Abstract 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.

中文翻译：

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评估 LiDAR 数据在火灾损失评估中的潜力：一种辐射传输模型方法

摘要 提供有关火灾影响的准确信息对于了解火灾后的生态过程和设计适当的土地管理策略至关重要。来自光学无源传感器的多光谱图像通常用于估计火灾损失，但此类数据仅对上层树冠的影响敏感。本文使用 3D 辐射传输模型方法评估全波形 LiDAR 数据的灵敏度，以估计野火的严重程度。该方法首次尝试评估不同火灾影响对 LiDAR 信号的影响，即植被结构以及土壤和叶子颜色的变化。FLIGHT 3D 辐射传输模型用于模拟代表地中海生态系统的 10 个地块的全波形数据，以及由复合燃烧指数 (CBI) 定义的具有不同严重程度的各种火灾后情景。提出了一个新的度量标准，即波形面积相对变化 (WARC)，它提供了综合严重性评估，考虑了所有地层并考虑了结构、叶和土壤颜色的变化。它显示出与 CBI 值（Spearman's Rho = 0.9 ± 0.02）的强相关性，优于常用于植被建模的 LiDAR 指标的相对变化，例如能量分位数的相对高度（Spearman's Rho = 0.56 ± 0.07，对于相对变化） RH60，第二强的相关性）。基于 WARC 为每个图拟合的对数模型产生了非常好的性能，R2（± 标准偏差）和 RMSE（± 标准偏差）分别为 0.8 (± 0.05) 和 0.22 (± 0.03)。LiDAR 指标在美国加利福尼亚州 King Fire 进行了评估，其中提供了火前和火后离散返回机载 LiDAR 数据。在强度数据的辐射测量归一化之后计算伪波形。WARC 再次显示出与 GeoCBI 值（Spearman's Rho = 0.91）的实地测量的最强相关性，紧随其后的是 RH40 的相对变化（Spearman's Rho = 0.89）。使用 WARC 拟合的对数模型提供 0.78 的 R2 和 0.37 的 RMSE。为 King Fire 获得的准确结果，与我们的模拟数据相比，具有非常不同的植被特征，