Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity
Abstract Interruption of frequent burning in dry forests across western North America and the continued impacts of anthropogenic climate change have resulted in increases in fire size and severity compared to historical fire regimes. Recent legislation, funding, and planning have emphasized increase...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Ecosphere |
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| Online Access: | https://doi.org/10.1002/ecs2.70073 |
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| author | Caden P. Chamberlain Garrett W. Meigs Derek J. Churchill Jonathan T. Kane Astrid Sanna James S. Begley Susan J. Prichard Maureen C. Kennedy Craig Bienz Ryan D. Haugo Annie C. Smith Van R. Kane C. Alina Cansler |
| author_facet | Caden P. Chamberlain Garrett W. Meigs Derek J. Churchill Jonathan T. Kane Astrid Sanna James S. Begley Susan J. Prichard Maureen C. Kennedy Craig Bienz Ryan D. Haugo Annie C. Smith Van R. Kane C. Alina Cansler |
| author_sort | Caden P. Chamberlain |
| collection | DOAJ |
| description | Abstract Interruption of frequent burning in dry forests across western North America and the continued impacts of anthropogenic climate change have resulted in increases in fire size and severity compared to historical fire regimes. Recent legislation, funding, and planning have emphasized increased implementation of mechanical thinning and prescribed burning treatments to decrease the risk of undesirable ecological and social outcomes due to fire. As wildfires and treatments continue to interact, managers require consistent approaches to evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing–based, analytical framework for conducting fire‐scale assessments of treatment effectiveness that informs local management while also supporting cross‐fire comparisons. We demonstrate this framework on the 2021 Bootleg Fire in Oregon and the 2021 Schneider Springs Fire in Washington. Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. The application of our framework showed that, in both fires, recent prescribed burning treatments were the most effective at reducing burn severity relative to untreated controls. In contrast, thinning‐only treatments only produced low/moderate‐severity effects under the more moderate fire weather conditions in the Schneider Springs Fire. Our framework offers a robust approach for evaluating treatment effects on burn severity at the scale of individual fires, which can be scaled up to assess treatment effectiveness across multiple fires. As climate change brings increased uncertainty to dry forest ecosystems of western North America, our framework can support more strategic management actions to reduce wildfire risk and foster resilience. |
| format | Article |
| id | doaj-art-c1e61c4013a44943b071782ef4f8f397 |
| institution | Kabale University |
| issn | 2150-8925 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj-art-c1e61c4013a44943b071782ef4f8f3972025-01-27T14:51:34ZengWileyEcosphere2150-89252024-12-011512n/an/a10.1002/ecs2.70073Learning from wildfires: A scalable framework to evaluate treatment effects on burn severityCaden P. Chamberlain0Garrett W. Meigs1Derek J. Churchill2Jonathan T. Kane3Astrid Sanna4James S. Begley5Susan J. Prichard6Maureen C. Kennedy7Craig Bienz8Ryan D. Haugo9Annie C. Smith10Van R. Kane11C. Alina Cansler12School of Environmental and Forest Sciences University of Washington Seattle Washington USAWashington State Department of Natural Resources Olympia Washington USASchool of Environmental and Forest Sciences University of Washington Seattle Washington USASchool of Environmental and Forest Sciences University of Washington Seattle Washington USASchool of Environmental and Forest Sciences University of Washington Seattle Washington USAWashington Conservation Science Institute Roslyn Washington USASchool of Environmental and Forest Sciences University of Washington Seattle Washington USASchool of Interdisciplinary Arts and Sciences University of Washington Tacoma Washington USAThe Nature Conservancy Portland Oregon USAThe Nature Conservancy Portland Oregon USAWashington State Department of Natural Resources Olympia Washington USASchool of Environmental and Forest Sciences University of Washington Seattle Washington USAW.A. Franke College of Forestry & Conservation University of Montana Missoula Montana USAAbstract Interruption of frequent burning in dry forests across western North America and the continued impacts of anthropogenic climate change have resulted in increases in fire size and severity compared to historical fire regimes. Recent legislation, funding, and planning have emphasized increased implementation of mechanical thinning and prescribed burning treatments to decrease the risk of undesirable ecological and social outcomes due to fire. As wildfires and treatments continue to interact, managers require consistent approaches to evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing–based, analytical framework for conducting fire‐scale assessments of treatment effectiveness that informs local management while also supporting cross‐fire comparisons. We demonstrate this framework on the 2021 Bootleg Fire in Oregon and the 2021 Schneider Springs Fire in Washington. Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. The application of our framework showed that, in both fires, recent prescribed burning treatments were the most effective at reducing burn severity relative to untreated controls. In contrast, thinning‐only treatments only produced low/moderate‐severity effects under the more moderate fire weather conditions in the Schneider Springs Fire. Our framework offers a robust approach for evaluating treatment effects on burn severity at the scale of individual fires, which can be scaled up to assess treatment effectiveness across multiple fires. As climate change brings increased uncertainty to dry forest ecosystems of western North America, our framework can support more strategic management actions to reduce wildfire risk and foster resilience.https://doi.org/10.1002/ecs2.70073adaptive managementburn severityframeworkfuel reduction treatmentstreatment effectivenesswildfire |
| spellingShingle | Caden P. Chamberlain Garrett W. Meigs Derek J. Churchill Jonathan T. Kane Astrid Sanna James S. Begley Susan J. Prichard Maureen C. Kennedy Craig Bienz Ryan D. Haugo Annie C. Smith Van R. Kane C. Alina Cansler Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity Ecosphere adaptive management burn severity framework fuel reduction treatments treatment effectiveness wildfire |
| title | Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity |
| title_full | Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity |
| title_fullStr | Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity |
| title_full_unstemmed | Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity |
| title_short | Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity |
| title_sort | learning from wildfires a scalable framework to evaluate treatment effects on burn severity |
| topic | adaptive management burn severity framework fuel reduction treatments treatment effectiveness wildfire |
| url | https://doi.org/10.1002/ecs2.70073 |
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