Change "Resampling the raster image" heading level to h2

episodes/06-raster-intro.md

@@ -49,7 +49,7 @@
 For your convenience, we included the scene of interest among the datasets that you have already downloaded when following [the setup instructions](../learners/setup.md) (the raster data files should be in the `data/sentinel2` directory). You should, however, be able to download the same datasets "on-the-fly" using the JSON metadata file that was created in [the previous episode](05-access-data.md) (the file `rhodes_sentinel-2.json`).
 
 
 If you choose to work with the provided data (which is advised in case you are working offline or have a slow/unstable network connection) you can skip the remaining part of the block and continue with the following section: [Load a Raster and View Attributes](#Load-a-Raster-and-View-Attributes).
 
 If you want instead to experiment with downloading the data on-the-fly, you need to load the file `rhodes_sentinel-2.json`, which contains information on where and how to access the target images from the remote repository:
 
@@ -113,7 +113,7 @@
 
 The output tells us that we are looking at an `xarray.DataArray`, with `1` band, `10980` rows, and `10980` columns. We can also see the number of pixel values in the `DataArray`, and the type of those pixel values, which is unsigned integer (or `uint16`). The `DataArray` also stores different values for the coordinates of the `DataArray`. When using `rioxarray`, the term coordinates refers to spatial coordinates like `x` and `y` but also the `band` coordinate. Each of these sequences of values has its own data type, like `float64` for the spatial coordinates and `int64` for the `band` coordinate.
 
 This `DataArray` object also has a couple of attributes that are accessed like `.rio.crs`, `.rio.nodata`, and `.rio.bounds()` (in jupyter you can browse through these attributes by using `tab` for auto completion or have a look at the documentation [here](https://corteva.github.io/rioxarray/stable/rioxarray.html#rioxarray-rio-accessors)), which contains the metadata for the file we opened. Note that many of the metadata are accessed as attributes without `()`, however since `bounds()` is a method (i.e. a function in an object) it requires these parentheses this is also the case for `.rio.resolution()`.
 
 ```python
 print(rhodes_red.rio.crs)
@@ -163,7 +163,7 @@
 
 Notice that `rioxarray` helpfully allows us to plot this raster with spatial coordinates on the x and y axis (this is not the default in many cases with other functions or libraries). Nice plot! However, it probably took a while for it to load therefore it would make sense to resample it.
 
-# Resampling the raster image
+## Resampling the raster image
 
 The red band image is available as a raster file with 10 m resolution, which makes it a relatively large file (few hundreds MBs).
 In order to keep calculations "manageable" (reasonable execution time and memory usage) we select here a lower resolution version of the image, taking
@@ -213,7 +213,7 @@
 
 ![Raster plot using vmin 100 and vmax 2000](fig/E06/rhodes_red_80_B04_vmin100_vmax2000.png){alt="raster plot with robust setting"}
 
 More options can be consulted [here](https://docs.xarray.dev/en/v2024.02.0/generated/xarray.plot.imshow.html). You will notice that these parameters are part of the `imshow` method from the plot function. Since plot originates from matplotlib and is so widely used, your python environment helps you to interpret the parameters without having to specify the method. It is a service to help you, but can be confusing when teaching it. We will explain more about this below.
 
 :::