# Lesson 6. Practice Opening and Plotting Landsat Data in Python Using Rasterio

## Learning Objectives

After completing this lesson, you will be able to:

• Practice your skills using Landsat data in Python.
import os
from glob import glob  # File manipulation
import matplotlib.pyplot as plt
import geopandas as gpd
import rasterio as rio
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep

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:

1. Crop all of the bands (tif files with the word “band” in them, in the LC080340322016070701T1-SC20180214145604 directory using earthpy crop_all().
2. Next stack the cropped tif files using es.stack().
3. Finally plot the data using ep.plot_bands()

## 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:

1. A color RGB image of the landsat data collected post fire
2. 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.

• For Regular color images use: rgb=[3, 2, 1]
• For color infrared use: rgb=[4, 3, 2]