Parks2019

Référence

Parks, S.A., Holsinger, L.M., Koontz, M.J., Collins, L., Whitman, E., Parisien, M.-A., Loehman, R.A., Barnes, J.L., Bourdon, J.-F., Boucher, J., Boucher, Y., Caprio, A.C., Collingwood, A., Hall, R.J., Park, J., Saperstein, L.B., Smetanka, C., Smith, R.J., Soverel, N. (2019) Giving ecological meaning to satellite-derived fire severity metrics across North American forests. Remote Sensing, 11(14). (Scopus )

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. © 2019 by the authors.

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@ARTICLE { Parks2019,
    AUTHOR = { Parks, S.A. and Holsinger, L.M. and Koontz, M.J. and Collins, L. and Whitman, E. and Parisien, M.-A. and Loehman, R.A. and Barnes, J.L. and Bourdon, J.-F. and Boucher, J. and Boucher, Y. and Caprio, A.C. and Collingwood, A. and Hall, R.J. and Park, J. and Saperstein, L.B. and Smetanka, C. and Smith, R.J. and Soverel, N. },
    TITLE = { Giving ecological meaning to satellite-derived fire severity metrics across North American forests },
    JOURNAL = { Remote Sensing },
    YEAR = { 2019 },
    VOLUME = { 11 },
    NUMBER = { 14 },
    DOI = { 10.3390/rs11141735 },
    ART_NUMBER = { 1735 },
    NOTE = { cited By 26 },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071595309&doi=10.3390%2frs11141735&partnerID=40&md5=b4436dc8b6476d2fedd19afaba65655c },
    AFFILIATION = { Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station, US Forest Service, 790 E. Beckwith Ave, Missoula, MT 59801, United States; Earth Lab, University of Colorado, Boulder, CO 80303, United States; Graduate Group in Ecology, University of California, Davis, CA 95616, United States; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, VIC 3086, Australia; Arthur Rylah Institute for Environmental Research, Department of Environment, Land, Water and Planning, PO Box 137, Heidelberg, VIC 3084, Australia; Research Centre for Future Landscapes, La Trobe University, Bundoora, VIC 3086, Australia; Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320-122 Street, Edmonton, AB T6H 3S5, Canada; Alaska Science Center, US Geological Survey, 4210 University Drive, Anchorage, AK 99508, United States; National Park Service, Alaska Regional Office, 4175 Geist Rd., Fairbanks, AK 99709, United States; Département des Sciences du bois et de la Forêt, Faculté de Foresterie, de Géographie et de Géomatique, Université Laval, 2405, rue de la Terrasse, Quebec City, QC G1V 0A6, Canada; Société de Protection des Forêts Contre le feu, 715 7e rue de l'Aéroport, Quebec City, QC G2G 2S7, Canada; Centre for Forest Research, Université du Québec à Montréal, P.O. Box 8888, Centre-ville Station, Montréal, QC H3C 3P8, Canada; Direction de la Recherche Forestière, Ministère des Forêts, de la Faune et des Parcs du Québec, 2700, rue Einstein, Quebec City, QC G1P 3W8, Canada; Sequoia and Kings Canyon National Parks, National Park Service, Three Rivers, CA 93271, United States; Waterton Lakes National Park, Parks Canada, 1 Compound Rd., Waterton Park, AB T0K 2M0, Canada; BanffField Unit, BanffNational Park, Parks Canada, PO Box 900, Banff, AB T1L 1K2, Canada; US Fish and Wildlife Service, 1011 E. Tudor Rd., Anchorage, AK 99503, United States; Department of Applied Geomatics, Université de Sherbrooke, 2500 Boulevard de l'Université, Sherbrooke, QC J1K 0A5, Canada; Yellowstone National Park, National Park Service, Yellowstone National Park, 106 Stable StWY 82190, United States; Self-Employed, 206-270 3rd St. West, North Vancouver, BC V7M 1G1, Canada },
    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. © 2019 by the authors. },
    AUTHOR_KEYWORDS = { Burn severity; CBI; Composite burn index; Fire effects; Fire severity; Google Earth Engine; Random Forest },
    DOCUMENT_TYPE = { Article },
    SOURCE = { Scopus },
}

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