Miguel L. Villarreal
Leonhard Blesius
Jerry D. Davis
Skye C. Corbett
Joshua W. Von Nonn
2024
<div id="abstracts" class="Abstracts u-font-serif text-s"><div id="abs0010" class="abstract author" lang="en"><div id="abssec0010"><p id="abspara0010"><span>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 </span><i>in-situ</i><span> 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</span><sup>2</sup> = 0.79 and R<sup>2</sup> = 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.</p></div></div></div>
application/pdf
10.1016/j.envsoft.2023.105903
en
Elsevier
An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management
article