Simulating burn severity maps at 30 meters in two forested regions in California

Bibliographic Data

ID15545463
AuthorsJonathan A Sam (0000-0002-7208-6224, University of California, Merced, corresponding author), Wayne J Baldwin (0000-0001-5470-6306, University of California, Merced), A L Westerling (0000-0003-4573-0595, University of California, Merced), Haiganoush K Preisler (Pacific Southwest Research Station), Qingqing Xu (0009-0003-4788-3824, University of California, Merced), Matthew D Hurteau (0000-0001-8457-8974, University of New Mexico), Benjamin Sleeter (0000-0003-2371-9571, United States Geological Survey), Samrajya Bikram Thapa (0000-0002-1674-6689, University of California, Merced)
Year2022
Volume17
Issue10
Pages105004-105004
Publication date2022-09-21
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac939b
OpenAlexW4296699965
LanguageEN
References cited43

Climate change is altering wildfire and vegetation regimes in California’s forested ecosystems. Present day fires are seeing an increase in high burn severity area and high severity patch size. The ability to predict future burn severity patterns could better support policy and land management decisions. Here we demonstrate a methodology to first, statistically estimate individual burn severity classes at 30 meters and second, cluster and smooth high severity patches onto a known landscape. Our goal here was not to exactly replicate observed burn severity maps, but rather to utilize observed maps as one realization of a random process dependent on climate, topography, fire weather, and fuels, to inform creation of additional realizations through our simulation technique. We developed two sets of empirical models with two different vegetation datasets to test if coarse vegetation could accurately model for burn severity. While visual acuity can be used to assess the performance of our simulation process, we also employ the Ripley’s K function to compare spatial point processes at different scales to test if the simulation is capturing an appropriate amount of clustering. We utilize FRAGSTATS to obtain high severity patch metrics to test the contiguity of our high severity simulation. Ripley’s K function helped identify the number of clustering iterations and FRAGSTATS showed how different focal window sizes affected our ability to cluster high severity patches. Improving our ability to simulate burn severity may help advance our understanding of the potential influence of land and fuels management on ecosystem-level response variables that are important for decision-makers. Simulated burn severity maps could support managing habitat and estimating risks of habitat loss, protecting infrastructure and homes, improving future wildfire emissions projections, and better mapping and planning for fuels treatment scenarios

Climatology · Cluster analysis · Contiguity · Environmental resource management · Geography · Machine learning · Meteorology · Physical geography · Replicate · Statistics · Vegetation (pathology · Computer Science · Ecology and Vegetation Dynamics Studies · Environmental Science · Fire effects on ecosystems · Mathematics · Remote Sensing in Agriculture · Geology

Tools

Open DOIOpen Access