Using Open-Source Python Scripting to Update Fire Perimeter Datasets for the USGS

Abstract

As occurrence and intensity of wildfires in the United States increases, the need for a centralized fire perimeter dataset is crucial for ecosystem, fire and other disciplinary analyses. In late 2021, the Combined wildland fire datasets for the United States and certain territories, 1800s-Present was created by the USGS to fill the need for a single multi agency and year dataset. While this dataset improves the ease of obtaining wildfire data; errors and assumptions create inaccuracies in the dataset that hinders the usability of the data. In response, open-source python libraries are used to iterate over the data and correctly identify fire perimeters based on decision tree methodology, calculated by fire ecologists. This script successfully identifies smaller fires that would otherwise be grouped with larger focal fires. The final python script successfully identifies separate fire perimeters in the same calendar year, increasing the datasets accuracy and allowing smaller fires to be further analyzed.