-Feb_12m,12,2009-03-01,2010-03-01,primary_plus_supplemental,"LT05,LE07",11,93.21865856027992,50.16453781543488,True +2015,Mar-Feb_12m,12,2014-03-01,2015-03-01,primary_only,LC08,20,99.99998288037484,91.05817819518664,False +2015,Mar-Feb_12m,12,2014-03-01,2015-03-01,primary_plus_supplemental,"LC08,LE07",43,99.99999992303607,91.05817819518664,True +2020,Mar-Feb_12m,12,2019-03-01,2020-03-01,primary_only,LC08,20,99.99999875162852,92.4627230944063,False +2025,Mar-Feb_12m,12,2024-03-01,2025-03-01,primary_only,LC08,20,99.99916078896021,78.98972857254677,False +1990,Apr-Mar_12m,12,1989-04-01,1990-04-01,primary_only,LT05,0,0.0,0.0,False +1995,Apr-Mar_12m,12,1994-04-01,1995-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_only,LT05,0,0.0,0.0,False +2000,Apr-Mar_12m,12,1999-04-01,2000-04-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_only,LT05,0,0.0,0.0,False +2005,Apr-Mar_12m,12,2004-04-01,2005-04-01,primary_plus_supplemental,"LT05,LE07",5,53.96206253425382,20.979279514264185,True +2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_only,LT05,0,0.0,0.0,False +2010,Apr-Mar_12m,12,2009-04-01,2010-04-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True +2015,Apr-Mar_12m,12,2014-04-01,2015-04-01,primary_only,LC08,18,99.9999747937986,91.05817819518664,False +2015,Apr-Mar_12m,12,2014-04-01,2015-04-01,primary_plus_supplemental,"LC08,LE07",42,99.99999993787675,91.05817819518664,True +2020,Apr-Mar_12m,12,2019-04-01,2020-04-01,primary_only,LC08,23,99.99999978896516,92.4627230944063,False +2025,Apr-Mar_12m,12,2024-04-01,2025-04-01,primary_only,LC08,20,99.99941834568435,82.78798056832599,False +1990,May-Apr_12m,12,1989-05-01,1990-05-01,primary_only,LT05,0,0.0,0.0,False +1995,May-Apr_12m,12,1994-05-01,1995-05-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_only,LT05,0,0.0,0.0,False +2000,May-Apr_12m,12,1999-05-01,2000-05-01,primary_plus_supplemental,"LT05,LE07",4,82.30691072462102,35.07825752215467,True +2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_only,LT05,0,0.0,0.0,False +2005,May-Apr_12m,12,2004-05-01,2005-05-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_only,LT05,0,0.0,0.0,False +2010,May-Apr_12m,12,2009-05-01,2010-05-01,primary_plus_supplemental,"LT05,LE07",11,91.73130241956882,45.67893066665478,True +2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_only,LC08,20,99.99998902665786,91.05817819518664,False +2015,May-Apr_12m,12,2014-05-01,2015-05-01,primary_plus_supplemental,"LC08,LE07",46,99.99999999014055,91.05817819518664,True +2020,May-Apr_12m,12,2019-05-01,2020-05-01,primary_only,LC08,25,99.99999993920503,92.4627230944063,False +2025,May-Apr_12m,12,2024-05-01,2025-05-01,primary_only,LC08,18,99.99876965319511,82.78798056832599,False +1990,Jun-May_12m,12,1989-06-01,1990-06-01,primary_only,LT05,0,0.0,0.0,False +1995,Jun-May_12m,12,1994-06-01,1995-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_only,LT05,0,0.0,0.0,False +2000,Jun-May_12m,12,1999-06-01,2000-06-01,primary_plus_supplemental,"LT05,LE07",8,99.55228753552893,65.10708852743944,True +2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_only,LT05,0,0.0,0.0,False +2005,Jun-May_12m,12,2004-06-01,2005-06-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_only,LT05,0,0.0,0.0,False +2010,Jun-May_12m,12,2009-06-01,2010-06-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Jun-May_12m,12,2014-06-01,2015-06-01,primary_only,LC08,20,99.99997220174258,89.20002667996266,False +2015,Jun-May_12m,12,2014-06-01,2015-06-01,primary_plus_supplemental,"LC08,LE07",45,99.99999989988848,89.20002667996266,True +2020,Jun-May_12m,12,2019-06-01,2020-06-01,primary_only,LC08,27,99.99999996122469,92.4627230944063,False +2025,Jun-May_12m,12,2024-06-01,2025-06-01,primary_only,LC08,19,99.99975550478729,84.64614702896075,False +1990,Jul-Jun_12m,12,1989-07-01,1990-07-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Jun_12m,12,1994-07-01,1995-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Jun_12m,12,1999-07-01,2000-07-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Jun_12m,12,2004-07-01,2005-07-01,primary_plus_supplemental,"LT05,LE07",6,57.95877387597446,20.979279514264185,True +2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_only,LT05,0,0.0,0.0,False +2010,Jul-Jun_12m,12,2009-07-01,2010-07-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Jul-Jun_12m,12,2014-07-01,2015-07-01,primary_only,LC08,18,99.99997209501109,89.20002667996266,False +2015,Jul-Jun_12m,12,2014-07-01,2015-07-01,primary_plus_supplemental,"LC08,LE07",41,99.99999988022066,89.20002667996266,True +2020,Jul-Jun_12m,12,2019-07-01,2020-07-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False +2025,Jul-Jun_12m,12,2024-07-01,2025-07-01,primary_only,LC08,18,99.99968390274987,84.64614702896075,False +1990,Aug-Jul_12m,12,1989-08-01,1990-08-01,primary_only,LT05,0,0.0,0.0,False +1995,Aug-Jul_12m,12,1994-08-01,1995-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_only,LT05,0,0.0,0.0,False +2000,Aug-Jul_12m,12,1999-08-01,2000-08-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_only,LT05,0,0.0,0.0,False +2005,Aug-Jul_12m,12,2004-08-01,2005-08-01,primary_plus_supplemental,"LT05,LE07",7,60.36951364729075,20.979279514264185,True +2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_only,LT05,0,0.0,0.0,False +2010,Aug-Jul_12m,12,2009-08-01,2010-08-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False +2015,Aug-Jul_12m,12,2014-08-01,2015-08-01,primary_plus_supplemental,"LC08,LE07",42,99.99999994297887,89.20002667996266,True +2020,Aug-Jul_12m,12,2019-08-01,2020-08-01,primary_only,LC08,28,99.99999996588176,92.4627230944063,False +2025,Aug-Jul_12m,12,2024-08-01,2025-08-01,primary_only,LC08,18,99.99968390274987,84.64614702896075,False +1990,Sep-Aug_12m,12,1989-09-01,1990-09-01,primary_only,LT05,0,0.0,0.0,False +1995,Sep-Aug_12m,12,1994-09-01,1995-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_only,LT05,0,0.0,0.0,False +2000,Sep-Aug_12m,12,1999-09-01,2000-09-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_only,LT05,0,0.0,0.0,False +2005,Sep-Aug_12m,12,2004-09-01,2005-09-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True +2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_only,LT05,0,0.0,0.0,False +2010,Sep-Aug_12m,12,2009-09-01,2010-09-01,primary_plus_supplemental,"LT05,LE07",8,88.53021970019955,45.67893066665478,True +2015,Sep-Aug_12m,12,2014-09-01,2015-09-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False +2015,Sep-Aug_12m,12,2014-09-01,2015-09-01,primary_plus_supplemental,"LC08,LE07",40,99.99999994241495,89.20002667996266,True +2020,Sep-Aug_12m,12,2019-09-01,2020-09-01,primary_only,LC08,29,99.99999997651783,92.4627230944063,False +2025,Sep-Aug_12m,12,2024-09-01,2025-09-01,primary_only,LC08,19,99.99968719394975,84.64614702896075,False +1990,Oct-Sep_12m,12,1989-10-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Oct-Sep_12m,12,1994-10-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Oct-Sep_12m,12,1999-10-01,2000-10-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Oct-Sep_12m,12,2004-10-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",8,60.68284119102544,20.979279514264185,True +2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_only,LT05,0,0.0,0.0,False +2010,Oct-Sep_12m,12,2009-10-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",9,91.63102270855705,46.28341533178432,True +2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_only,LC08,19,99.99998671579178,89.20002667996266,False +2015,Oct-Sep_12m,12,2014-10-01,2015-10-01,primary_plus_supplemental,"LC08,LE07",41,99.9999999424153,89.20002667996266,True +2020,Oct-Sep_12m,12,2019-10-01,2020-10-01,primary_only,LC08,28,99.99999997176484,92.4627230944063,False +2025,Oct-Sep_12m,12,2024-10-01,2025-10-01,primary_only,LC08,19,99.99968719394975,84.64614702896075,False +1990,Nov-Oct_12m,12,1989-11-01,1990-11-01,primary_only,LT05,0,0.0,0.0,False +1995,Nov-Oct_12m,12,1994-11-01,1995-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_only,LT05,0,0.0,0.0,False +2000,Nov-Oct_12m,12,1999-11-01,2000-11-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_only,LT05,0,0.0,0.0,False +2005,Nov-Oct_12m,12,2004-11-01,2005-11-01,primary_plus_supplemental,"LT05,LE07",9,63.8530225472835,20.979279514264185,True +2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_only,LT05,0,0.0,0.0,False +2010,Nov-Oct_12m,12,2009-11-01,2010-11-01,primary_plus_supplemental,"LT05,LE07",8,91.34324607677755,46.28341533178432,True +2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_only,LC08,15,99.9999786819862,89.20002667996266,False +2015,Nov-Oct_12m,12,2014-11-01,2015-11-01,primary_plus_supplemental,"LC08,LE07",37,99.99999989346502,89.20002667996266,True +2020,Nov-Oct_12m,12,2019-11-01,2020-11-01,primary_only,LC08,31,99.99999997883752,92.4627230944063,False +2025,Nov-Oct_12m,12,2024-11-01,2025-11-01,primary_only,LC08,20,99.99972206748909,84.64614702896075,False +1990,Dec-Nov_12m,12,1989-12-01,1990-12-01,primary_only,LT05,0,0.0,0.0,False +1995,Dec-Nov_12m,12,1994-12-01,1995-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_only,LT05,0,0.0,0.0,False +2000,Dec-Nov_12m,12,1999-12-01,2000-12-01,primary_plus_supplemental,"LT05,LE07",9,99.60710954333274,65.10708852743944,True +2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_only,LT05,0,0.0,0.0,False +2005,Dec-Nov_12m,12,2004-12-01,2005-12-01,primary_plus_supplemental,"LT05,LE07",12,92.54897664388146,42.90104908880774,True +2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_only,LT05,0,0.0,0.0,False +2010,Dec-Nov_12m,12,2009-12-01,2010-12-01,primary_plus_supplemental,"LT05,LE07",10,91.97785219851266,46.28341533178432,True +2015,Dec-Nov_12m,12,2014-12-01,2015-12-01,primary_only,LC08,17,99.99998124856444,89.20002667996266,False +2015,Dec-Nov_12m,12,2014-12-01,2015-12-01,primary_plus_supplemental,"LC08,LE07",40,99.99999990374664,89.20002667996266,True +2020,Dec-Nov_12m,12,2019-12-01,2020-12-01,primary_only,LC08,27,99.99999996203172,92.4627230944063,False +2025,Dec-Nov_12m,12,2024-12-01,2025-12-01,primary_only,LC08,18,99.999542324259,84.64614702896075,False diff --git a/data/metadata/landsat/candidate_window_summary.csv b/data/metadata/landsat/candidate_window_summary.csv new file mode 100644 index 0000000..63ddb99 --- /dev/null +++ b/data/metadata/landsat/candidate_window_summary.csv @@ -0,0 +1,49 @@ +window_name,number_of_months,minimum_epoch_coverage_1_pct,minimum_epoch_coverage_3_pct,median_epoch_coverage_3_pct,total_scene_count +Apr-Jul_4m,4,0.0,0.0,0.0,17 +Apr-Jun_3m,3,0.0,0.0,0.0,15 +Apr-Mar_12m,12,0.0,0.0,0.0,61 +Apr-Sep_6m,6,0.0,0.0,0.0,19 +Aug-Jan_6m,6,0.0,0.0,0.0,33 +Aug-Jul_12m,12,0.0,0.0,0.0,65 +Aug-Nov_4m,4,0.0,0.0,0.0,11 +Aug-Oct_3m,3,0.0,0.0,0.0,6 +Dec-Feb_3m,3,0.0,0.0,0.0,27 +Dec-Mar_4m,4,0.0,0.0,0.0,34 +Dec-May_6m,6,0.0,0.0,0.0,48 +Dec-Nov_12m,12,0.0,0.0,0.0,62 +Feb-Apr_3m,3,0.0,0.0,0.0,19 +Feb-Jan_12m,12,0.0,0.0,0.0,57 +Feb-Jul_6m,6,0.0,0.0,0.0,32 +Feb-May_4m,4,0.0,0.0,0.0,29 +Jan-Apr_4m,4,0.0,0.0,0.0,28 +Jan-Dec_12m,12,0.0,0.0,0.0,61 +Jan-Jun_6m,6,0.0,0.0,0.0,39 +Jan-Mar_3m,3,0.0,0.0,0.0,24 +Jul-Dec_6m,6,0.0,0.0,0.0,22 +Jul-Jun_12m,12,0.0,0.0,0.0,64 +Jul-Oct_4m,4,0.0,0.0,0.0,8 +Jul-Sep_3m,3,0.0,0.0,0.0,4 +Jun-Aug_3m,3,0.0,0.0,0.0,5 +Jun-May_12m,12,0.0,0.0,0.0,66 +Jun-Nov_6m,6,0.0,0.0,0.0,14 +Jun-Sep_4m,4,0.0,0.0,0.0,5 +Mar-Aug_6m,6,0.0,0.0,0.0,26 +Mar-Feb_12m,12,0.0,0.0,0.0,60 +Mar-Jun_4m,4,0.0,0.0,0.0,22 +Mar-May_3m,3,0.0,0.0,0.0,21 +May-Apr_12m,12,0.0,0.0,0.0,63 +May-Aug_4m,4,0.0,0.0,0.0,15 +May-Jul_3m,3,0.0,0.0,0.0,13 +May-Oct_6m,6,0.0,0.0,0.0,19 +Nov-Apr_6m,6,0.0,0.0,0.0,47 +Nov-Feb_4m,4,0.0,0.0,0.0,36 +Nov-Jan_3m,3,0.0,0.0,0.0,28 +Nov-Oct_12m,12,0.0,0.0,0.0,66 +Oct-Dec_3m,3,0.0,0.0,0.0,18 +Oct-Jan_4m,4,0.0,0.0,0.0,32 +Oct-Mar_6m,6,0.0,0.0,0.0,47 +Oct-Sep_12m,12,0.0,0.0,0.0,66 +Sep-Aug_12m,12,0.0,0.0,0.0,67 +Sep-Dec_4m,4,0.0,0.0,0.0,18 +Sep-Feb_6m,6,0.0,0.0,0.0,41 +Sep-Nov_3m,3,0.0,0.0,0.0,9 diff --git a/data/metadata/landsat/catalog_version.json b/data/metadata/landsat/catalog_version.json new file mode 100644 index 0000000..56347d1 --- /dev/null +++ b/data/metadata/landsat/catalog_version.json @@ -0,0 +1,7 @@ +{ + "catalog_version": 1, + "config_sha256": "197a0d9faf8fa3819efb068bd4a3dbe815fa29cf036c5c1d22404baaed51dd0f", + "selected_scene_manifest_sha256": "2325905e823c14352a4f29cea3652d62c1d15ad81a293bda73d826acf2dce321", + "compositing_protocol_sha256": "a31dee916b03f507d961be8586623ea01e7c8db8f9395a95638ee7eac973059c", + "selected_scene_count": 7 +} \ No newline at end of file diff --git a/data/metadata/landsat/compositing_protocol.yaml b/data/metadata/landsat/compositing_protocol.yaml new file mode 100644 index 0000000..6546240 --- /dev/null +++ b/data/metadata/landsat/compositing_protocol.yaml @@ -0,0 +1,98 @@ +catalog_version: 1 +common_window_name: Jul-Sep_3m +quality_mask: + mask_fill: true + mask_dilated_cloud: true + mask_cirrus_landsat8: true + mask_cloud: true + mask_cloud_shadow: true + mask_snow: true + mask_radiometric_saturation: true + mask_water: false +selection_thresholds: + minimum_coverage_one_observation_pct: 95.0 + preferred_coverage_three_observations_pct: 80.0 + select_shortest_acceptable_window: true + coverage_comparison_tolerance_pct: 0.5 +epochs: + 1990: + window_name: Jul-Sep_3m + window_start: '1990-07-01' + window_end: '1990-10-01' + sensor_mode: primary_only + sensor_keys: + - LT05 + scene_count: 0 + earth_engine_asset_ids: [] + 1995: + window_name: Jul-Sep_3m + window_start: '1995-07-01' + window_end: '1995-10-01' + sensor_mode: primary_only + sensor_keys: + - LT05 + scene_count: 0 + earth_engine_asset_ids: [] + 2000: + window_name: Jul-Sep_3m + window_start: '2000-07-01' + window_end: '2000-10-01' + sensor_mode: primary_only + sensor_keys: + - LT05 + scene_count: 0 + earth_engine_asset_ids: [] + 2005: + window_name: Jul-Sep_3m + window_start: '2005-07-01' + window_end: '2005-10-01' + sensor_mode: primary_plus_supplemental + sensor_keys: + - LT05 + - LE07 + scene_count: 2 + earth_engine_asset_ids: + - LANDSAT/LE07/C02/T1_L2/LE07_185057_20050719 + - LANDSAT/LE07/C02/T1_L2/LE07_185058_20050820 + 2010: + window_name: Jul-Sep_3m + window_start: '2010-07-01' + window_end: '2010-10-01' + sensor_mode: primary_plus_supplemental + sensor_keys: + - LT05 + - LE07 + scene_count: 1 + earth_engine_asset_ids: + - LANDSAT/LE07/C02/T1_L2/LE07_185057_20100919 + 2015: + window_name: Jul-Sep_3m + window_start: '2015-07-01' + window_end: '2015-10-01' + sensor_mode: primary_only + sensor_keys: + - LC08 + scene_count: 2 + earth_engine_asset_ids: + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20150707 + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20150707 + 2020: + window_name: Jul-Sep_3m + window_start: '2020-07-01' + window_end: '2020-10-01' + sensor_mode: primary_only + sensor_keys: + - LC08 + scene_count: 1 + earth_engine_asset_ids: + - LANDSAT/LC08/C02/T1_L2/LC08_185057_20200805 + 2025: + window_name: Jul-Sep_3m + window_start: '2025-07-01' + window_end: '2025-10-01' + sensor_mode: primary_only + sensor_keys: + - LC08 + scene_count: 1 + earth_engine_asset_ids: + - LANDSAT/LC08/C02/T1_L2/LC08_185058_20250819 diff --git a/data/metadata/landsat/epoch_quality_summary.csv b/data/metadata/landsat/epoch_quality_summary.csv new file mode 100644 index 0000000..85c2577 --- /dev/null +++ b/data/metadata/landsat/epoch_quality_summary.csv @@ -0,0 +1,9 @@ +epoch,window_name,number_of_months,window_start,window_end,sensor_mode,sensor_keys,scene_count,coverage_at_least_1_core_pct,coverage_at_least_3_core_pct,supplemental_sensor_used +1990,Jul-Sep_3m,3,1990-07-01,1990-10-01,primary_only,LT05,0,0.0,0.0,False +1995,Jul-Sep_3m,3,1995-07-01,1995-10-01,primary_only,LT05,0,0.0,0.0,False +2000,Jul-Sep_3m,3,2000-07-01,2000-10-01,primary_only,LT05,0,0.0,0.0,False +2005,Jul-Sep_3m,3,2005-07-01,2005-10-01,primary_plus_supplemental,"LT05,LE07",2,6.479514434271549,0.0,True +2010,Jul-Sep_3m,3,2010-07-01,2010-10-01,primary_plus_supplemental,"LT05,LE07",1,27.034545800423416,0.0,True +2015,Jul-Sep_3m,3,2015-07-01,2015-10-01,primary_only,LC08,2,70.05992553901368,0.0,False +2020,Jul-Sep_3m,3,2020-07-01,2020-10-01,primary_only,LC08,1,31.174148193366612,0.0,False +2025,Jul-Sep_3m,3,2025-07-01,2025-10-01,primary_only,LC08,1,1.041198517932418,0.0,False diff --git a/data/metadata/landsat/monthly_availability.csv b/data/metadata/landsat/monthly_availability.csv new file mode 100644 index 0000000..9e3ac9c --- /dev/null +++ b/data/metadata/landsat/monthly_availability.csv @@ -0,0 +1,87 @@ +epoch,acquisition_month,sensor_key,scene_count,positive_core_scene_count,median_valid_fraction_core,maximum_valid_fraction_core,median_valid_fraction_context,median_cloud_cover_scene_pct +2000,2,LE07,3,2,0.015526905539321192,0.056200007669942736,0.010236236773862048,72.0 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