Modelling and mapping burn severity of prescribed and wildfires across the southeastern United States (2000-2022)

International Journal of Wildland Fire
By: , and 

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Abstract

Background

The southeastern United States (‘Southeast’) experiences high levels of fire activity, but the preponderance of small and prescribed fires means that existing burn severity products are incomplete across the region.

Aims

We developed and applied a burn severity model across the Southeast to enhance our understanding of regional burn severity patterns.

Methods

We used Composite Burn Index (CBI) plot data from across the conterminous US (CONUS) to train a gradient-boosted decision tree model. The model was optimised for the Southeast and applied to the annual Landsat Burned Area product for 2000–2022 across the region.

Key results

The burn severity model had a root mean square error (RMSE) of 0.48 (R2 = 0.70) and 0.50 (R2 = 0.37) for the CONUS and Southeast, respectively. The Southeast, relative to CONUS, had lower mean absolute residuals in low and moderate burn severity categories. Burn severity was consistently lower in areas affected by prescribed burns relative to wildfires.

Conclusions

Although regional performance was limited by a lack of high burn severity CBI plots, the burn severity dataset demonstrated patterns consistent with low-severity, frequent fire regimes characteristic of Southeastern ecosystems.

Implications

More complete data on burn severity will enhance regional management of fire-dependent ecosystems and improve estimates of fuels and fire emissions.

Study Area

Publication type Article
Publication Subtype Journal Article
Title Modelling and mapping burn severity of prescribed and wildfires across the southeastern United States (2000-2022)
Series title International Journal of Wildland Fire
DOI 10.1071/WF24137
Volume 34
Year Published 2025
Language English
Publisher CSIRO Publishing
Contributing office(s) Geosciences and Environmental Change Science Center
Description WF24137, 18 p.
Country United States
Other Geospatial Southeastern United States
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