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Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity.

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Title: Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity.
Authors: Chamberlain, Caden P.1 (AUTHOR) cc27@uw.edu, Meigs, Garrett W.2,3 (AUTHOR), Churchill, Derek J.1,2 (AUTHOR), Kane, Jonathan T.1 (AUTHOR), Sanna, Astrid1 (AUTHOR), Begley, James S.4 (AUTHOR), Prichard, Susan J.1 (AUTHOR), Kennedy, Maureen C.5 (AUTHOR), Bienz, Craig6,7 (AUTHOR), Haugo, Ryan D.6 (AUTHOR), Smith, Annie C.2 (AUTHOR), Kane, Van R.1 (AUTHOR), Cansler, C. Alina8 (AUTHOR)
Source: Ecosphere. Dec2024, Vol. 15 Issue 12, p1-22. 22p.
Subject Terms: *Effect of human beings on climate change, *Tropical dry forests, *Prescribed burning, *Wildfire prevention, *Forest fires, *Wildfires, Fire weather, Treatment effectiveness
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. [ABSTRACT FROM AUTHOR]
Copyright of Ecosphere is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity.
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  Data: <searchLink fieldCode="AR" term="%22Chamberlain%2C+Caden+P%2E%22">Chamberlain, Caden P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cc27@uw.edu</i><br /><searchLink fieldCode="AR" term="%22Meigs%2C+Garrett+W%2E%22">Meigs, Garrett W.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Churchill%2C+Derek+J%2E%22">Churchill, Derek J.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kane%2C+Jonathan+T%2E%22">Kane, Jonathan T.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sanna%2C+Astrid%22">Sanna, Astrid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Begley%2C+James+S%2E%22">Begley, James S.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Prichard%2C+Susan+J%2E%22">Prichard, Susan J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kennedy%2C+Maureen+C%2E%22">Kennedy, Maureen C.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bienz%2C+Craig%22">Bienz, Craig</searchLink><relatesTo>6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haugo%2C+Ryan+D%2E%22">Haugo, Ryan D.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Smith%2C+Annie+C%2E%22">Smith, Annie C.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kane%2C+Van+R%2E%22">Kane, Van R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cansler%2C+C%2E+Alina%22">Cansler, C. Alina</searchLink><relatesTo>8</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Ecosphere%22">Ecosphere</searchLink>. Dec2024, Vol. 15 Issue 12, p1-22. 22p.
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  Data: *<searchLink fieldCode="DE" term="%22Effect+of+human+beings+on+climate+change%22">Effect of human beings on climate change</searchLink><br />*<searchLink fieldCode="DE" term="%22Tropical+dry+forests%22">Tropical dry forests</searchLink><br />*<searchLink fieldCode="DE" term="%22Prescribed+burning%22">Prescribed burning</searchLink><br />*<searchLink fieldCode="DE" term="%22Wildfire+prevention%22">Wildfire prevention</searchLink><br />*<searchLink fieldCode="DE" term="%22Forest+fires%22">Forest fires</searchLink><br />*<searchLink fieldCode="DE" term="%22Wildfires%22">Wildfires</searchLink><br /><searchLink fieldCode="DE" term="%22Fire+weather%22">Fire weather</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Ecosphere is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/ecs2.70073
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 1
    Subjects:
      – SubjectFull: Effect of human beings on climate change
        Type: general
      – SubjectFull: Tropical dry forests
        Type: general
      – SubjectFull: Prescribed burning
        Type: general
      – SubjectFull: Wildfire prevention
        Type: general
      – SubjectFull: Forest fires
        Type: general
      – SubjectFull: Wildfires
        Type: general
      – SubjectFull: Fire weather
        Type: general
      – SubjectFull: Treatment effectiveness
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      – TitleFull: Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity.
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            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
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