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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:
- Chapter 11 on Calculating Normalized Burn Ratio (NBR) in Section 5 of the Intermediate Earth Data Science Textbook and
- 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.
- Denver Post article on the Cold Springs fire.
- Fire science for rainforests - Cochrane 2003.
- A review of ways to use remote sensing to assess fire and post-fire effects - Lentile et al 2006.
- Comparison of dNBR vs RdNBR accuracy / introduction to fire indices - Soverel et al 2010.
Homework Figure 1 - Grid of 3 Color InfraRed (CIR) Post-Fire Plots: NAIP, Landsat and MODIS
Homework Figure 2 - Difference NDVI (dNDVI) Using Landsat & MODIS
Homework Figure 3 - Difference NBR (dNBR) Using Landsat & MODIS
Landsat vs MODIS Burned Area
Burned Landsat class 4:
Burned Landsat class 5:
Burned MODIS class 4:
Burned MODIS class 5:
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