An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management

Joshua W. von Nonn; Miguel L. Villarreal; Leonhard Blesius; Jerry D. Davis; Skye C. Corbett

doi:10.1016/j.envsoft.2023.105903

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An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management

Joshua W. von Nonn·Miguel L. Villarreal·Leonhard Blesius·Jerry D. Davis·Skye C. Corbett

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Abstract

Wildfires are increasing in size and severity across much of the western United States, exposing vulnerable wildland-urban interfaces to post-fire hazards. The Mediterranean chaparral region of Northern California contains many high sloping watersheds prone to hazardous post-fire flood events and identifying watersheds at high risk of soil loss and debris flows is a priority for post-fire response and management. Uncrewed Aerial Systems (UAS; aka drones) offer post-fire management teams the ability to quickly mobilize and survey burned areas with very high-resolution imagery (∼1 cm), facilitating emergency management and post-fire hazard assessment. However, adoption of this technology by hazard response teams may be hindered by complicated workflows for UAS data acquisition, image processing and analysis. We present an open-source workflow using mature Geographic Information Systems (GIS) software and Python packages in a Jupyter Notebook environment that guides users through classification of true-color UAS imagery to generate high resolution burn severity maps which can then be scaled across larger watersheds using Sentinel-2 normalized burn ratio (NBR) images. Soil burn severity classifications using a weighted brightness (WB) image and Char Index (CI) generated from UAS imagery were validated with in-situ data and random stratified points, resulting in the CI having the highest overall accuracy of 87.5%. CI also displayed a marginally stronger relationship over the WB with the post-fire Sentinel-2 NBR, R 2 = 0.79 and R 2 = 0.78 respectively. Our methods offer the unique opportunity to standardize GIS workflows, promoting replication through transparency, while improving the user's understanding of scientific GIS functionality.

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Environmental Modelling & Software Article

Source-reported bounding extent: 38.12528904182068° to 38.47017109042332° latitude; -122.56453869295964° to -122.02620861483481° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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BibTeX RIS

Joshua W. von Nonn, Miguel L. Villarreal, Leonhard Blesius, Jerry D. Davis, Skye C. Corbett. 2024. An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management. https://doi.org/10.1016/j.envsoft.2023.105903

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