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DOI10.1016/j.rse.2020.111702
A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest
Gibson R.; Danaher T.; Hehir W.; Collins L.
发表日期2020
ISSN00344257
卷号240
英文摘要Accurate and consistent broad-scale mapping of fire severity is an important resource for fire management as well as fire-related ecological and climate change research. Remote sensing and machine learning approaches present an opportunity to enhance accuracy and efficiency of current practices. Quantitative biophysical models of photosynthetic, non-photosynthetic and bare cover fractions have not been widely applied to fire severity studies but may provide greater consistency in comparisons of different fires across the landscape compared to reflectance-based indices. We systematically tested and compared reflectance and fractional cover candidate severity indices derived from Sentinel 2 satellite imagery using a random forest (RF) machine learning framework. Assessment of predictive power (cross-validation) was undertaken to quantify the accuracy of mapping severity of new fires. The effect of environmental variables on the accuracy of the RF predicted severity classification was examined to assess the stability of the mapping across the landscape. The results indicate that fire severity can be mapped with very high accuracy using Sentinel 2 imagery and RF supervised classification. The mean accuracy was >95% for the unburnt and extreme severity class (complete crown consumption), >85% for high severity class (full crown scorch), >80% for low severity (burnt understory, unburnt canopy) and >70% for the moderate severity class (partial canopy scorch). Higher canopy cover and higher topographic complexity was associated with a higher rate of under-prediction, due to the limitations of optical sensors in viewing the burnt understorey of low severity classes under these conditions. Further research is aimed at improving classification accuracy of low and moderate severity classes and applying the RF algorithm to hazard reduction fires. © 2020
英文关键词Aerial photograph interpretation; Australia; Canopy density; Fire severity; Fractional cover; Machine learning; Normalised burn ratio; Random forest; Regional scale; Sentinel 2; Supervised classification; Topographic complexity
语种英语
scopus关键词Climate change; Decision trees; Fires; Learning systems; Mapping; Reflection; Remote sensing; Satellite imagery; Supervised learning; Aerial Photographs; Australia; Canopy density; Fire severity; Fractional cover; Normalised burn ratio; Regional scale; Sentinel 2; Supervised classification; Topographic complexity; Random forests; aerial photography; climate change; fire management; machine learning; remote sensing; satellite imagery; Sentinel; supervised classification; topography; understory; Australia
来源期刊Remote Sensing of Environment
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/179407
作者单位Remote Sensing and Landscape Science, Department of Planning, Industry and Environment, Alstonville, NSW 2477, Australia; Joint Remote Sensing Research Program, School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia; New South Wales Rural Fire Service, Sydney Olympic ParkNSW 2127, Australia; Department of Ecology, Environment & Evolution, La Trobe University, Bundoora, Victoria 3086, Australia; Arthur Rylah Institute for Environmental Research, Department of Environment, Land, Water and Planning, PO Box 137, Heidelberg, Victoria 3084, Australia; Research Centre for Future Landscapes, La Trobe University, Bundoora, Victoria 3086, Australia
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Gibson R.,Danaher T.,Hehir W.,et al. A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest[J],2020,240.
APA Gibson R.,Danaher T.,Hehir W.,&Collins L..(2020).A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest.Remote Sensing of Environment,240.
MLA Gibson R.,et al."A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest".Remote Sensing of Environment 240(2020).
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