Abstract The assessment of the health conditions of trees in forests is extremely important for biodiversity, forest management, global environment monitoring, and carbon dynamics. There is a vast amount of research using remote sensing (RS) techniques for the assessment of the current condition of a forest, but only a small number of these are concerned with detection and classification of dead trees. Among the available RS techniques, only the airborne laser scanner (ALS) enables dead tree detection at the single tree level with high accuracy. The main objective of the study was to identify spruce, pine and deciduous trees by alive or dead classifications. Three RS data sets including ALS (leaf-on and leaf-off) and color-infrared (CIR) imagery (leaf-on) were used for the study. We used intensity and structural variables from the ALS data and spectral information derived from aerial imagery for the classification procedure. Additionally, we tested the differences in the classification accuracy of all variants contained in the data integration. In the study, the random forest (RF) classifier was used. The study was carried out in the Polish part of the Bialowieza Forest (BF). In general, we can state that all classifications, with different combinations of ALS features and CIR, resulted in high overall accuracy (OA ≥ 90%) and Kappa (κ > 0.86). For the best variant (CIR_ALSWSn-FH), the mean values of overall accuracy and Kappa were equal to 94.3% and 0.93, respectively. The leaf-on point cloud features alone produced the lowest accuracies (OA = 75–81% and κ = 0.68–0.76). Improvements of 0-0.04 in the Kappa coefficient and 0–3.1% in the overall classification accuracy were found after the point cloud normalization for all variants. Full-height point cloud features (F) produced lower accuracies than the results based on features calculated for half of the tree height point clouds (H) and combined FH. The importance of each of the predictors for different data sets for tree species classification provided by the RF algorithm was investigated. The lists of top features were the same, independent of intensity normalization. For the classification based on both of the point clouds (leaf–on and leaf-off), three structural features (a proportion of first returns for both half-height and full-height variants and the canopy relief ratio of points) and two intensity features from first returns and half-height variant (the coefficient of variation and skewness) were rated as the most important. In the classification based on the point cloud with CIR features, two image features were among the most important (the NDVI and mean value of reflectance in the green band).

中文翻译：

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使用多时相 ALS 数据和 CIR 图像进行物种相关的单棵死树检测

摘要 森林树木健康状况的评估对于生物多样性、森林管理、全球环境监测和碳动力学极为重要。有大量的研究使用遥感 (RS) 技术来评估森林的当前状况，但只有少数研究涉及死树的检测和分类。在可用的 RS 技术中，只有机载激光扫描仪 (ALS) 能够在单棵树级别上以高精度检测死树。该研究的主要目的是通过活的或死的分类来识别云杉、松树和落叶树。该研究使用了三个 RS 数据集，包括 ALS（叶子上和叶子上）和彩色红外 (CIR) 图像（叶子上）。我们使用来自 ALS 数据的强度和结构变量以及从航空影像中获得的光谱信息进行分类。此外，我们测试了数据集成中包含的所有变体在分类准确度上的差异。在研究中，使用了随机森林 (RF) 分类器。该研究是在比亚沃维耶扎森林 (BF) 的波兰部分进行的。总的来说，我们可以说所有分类，具有 ALS 特征和 CIR 的不同组合，导致高整体准确度 (OA ≥ 90%) 和 Kappa (κ > 0.86)。对于最佳变体 (CIR_ALSWSn-FH)，总体准确度和 Kappa 的平均值分别等于 94.3% 和 0.93。仅叶上点云特征产生的准确度最低（OA = 75-81% 和 κ = 0.68-0.76）。0-0的改进。在对所有变体进行点云归一化后，发现 Kappa 系数为 04，整体分类准确度为 0-3.1%。全高点云特征 (F) 产生的准确度低于基于为一半树高点云 (H) 和组合 FH 计算的特征的结果。研究了由 RF 算法提供的用于树种分类的不同数据集的每个预测器的重要性。顶级特征的列表是相同的，独立于强度归一化。对于基于两个点云（leaf-on 和leaf-off）的分类，三个结构特征（半高和全高变体的首次回归比例和点的冠层浮雕比）和首次回归和半高变体的两个强度特征（变异系数和偏度）被评为最重要的。在基于具有 CIR 特征的点云的分类中，两个图像特征是最重要的（NDVI 和绿带中反射率的平均值）。