This repository contains code for evaluating the effect of vegetation mortality on burn severity during the Woolsey Fire. It was developed as part of the CU Boulder Earth Data Analytics Certificate Program.
The Woolsey Fire burned nearly 100,000 acres near Malibu, CA in November 2018. A four-year drought preceded the fire, resulting in widespread dieback of grass, shrubs, and trees in and around the area that burned. This project seeks to understand how the dieback affected the severity of the fire. Did areas where more vegetation die burn more severely during the fire? Can satellite-based estimates of dieback be used to inform planning for future wildfires?
In this repository, I've used the random-forest machine-learning algorithm to try to evaluate these questions.
The following software is required to install this package using the instructions below:
Once you have Git and Miniconda installed, open the command line and run the following commands to set up the environment needed to run this package:
git clone https://github.com/adamancer/ea-drought-burn
cd ea-drought-burn
conda env create --file environment.yml
conda activate ea-drought-burn
Then install the ea-drought-burn scripts using:
pip install -e .
The data required for this project is not currently available for download. Some data was prepared by other researchers, and I do not have permission to share it. To run the notebooks in this repository, please contact me to obtain a copy of the data as a zip file, then extract it to ~/earth-analytics/data/woolsey-fire.
The package includes the following directories:
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ea_drought_burn contains a set of utility functions used by the notebooks to read, process, and plot raster data.
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notebooks contains a set of Jupyter Notebooks used to explore and model climate, vegetation, and burn severity data related to the Woolsey Fire.
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reports contains documents summarizing the results of the project.
Notebooks include:
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0-run-all-notebooks.ipynb runs all notebooks in the notebooks directory
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1-load-data.ipynb loads and provides reference info for the data used in this project. The full list of references is below.
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2-data-exploration.ipynb includes plots and descriptions of the data used in the burn-severity model.
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3-random-forest.ipynb runs a random-forest model that predicts burn severity for the Woolsey Fire based on pre-fire conditions. Variables, sampling strategies, and the area of interest are all adjustable. By default, individual runs are saved to ~/earth-analytics/data/woolsey-fire/outputs/models.
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4-view-model-results.ipynb allow you to view and compare results of previous models.
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5-project-report.ipynb generates an HTML blog post summarizing some results of this project for a general audience.
Once you have a copy of the data in the right place, you can run the notebooks using the Jupyter Notebook interface:
conda activate ea-drought-burn
cd path/to/ea-drought-burn
jupyter notebook
Open and run 0-run-all-notebooks.ipynb in the Jupyter Notebook interface to run all notebooks at once and recreate the HTML report in the reports directory.
The utility functions defined in ea_drought_burn can be accessed directly. For
example, the plot_bands function simplifies plotting an xarray.DataArray
using the earthpy library:
import rioxarray as rxr
from ea_drought_burn.utils import plot_bands
xda = rxr.open_rasterio("path/to/raster.tif", masked=True)
plot_bands(xda)Please see the Zenodo record for this repository for a version-specific citation.
Data and publications used in this repository include:
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CA State Boundary. Available from: https://data.ca.gov/dataset/ca-geographic-boundaries/resource/3db1e426-fb51-44f5-82d5-a54d7c6e188b.
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Dagit R, Contreras S, Daukiss R, Spyrka A, Quelly N, Foster K, Nickmeyer A, Rousseau B, Chang E. How can we save our native trees? Drought and Invasive Beetle impacts on Wildland Trees and Shrublands in the Santa Monica Mountains. Final Report for Los Angeles County Contract CP-03-44. 2017. Available from: https://www.rcdsmm.org/wp-content/uploads/2016/04/Drought-and-Invasive-Beetle-impacts-RCDSMM-1.2.18.pdf.
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Eidenshink J, Schwind B, Brewer K et al. A Project for Monitoring Trends in Burn Severity. Fire Ecol. 2007;3:3-21. doi:10.4996/fireecology.0301003.
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Foster K, Queally N, Nickmeyer A, Rousseau N. Appendix: Santa Monica Mountains Ecological Forecasting II: Utilizing NASA Earth Observations to Determine Drought Dieback and Insect-related Damage in the Santa Monica Mountains, California. 2017A. Avalable from: https://www.rcdsmm.org/wp-content/uploads/2016/04/Drought-and-Tree-Appendices_12.15.17.pdf.
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Foster K, Queally N, Nickmeyer A, Rousseau N. Utilizing NASA Earth Observations to Determine Drought Dieback and Insect-related Damage in the Santa Monica Mountains, California. 2017B. Available from: https://develop.larc.nasa.gov/2017/fall/posters/2017Fall_JPL_SantaMonicaMountainsEcoII_Poster.pdf
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Gao B. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ. 1996;58(3):257-266. doi:10.1016/S0034-4257(96)00067-3.
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Hicke JA, Johnson MC, Hayes JL, Presiler HK. Effects of bark beetle-caused tree mortality on wildfire. For Ecol Manag. 2013;271:81-90. doi:10.1016/j.foreco.2012.02.005.
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McCune B, Keon D. Equations for potential annual direct incident radiation and heat load. Jour Veg Sci. 2002;13(4):603-606. doi:10.1111/j.1654-1103.2002.tb02087.x.
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National Interagency Fire Center. Historic Perimeters Combined 2000-2018. 2019. Available from: https://data-nifc.opendata.arcgis.com/datasets/historic-perimeters-combined-2000-2018.
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National Interagency Fire Center. Interagency Fire Perimeter History - All Years. 2021. Available from: https://data-nifc.opendata.arcgis.com/datasets/4454e5d8e8c44b0280258b51bcf24794_0.
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Rao K, Williams AP, Flefil JF, Konings AG. SAR-enhanced mapping of live fuel moisture content. Remote Sens Environ. 2020;245:111797. doi:10.1016/j.rse.2020.111797.
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PRISM Climate Group, Oregon State University https://prism.oregonstate.edu, created October 2017.
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PRISM Climate Group, Oregon State University https://prism.oregonstate.edu, created June 2021.