Combined Methodology Based on Field Spectrometry and Digital Photography for Estimating Fire Severity
Combined Methodology Based on Field Spectrometry and Digital Photography for Estimating Fire Severity
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基于现场光谱测量和数码摄影的组合方法来估计火灾严重程度
DOI：
10.1109/jstars.2008.2011624
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发表时间：
2008
影响因子：
5.5
通讯作者：
J. Riva
中科院分区：
文献类型：
--
作者：
R. M. Llovería;F. Pérez;A. García;J. Riva
火灾严重程度是影响火烧区发展的重要因素之一。记录反射率值变化的地面研究提高了对大面积空间火灾严重程度的区分。本研究的目的是通过调查火灾后表面材料和反射率值之间的关系，以确定这些光谱区域最敏感的火灾严重程度。在西班牙发生两次自然火灾后，立即对总共34个田间地块进行了分析。为了获得火灾后的数据，我们使用了一个便携式结构，以获得垂直的照片和反射率值在可见光-近红外（VIS-NIR）范围。光谱反射率和卷积的陆地卫星专题成像仪（TM）波段统计相关的个人火灾后的材料和火灾的严重程度指数，燃烧产品指数（CPI），来自他们。对火后物质最敏感的波长分别为450.6、758.2和797 nm，分别用于灰、黑碳和植被，尽管这些波长包括在可比较的行为范围内。最后，逐步多元回归模型（SMLR）的数据。SMLR预测灰，黑碳和植被水平的反射率数据的准确性高于75%。Landsat-TM波段的估计精度略低。CPI的严重性指数也很好地估计使用反射率数据或TM波段（r2 = 0.925和r2 = 0.840，分别）。
Fire severity can be considered one of the most influential factors in the postfire development of burnt areas. Ground level studies documenting changes in reflectance values improve the discrimination of spatial fire severity across large areas. The objective of this study was to determine those spectral regions most sensitive to fire severity levels by investigating the relationship between postfire surface materials and reflectance values. A total of 34 field plots were analyzed immediately following two natural fires in Spain. To obtain postfire data, we used a portable structure to obtain vertical photographs and reflectance values in the visible-near-infrared (VIS-NIR) range. Spectral reflectance and convolved Landsat Thematic Mapper (TM) bands were statistically correlated with individual postfire materials and with a fire severity index, the Combustion Products Index (CPI), derived from them. The wavelengths most sensitive to postfire materials were 450.6, 758.2, and 797 nm for ash, black carbon, and vegetation, respectively, although these wavelengths were included within ranges of comparable behavior. Finally, stepwise multiple regression models (SMLRs) were applied to the data. SMLR predicted ash, black carbon, and vegetation levels from reflectance data with accuracies higher than 75%. Estimation from Landsat-TM bands yielded slightly lower accuracies. The CPI severity index was also well estimated using either reflectance data or TM bands (r 2 = 0.925 and r 2 = 0.840 , respectively).