Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity DOI
Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill

et al.

Ecosphere, Journal Year: 2024, Volume and Issue: 15(12)

Published: Dec. 1, 2024

Abstract Interruption of frequent burning in dry forests across western North America and the continued impacts anthropogenic climate change have resulted increases fire size severity compared to historical regimes. Recent legislation, funding, planning emphasized increased implementation mechanical thinning prescribed treatments decrease risk undesirable ecological social outcomes due fire. As wildfires continue interact, managers require consistent approaches evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing–based, analytical framework for conducting fire‐scale assessments that informs local management while also supporting cross‐fire comparisons. We demonstrate on 2021 Bootleg Fire Oregon Schneider Springs Washington. Our used (1) machine learning identify key bioclimatic, topographic, weather drivers each fire, (2) standardized workflows statistically sample untreated control units, (3) spatial regression modeling effects type time since The application our showed that, both fires, recent were most effective reducing relative controls. contrast, thinning‐only only produced low/moderate‐severity under more moderate conditions Fire. offers robust approach evaluating scale individual which can be scaled up assess multiple fires. brings uncertainty forest ecosystems America, support strategic actions reduce wildfire foster resilience.

Language: Английский

Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity DOI
Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill

et al.

Ecosphere, Journal Year: 2024, Volume and Issue: 15(12)

Published: Dec. 1, 2024

Abstract Interruption of frequent burning in dry forests across western North America and the continued impacts anthropogenic climate change have resulted increases fire size severity compared to historical regimes. Recent legislation, funding, planning emphasized increased implementation mechanical thinning prescribed treatments decrease risk undesirable ecological social outcomes due fire. As wildfires continue interact, managers require consistent approaches evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing–based, analytical framework for conducting fire‐scale assessments that informs local management while also supporting cross‐fire comparisons. We demonstrate on 2021 Bootleg Fire Oregon Schneider Springs Washington. Our used (1) machine learning identify key bioclimatic, topographic, weather drivers each fire, (2) standardized workflows statistically sample untreated control units, (3) spatial regression modeling effects type time since The application our showed that, both fires, recent were most effective reducing relative controls. contrast, thinning‐only only produced low/moderate‐severity under more moderate conditions Fire. offers robust approach evaluating scale individual which can be scaled up assess multiple fires. brings uncertainty forest ecosystems America, support strategic actions reduce wildfire foster resilience.

Language: Английский

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