Document Type
Article
Journal/Book Title/Conference
Fire Ecology
Author ORCID Identifier
Kipling B. Klimas https://orcid.org/0000-0003-4547-3963
Larissa L. Yocom https://orcid.org/0000-0003-2459-0765
Brendan P. Murphy https://orcid.org/0000-0001-8025-1253
Scott R. David https://orcid.org/0000-0003-2708-4251
James A. Lutz https://orcid.org/0000-0002-2560-0710
R. Justin DeRose https://orcid.org/0000-0002-4849-7744
Sara A. Wall https://orcid.org/0000-0002-3673-1126
Volume
21
Issue
8
Publisher
SpringerOpen
Publication Date
2-10-2025
Journal Article Version
Version of Record
First Page
1
Last Page
23
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
Background
High-severity burned areas can have lasting impacts on vegetation regeneration, carbon dynamics, hydrology, and erosion. While landscape models can predict erosion from burned areas using the differenced normalized burn ratio (dNBR), post-fire erosion modeling has predominantly focused on areas that have recently burned. Here, we developed and validated a predictive burn severity model that produces continuous dNBR predictions for recently unburned forest land in Utah.
Results
Vegetation productivity, elevation, and canopy fuels were the most important predictor variables in the model, highlighting the strong control of fuels and vegetation on burn severity in Utah. Final model out-of-bag R2 was 67.1%, residuals showed a correlation coefficient of 0.89 and classification accuracy into three classes was 85%. We demonstrated that dNBR can be empirically modeled relative to fuels and topography and found burn severity was highest in productive vegetation and at relatively cooler sites.
Conclusions
We found that prediction accuracy was higher when fuel moisture was lower, suggesting drier weather conditions drive more consistent and predictable burn severity patterns across a range of burn severity, vegetation types, and geographic locations. Moreover, burn severity predictions from this model can be used to inform hydro-erosion models and subsequent management actions aimed at reducing burn severity and post-wildfire erosion risks.
Recommended Citation
Klimas, K.B., Yocom, L.L., Murphy, B.P. et al. A machine learning model to predict wildfire burn severity for pre-fire risk assessments, Utah, USA. fire ecol 21, 8 (2025). https://doi.org/10.1186/s42408-024-00346-z
Comments
The final published version is available here: https://doi.org/10.1186/s42408-024-00346-z, and the publisher is Springer Science+Business Media. This article is licensed under a CC-BY license.