Overview#

The library#

All functionality in xlandsat is available from the base namespace of the xlandsat package. This means that you can access all of them with a single import:

# xlandsat is usually imported as xls
import xlandsat as xls

Download a sample scene#

As an example, lets download a tar archive of a Landsat 8 scene of the Brumadinho tailings dam disaster that happened in 2019 in Brazil. The archive is available on figshare at https://doi.org/10.6084/m9.figshare.21665630.v1 and includes scenes from before and after the disaster as both the full scene and a cropped version.

We’ll use pooch to download the scenes from before and after the disaster to our computer. To save space and bandwidth, we’ll use the cropped version here.

import pooch

path_before = pooch.retrieve(
      "doi:10.6084/m9.figshare.21665630.v1/cropped-before.tar.gz",
     known_hash="md5:d2a503c944bb7ef3b41294d44b77e98c",
)
path_after = pooch.retrieve(
      "doi:10.6084/m9.figshare.21665630.v1/cropped-after.tar.gz",
     known_hash="md5:4ae61a2d7a8b853c727c0c433680cece",
)
print(path_before)
print(path_after)

/home/runner/.cache/pooch/cc3e48c8ee8ca9c9ccb64e2a0e798286-cropped-before.tar.gz
/home/runner/.cache/pooch/7450bb0a799deea586280f7c3b539898-cropped-after.tar.gz

Tip

Running the code above will only download the data once since Pooch is smart enough to check if the file already exists on your computer. See pooch.retrieve for more information.

Plot some reflectance bands#

Now we can use the xarray.DataArray.plot method to make plots of individual bands with matplotlib. A bonus is that xarray uses the metadata that xlandsat.load_scene inserts into the scene to automatically add labels and annotations to the plot.

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))

# Make the pseudocolor plots of the near infrared band
before.nir.plot(ax=ax1)
after.nir.plot(ax=ax2)

# Set the title using metadata from each scene
ax1.set_title(f"Before: {before.attrs['title']}")
ax2.set_title(f"After: {after.attrs['title']}")

# Set the aspect to equal so that pixels are squares, not rectangles
ax1.set_aspect("equal")
ax2.set_aspect("equal")

plt.show()

Plot the composites#

Composites can be plotted using xarray.DataArray.plot.imshow (using plot won’t work and will display histograms instead). Let’s make the before and after figures again for each of the composites we generated.

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))

# Plot the composites
before.rgb.plot.imshow(ax=ax1)
after.rgb.plot.imshow(ax=ax2)

# The "long_name" of the composite is the band combination
ax1.set_title(f"Before: {before.rgb.attrs['long_name']}")
ax2.set_title(f"After: {after.rgb.attrs['long_name']}")

ax1.set_aspect("equal")
ax2.set_aspect("equal")

plt.show()

And now the CIR composites:

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))

before.cir.plot.imshow(ax=ax1)
after.cir.plot.imshow(ax=ax2)

ax1.set_title(f"Before: {before.cir.attrs['long_name']}")
ax2.set_title(f"After: {after.cir.attrs['long_name']}")

ax1.set_aspect("equal")
ax2.set_aspect("equal")

plt.show()

What now?#

By getting the data into an xarray.Dataset, xlandsat opens the door for a huge range of operations. You now have access to everything that xarray can do: interpolation, reduction, slicing, grouping, saving to cloud-optimized formats, and much more. So go off and do something cool!

xlandsat v0.2.0

Overview

Contents

Overview#

The library#

Download a sample scene#

Load the scenes#

Plot some reflectance bands#

Make composites#

Plot the composites#

Calculating indices#

What now?#