THIS CONTENT IS MOVING!

We are moving our course lessons to an improved textbook series. All of the same content will be improved and available by the end of Spring 2020. While these pages will automagically redirect, you can also visit the links below to check out our new content! Our course landing pages with associated readings and assignments will stay here so you can continue to follow along with our courses!

Quantify the Impacts of a Fire Using MODIS and Landsat Remote Sensing Data in Python


Welcome to Week 8!

Welcome to week 8 of Earth Analytics! This week you will work with MODIS and Landsat data to calculate burn indices.

Download Cold Springs Fire Data (~150 MB)

Or use earthpy et.data.get_data('cold-springs-fire')

2. Complete the Assignment Using the Template for this week.

(10 points) Please note that like the flood report, this assignment is worth more points than a usual weekly assignment. You have 1 week to complete this assignment. Start early!

Homework Plot 1 - Grid of 3 - 3 band CIR plots post fire

CIR Composite images from NAIP, Landsat, and MODIS for the post-Cold Springs fire.
CIR Composite images from NAIP, Landsat, and MODIS for the post-Cold Springs fire.
NBR images calculated from Landsat for pre- and post-Cold Springs fire.
NBR images calculated from Landsat for pre- and post-Cold Springs fire.
NBR images calculated from MODIS for pre- and post-Cold Springs fire.
NBR images calculated from MODIS for pre- and post-Cold Springs fire.
Histogram for the dNBR image calculated from MODIS for the Cold Springs fire.
Histogram for the dNBR image calculated from MODIS for the Cold Springs fire.

Homework Plot 2 & 3 : Landsat & MODIS Difference Normalized Burn Ration dNBR

Difference NBR (dNBR) images calculated from Landsat and MODIS for the Cold Springs fire.
Difference NBR (dNBR) images calculated from Landsat and MODIS for the Cold Springs fire.
Burned Landsat class 1:
Burned Landsat class 2:
Burned MODIS class 1:
Burned MODIS class 2: