Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach

REMOTE SENSING OF ENVIRONMENT

卷:247

Article

Garcia, Mariano¹  North, Peter²  Viana-Soto, Alba¹  Stavros, Natasha E.³  Rosette, Jackie²  Pilar Martin, M.⁴  Franques, Magi¹  Gonzalez-Cascon, Rosario⁵  Riano, David^4,6  Becerra, Javier⁴  Zhao, Kaiguang^7,8 

[1] Univ Alcala, Dept Geol Geog & Environm, Environm Remote Sensing Res Grp, Calle Colegios 2, Alcala De Henares 28801, Spain

[2] Swansea Univ, Dept Geog, Global Environm Modelling & Earth Observat GEMEO, Swansea SA2 8PP, W Glam, Wales

[3] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA

[4] CSIC, Environm Remote Sensing & Spect Lab SpecLab, Albasanz 26-28, Madrid 28037, Spain

[5] Natl Inst Agr & Food Res & Technol INIA, Dept Environm, Ctra Coruna,Km 7,5, Madrid 28040, Spain

[6] Univ Calif Davis, Ctr Spatial Technol & Remote Sensing CSTARS, 139 Veihmeyer Hall,One Shields Ave, Davis, CA 95616 USA

[7] Ohio State Univ, Sch Environm & Nat Resources, Ohio Agr Res & Dev Ctr, Wooster, OH 44691 USA

[8] Ohio State Univ, Sch Environm & Nat Resources, Environm Sci Grad Program, Columbus, OH 43210 USA

关键词: LiDAR;    Radiative transfer models;    Full waveform simulation;    Fire effects;    Severity;    King Fire;   

DOI  :  10.1016/j.rse.2020.111893

来源: Elsevier

【 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 R-2 (+/- 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 R-2 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.

【 授权许可】

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