Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, th...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 11; H. 14; S. 1735
Hauptverfasser:	Parks, Sean, Holsinger, Lisa, Koontz, Michael, Collins, Luke, Whitman, Ellen, Parisien, Marc-André, Loehman, Rachel, Barnes, Jennifer, Bourdon, Jean-François, Boucher, Jonathan, Boucher, Yan, Caprio, Anthony, Collingwood, Adam, Hall, Ron, Park, Jane, Saperstein, Lisa, Smetanka, Charlotte, Smith, Rebecca, Soverel, Nick
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 23.07.2019
Schlagworte:	algorithms Artificial intelligence burn severity Canada CBI climate Climate variability composite burn index fire effects fire severity Fires Forest & brush fires forests Google Earth Engine Internet landscapes latitude Machine learning Mapping model validation National parks Parks & recreation areas Random Forest Remote sensing Spectra United States Variables Vegetation wildfires Canada Australia United States > US Quebec City Quebec Canada United States
ISSN:	2072-4292, 2072-4292
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Abstract	Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.
AbstractList	Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.
Author	Collingwood, Adam Whitman, Ellen Boucher, Yan Smetanka, Charlotte Barnes, Jennifer Bourdon, Jean-François Caprio, Anthony Smith, Rebecca Park, Jane Soverel, Nick Parks, Sean Holsinger, Lisa Saperstein, Lisa Loehman, Rachel Parisien, Marc-André Koontz, Michael Collins, Luke Hall, Ron Boucher, Jonathan
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Snippet	Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner...
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SubjectTerms	algorithms Artificial intelligence burn severity Canada CBI climate Climate variability composite burn index fire effects fire severity Fires Forest & brush fires forests Google Earth Engine Internet landscapes latitude Machine learning Mapping model validation National parks Parks & recreation areas Random Forest Remote sensing Spectra United States Variables Vegetation wildfires
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Title	Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests
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