Intermediate earth data science textbook
intermediate-earth-data-science-textbook Home

Lesson 6. Test Your Skills: Open Raster Data Using RioXarray In Open Source Python

Leah Wasser, Nathan Korinek

Learning Objectives

  • Practice your skills manipulating raster data using rioxarray.
# Import necessary packages
import os
import matplotlib.pyplot as plt
import seaborn as sns
import geopandas as gpd
import rioxarray as rxr 
# Plotting extent is used to plot raster & vector data together
from rasterio.plot import plotting_extent

import earthpy as et
import earthpy.plot as ep

# Prettier plotting with seaborn
sns.set(font_scale=1.5, style="white")

# Get data and set working directory
et.data.get_data("colorado-flood")
os.chdir(os.path.join(et.io.HOME, 'earth-analytics', 'data'))

Challenge 1: Open And Plot Hillshade

It’s time to practice your raster skills. Do the following:

Use the pre_DTM_hill.tif layer in the colorado-flood/spatial/boulder-leehill-rd/pre-flood/lidar directory.

  1. Open the pre_DTM_hill.tif layer using rioxarray.
  2. Plot the data using ep.plot_bands().
  3. Set the colormap (cmap=) parameter value to Greys: cmap="gray"

Give you plot a title.

Plot of a Lidar Digital Terrain Model overlayed on top of a hillshade. Your challenge 1 plot should look something like this one.
Plot of a Lidar Digital Terrain Model overlayed on top of a hillshade. Your challenge 1 plot should look something like this one.

Challenge 2: Overlay DTM Over DTM Hillshade

In the challenge above, you opened and plotted a hillshade of the lidar digital terrain model create from NEON lidar data before the 2013 Colorado Flood. In this challenge, you will use the hillshade to create a map that looks more 3-dimensional by overlaying the DTM data on top of the hillshade.

To do this, you will need to plot each layer using ep.plot_bands()

  1. Plot the hillshade layer pre_DTM_hill.tif that you opened in Challenge 1. Similar to Challenge one set cmap="gray"
  2. Plot the DTM that you opened above dtm_pre_arr
    • When you plot the DTM, use the alpha= parameter to adjust the opacity of the DTM so that you can see the shading on the hillshade underneath the DTM.
    • Set the colormap to viridis (or any colormap that you prefer) cmap='viridis' for the DTM layer.

HINT: be sure to use the ax= parameter to make sure both layers are on the same figure.

Data Tip:

Plot of a Lidar Digital Terrain Model colored using the viridis colormap in this example, overlayed on top of a hillshade. Your challenge 2 plot should look something like this one.
Plot of a Lidar Digital Terrain Model colored using the viridis colormap in this example, overlayed on top of a hillshade. Your challenge 2 plot should look something like this one.

Challenge 3: Add A Site Boundary to Your Raster Plot

Take all of the code that you wrote above to plot the DTM on top of your hillshade layer. Add the site boundary layer that you opened above site_bound_shp to your plot.

HINT: remember that the plotting_extent() object (lidar_dem_plot_ext) will be needed to add this final layer to your plot.

Data Tip: Plotting Raster and Vector Together

You can learn more about overlaying vector data on top of raster data to create maps in Python in this lesson on setting plotting extents.

Plot of a Lidar Digital Terrain Model overlayed on top of a hillshade. In this plot the site boundary is also overlayed. Your challenge 3 plot should look something like this one.
Plot of a Lidar Digital Terrain Model overlayed on top of a hillshade. In this plot the site boundary is also overlayed. Your challenge 3 plot should look something like this one.

Challenge 4 (Optional): Open Post Flood Raster

Above, you opened up a lidar derived Digital Terrain Model (DTM or DEM) that was created from data collected before the 2013 flood. In the post-flood directory, you will find a DTM containing data collected after the 2013 flood.

Create a figure with two plots.

In the first subplot, plot the pre-flood data that you opened above. In the second subplot, open and plot the post-flood DTM data. You wil find the file post_DTM.tif in the post-flood directory of your colorado-flood dataset downloaded using earthpy.

  • Add a super title (a title for the entire figure) using plt.suptitle("Title Here")
  • Adjust the location of your suptitle plt.tight_layout(rect=[0, 0.03, 1, 0.9])
Plots of Lidar Digital Terrain Models pre and post flood. Your challenge 5 plot should look something like this one.
Plots of Lidar Digital Terrain Models pre and post flood. Your challenge 5 plot should look something like this one.
Open Raster Data Python Geotiff File Format

You May Also Enjoy

Plot Data With Matplotlib

1 minute read

This Week

Calculate Seasonal Summary Values from Climate Data Variables Stored in NetCDF 4 Format: Work With MACA v2 Climate Data in Python

26 minute read

Learn how to calculate seasonal summary values for MACA 2 climate data using xarray and region mask in open source Python.

Calculate Summary Values Using Spatial Areas of Interest (AOIs) including Shapefiles for Climate Data Variables Stored in NetCDF 4 Format: Work With MACA v2 Climate Data in Python

32 minute read

Climate datasets stored in netcdf 4 format often cover the entire globe or an entire country. Learn how to subset climate data spatially and by time slices u...

How to Open and Process NetCDF 4 Data Format in Open Source Python

26 minute read

Historic and projected climate data are most often stored in netcdf 4 format. Learn how to open and process MACA version 2 climate data for the Continental U...