气候变化领域集成服务门户(CCPortal): Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach

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DOI	10.1016/j.rse.2020.111893
	Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach
	García M.; North P.; Viana-Soto A.; Stavros N.E.; Rosette J.; Martín M.P.; Franquesa M.; González-Cascón R.; Riaño D.; Becerra J.; Zhao K.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Providing accurate information on fire effects is critical to understanding post-fire ecological processes and to design appropriate land management strategies. Multispectral imagery from optical passive sensors is commonly used to estimate fire damage, yet this type of data is only sensitive to the effects in the upper canopy. This paper evaluates the sensitivity of full waveform LiDAR data to estimate the severity of wildfires using a 3D radiative transfer model approach. The approach represents the first attempt to evaluate the effect of different fire impacts, i.e. changes in vegetation structure as well as soil and leaf color, on the LiDAR signal. The FLIGHT 3D radiative transfer model was employed to simulate full waveform data for 10 plots representative of Mediterranean ecosystems along with a wide range of post-fire scenarios characterized by different severity levels, as defined by the composite burn index (CBI). A new metric is proposed, the waveform area relative change (WARC), which provides a comprehensive severity assessment considering all strata and accounting for changes in structure and leaf and soil color. It showed a strong correlation with CBI values (Spearman's Rho = 0.9 ± 0.02), outperforming the relative change of LiDAR metrics commonly applied for vegetation modeling, such as the relative height of energy quantiles (Spearman's Rho = 0.56 ± 0.07, for the relative change of RH60, the second strongest correlation). Logarithmic models fitted for each plot based on the WARC yielded very good performance with R2 (± standard deviation) and RMSE (± standard deviation) of 0.8 (± 0.05) and 0.22 (± 0.03), respectively. LiDAR metrics were evaluated over the King Fire, California, U.S., for which pre- and post-fire discrete return airborne LiDAR data were available. Pseudo-waveforms were computed after radiometric normalization of the intensity data. The WARC showed again the strongest correlation with field measures of GeoCBI values (Spearman's Rho = 0.91), closely followed by the relative change of RH40 (Spearman's Rho = 0.89). The logarithmic model fitted using WARC offered an R2 of 0.78 and a RMSE of 0.37. The accurate results obtained for the King Fire, with very different vegetation characteristics compared to our simulated data, demonstrate the robustness of the new metric proposed and its generalization capabilities to estimate the severity of fires. © 2020 Elsevier Inc.
英文关键词	Fire effects; Full waveform simulation; King Fire; LiDAR; Radiative transfer models; Severity
语种	英语
scopus关键词	Damage detection; Ecology; Fire hazards; Fires; Radiative transfer; Statistics; Structural design; Vegetation; Airborne lidar data; Composite burn indices; Generalization capability; Mediterranean ecosystem; Multi-spectral imagery; Radiative transfer model; Radiometric normalization; Vegetation structure; Optical radar; damage; design method; forest fire; lidar; Mediterranean environment; radiative transfer; remote sensing; vegetation structure; waveform analysis; California; United States
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/179268
作者单位	Environmental Remote Sensing Research Group, Department of Geology, Geography and the Environment, Universidad de Alcalá, Calle Colegios 2, Alcalá de Henares, 28801, Spain; Global Environmental Modelling and Earth Observation (GEMEO), Department of Geography, Swansea UniversitySA2 8PP, United Kingdom; Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States; Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Spanish National Research Council (CSIC), Albasanz 26-28, Madrid, 28037, Spain; Department of Environment, National Institute for Agriculture and Food Research and Technology (INIA), Ctra. Coruña, Km. 7,5, Madrid, 28040, Spain; Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, 139 Veihmeyer Hall, One Shields Avenue, Davis, CA 95616, United States; School of Environment and Natural Resources, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH 4469...
推荐引用方式 GB/T 7714	García M.,North P.,Viana-Soto A.,et al. Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach[J],2020,247.
APA	García M..,North P..,Viana-Soto A..,Stavros N.E..,Rosette J..,...&Zhao K..(2020).Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach.Remote Sensing of Environment,247.
MLA	García M.,et al."Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach".Remote Sensing of Environment 247(2020).