Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests. Parks, S. A., Holsinger, L. M., Koontz, M. J., Collins, L., Whitman, E., Parisien, M., Loehman, R. A., Barnes, J. L., Bourdon, J., Boucher, J., Boucher, Y., Caprio, A. C., Collingwood, A., Hall, R. J., Park, J., Saperstein, L. B., Smetanka, C., Smith, R. J., & Soverel, N. Remote Sensing, 11(14):1735, January, 2019. Number: 14 Publisher: Multidisciplinary Digital Publishing Institute![Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{parks_giving_2019,
title = {Giving {Ecological} {Meaning} to {Satellite}-{Derived} {Fire} {Severity} {Metrics} across {North} {American} {Forests}},
volume = {11},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {2072-4292},
url = {https://www.mdpi.com/2072-4292/11/14/1735},
doi = {10.3390/rs11141735},
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.},
language = {en},
number = {14},
urldate = {2023-06-29},
journal = {Remote Sensing},
author = {Parks, Sean A. and Holsinger, Lisa M. and Koontz, Michael J. and Collins, Luke and Whitman, Ellen and Parisien, Marc-André and Loehman, Rachel A. and Barnes, Jennifer L. and Bourdon, Jean-François and Boucher, Jonathan and Boucher, Yan and Caprio, Anthony C. and Collingwood, Adam and Hall, Ron J. and Park, Jane and Saperstein, Lisa B. and Smetanka, Charlotte and Smith, Rebecca J. and Soverel, Nick},
month = jan,
year = {2019},
note = {Number: 14
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {Terrestrial Ecoregions (CEC 1997)},
pages = {1735},
}
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