Collaborators: Minho Kim, Adrienne Dodd, Xihan Yao, John Radke, Matt Kondolf
With the exacerbation of climate change effects, communities in the Wildland Urban Interface (WUI) are at risk of being devastated by higher severity and more frequent wildfires over time. However, WUI landscapes are heterogeneous and mixed with highly complex features. In response, there is an urgent need to understand the wildfire-causing factors towards the development of effective and sustainable wildfire mitigation. However, there is a disconnect between our understanding of landscape patterns and wildfire burns, especially at a high spatial resolution needed to resolve the highly heterogeneous and complex WUI landscapes. We aim to develop a data-driven, machine learning approach to assess wildfire burn severity with high resolution remote sensing data and generate an understanding of burn severity with different landscape-related variables.
- numpy
- scikit-learn
- matplotlib
- pandas
- rasterio
- seaborn
- jupyter
- Install a new conda environment
$ conda env create --name burnseverityML --file environment.yml
- Activate the new environment and navigate to the "src" folder
$ conda activate burnseverityML
- Start notebook
$ jupyter notebook
Please cite the journal paper if this code is useful and helpful for your research.