ABSTRACT Forest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as the Light Detection and Ranging (LiDAR) Height Diversity Index (LHDI), the LiDAR Height Evenness Index (LHEI), and vertical and horizontal continuity of vegetation, as well as topographic metrics, were obtained in raster format from low point density data. In order to map burned and unburned areas, differentiate fire occurrence dates, and distinguish between old and more recent fires, a sample of pixels was previously selected to assess the existence of differences in forest structure using the Kruskal–Wallis test. Then, k-nearest neighbors algorithm (k-NN), support vector machine (SVM) and random forest (RF) classifiers were compared to select the most accurate technique. The results showed that, in more recent fires, around 70% of the laser returns came from grass and shrub layers, yielding low LHDI and LHEI values (0.37–0.65 and 0.28–0.46, respectively). In contrast, the areas burned more than 20 years ago had higher LHDI and LHEI values due to the growth of the shrub and tree strata. The classification of burned and unburned areas yielded an overall accuracy of 89.64% using the RF method. SVM was the best classifier for identifying the structural differences between fires occurring on different dates, with an overall accuracy of 68.79%. Furthermore, SVM yielded an overall accuracy of 75.49% for the classification between old and more recent fires.

中文翻译：

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使用机载激光扫描数据表征受火灾影响的地中海景观中的森林结构多样性

摘要 森林火灾可以改变森林结构和组成，低密度机载激光扫描 (ALS) 可以成为评估火灾后植被响应的宝贵工具。本研究的目的是分析地中海松树的结构多样性差异。1986 年至 2009 年不同日期受野火影响的森林。 几种类型的 ALS 指标，例如光探测和测距 (LiDAR) 高度多样性指数 (LHDI)、LiDAR 高度均匀度指数 (LHEI) 以及垂直和水平连续性植被以及地形指标是从低点密度数据以栅格格式获得的。为了绘制燃烧和未燃烧区域的地图，区分火灾发生日期，并区分旧的和最近的火灾，之前选择了一个像素样本来使用 Kruskal-Wallis 检验评估森林结构差异的存在。然后，比较 k-最近邻算法 (k-NN)、支持向量机 (SVM) 和随机森林 (RF) 分类器，以选择最准确的技术。结果表明，在最近的火灾中，大约 70% 的激光返回来自草和灌木层，产生低 LHDI 和 LHEI 值（分别为 0.37-0.65 和 0.28-0.46）。相比之下，由于灌木和乔木地层的生长，20 多年前燃烧的区域具有更高的 LHDI 和 LHEI 值。使用 RF 方法对烧毁和未烧毁区域的分类产生了 89.64% 的总体准确率。SVM 是识别不同日期火灾之间结构差异的最佳分类器，总体准确率为 68.79%。此外，SVM 对旧火灾和近期火灾的分类产生了 75.49% 的总体准确率。