After completing this lesson, you will be able to:
- Practice your skills using Landsat data in Python.
import os from glob import glob import matplotlib.pyplot as plt import geopandas as gpd import rioxarray as rxr import xarray as xr from shapely.geometry import mapping import numpy as np import numpy.ma as ma import earthpy as et import earthpy.spatial as es import earthpy.plot as ep # Download data and set working directory data = et.data.get_data('cold-springs-fire') os.chdir(os.path.join(et.io.HOME, 'earth-analytics', 'data'))
Challenge 1: Open And Crop Your Data
Above, you opened up the landsat scene in the directory:
LC080340322016072301T1-SC20180214145802. This data covers an area which a file occured near Nederland, Colorado. For this challenge, you will work with data that was collected before the fire for the same area. Do the following:
- Crop all of the bands (tif files with the word “band” in them, in the
LC080340322016070701T1-SC20180214145604directory using xarray
- Make sure your fire boundary data is in the correct crs before clipping the array!
- Plot the data using
Challenge 2 (Optional): Plot CIR and RGB Images Using Landsat
In this lesson which introduces working with Landsat data in open source Python, you learn how to plot both a color RGB and Color Infrared (CIR) images using landsat data. Create a figure below that has:
- A color RGB image of the landsat data collected post fire
- A CIR image of the landsat data collected post fire.
HINT: You will need to set the correct band combinations for your plots to turn our properly. You will also have to mask out the nan values for your plot to work.
- For Regular color images use:
rgb=[3, 2, 1]
- For color infrared use:
rgb=[4, 3, 2]