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


Welcome to Week 9!

Welcome to week 9 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')

Materials to Review For This Week’s Assignment

Please be sure to review:

  1. Chapter 11 on Calculating Normalized Burn Ratio (NBR) in Section 5 of the Intermediate Earth Data Science Textbook and
  2. Data Story on Cold Springs Fire in Section 7 of the Intermediate Earth Data Science Textbook

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

Note that this assignment is worth more points than a usual weekly assignment.

Click here to view the the example GitHub Repo with the assignment template.

2. Suggested Fire Readings

Please read the articles below to prepare for next week’s class.

Homework Figure 1 - Grid of 3 Color InfraRed (CIR) Post-Fire Plots: NAIP, Landsat and MODIS

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.

Homework Figure 2 - Difference NDVI (dNDVI) Using Landsat & MODIS

Homework Figure 3 - Difference NBR (dNBR) Using Landsat & MODIS

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.
Classified difference NBR (dNBR) images calculated from Landsat and MODIS for the Cold Springs fire.
Classified difference NBR (dNBR) images calculated from Landsat and MODIS for the Cold Springs fire.

Landsat vs MODIS Burned Area

Burned Landsat class 4:
Burned Landsat class 5:
Burned MODIS class 4:
Burned MODIS class 5: