A machine learning model to predict wildfire burn severity for pre-fire risk assessments, Utah, USA

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...

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Veröffentlicht in:	Fire ecology Jg. 21; H. 1; S. 8
Hauptverfasser:	Klimas, Kipling B., Yocom, Larissa L., Murphy, Brendan P., David, Scott R., Belmont, Patrick, Lutz, James A., DeRose, R. Justin, Wall, Sara A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V
Schlagworte:	Accuracy Biomedical and Life Sciences Combustion Correlation coefficient Correlation coefficients Ecology Forestry Fuels Geographical locations Hydrology Life Sciences Machine learning Original Research Predictions Risk assessment Vegetation Weather Wildfires United States > US Utah Fire modeling Fire effects Intermountain West Remote sensing Burn severity Machine learning
ISSN:	1933-9747
Online-Zugang:	Volltext
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