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Giving ecological meaning to satellite-derived fire severity metrics across North American forests

Parks Sean A., Holsinger Lisa M., Koontz Michael J., Collins Luke, Whitman Ellen, Parisien Marc-André, Loehman Rachel A., Barnes Jennifer L., Bourdon Jean-François, Boucher Jonathan, Boucher Yan, Caprio Anthony C., Collingwood Adam, Hall Ron J., Park Jane, Saperstein Lisa B., Smetanka Charlotte, Smith Rebecca J. et Soverel Nick. (2019). Giving ecological meaning to satellite-derived fire severity metrics across North American forests. Remote Sensing, 11, (14), p. 1735.

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URL officielle: http://dx.doi.org/doi:10.3390/rs11141735

Résumé

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.

Type de document:Article publié dans une revue avec comité d'évaluation
ISSN:2072-4292
Volume:11
Numéro:14
Pages:p. 1735
Version évaluée par les pairs:Oui
Date:2019
Identifiant unique:10.3390/rs11141735
Sujets:Sciences naturelles et génie > Sciences appliquées > Foresterie et sciences du bois
Sciences naturelles et génie > Sciences naturelles > Biologie et autres sciences connexes
Sciences naturelles et génie > Sciences naturelles > Sciences de la terre (géologie, géographie)
Département, module, service et unité de recherche:Départements et modules > Département des sciences fondamentales
Mots-clés:burn severity, CBI, composite burn index, fire effects, fire severity, Google Earth Engine, random forest
Déposé le:09 sept. 2021 20:36
Dernière modification:09 sept. 2021 20:36
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Creative Commons LicenseSauf indication contraire, les documents archivés dans Constellation sont rendus disponibles selon les termes de la licence Creative Commons "Paternité, pas d'utilisation commerciale, pas de modification" 2.5 Canada.

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