Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems. The monitoring of forest health is, therefore, indispensable for the long-term conservation of forests and their sustainable management. In this regard, evaluating the amount and quality of dead wood is of utmost interest as they are favorable indicators of biodiversity. Apparently, remote sensing-based techniques have proven to be more efficient and sustainable with unprecedented accuracy in forest inventory. However, the application of these techniques is still in its infancy with respect to dead wood mapping. This study investigates for the first time the automatic classification of individual coniferous trees into five decay stages (live, declining, dead, loose bark, and clean) from combined airborne laser scanning data and color infrared images using Machine Learning methods. First, CIR colorized point clouds are created by fusing the ALS point clouds and the color infrared images. Then, with the colorized point cloud, individual tree segmentation is conducted using a semi-automatic approach, which are further projected onto four orthogonal planes displaying the side views of the trees in 2D. Finally, the classification is conducted on the multispectral point clouds and projected images using the three Machine Learning algorithms. All models achieved promising results, reaching overall accuracy (OA) of up to 90.9%, 90.6%, and 80.6% for CNN, RF, and PointNet, respectively. The experimental results reveal that the image-based approach notably outperformed the point cloud-based one, while spectral image texture is of the highest relevance to the success of categorizing tree decay. Our models could therefore be used for automatic determination of single tree decay stages and landscape-wide assessment of dead wood amount and quality using modern airborne remote sensing.

翻译：理解森林健康状况对于维护森林生态系统完整性至关重要。因此，森林健康监测对于森林长期保护及其可持续管理不可或缺。在此方面，评估枯木的数量与质量具有极高价值，因其是生物多样性的有利指标。显然，基于遥感的技术已在森林清查中展现出前所未有的精度，且被证明更具效率和可持续性。然而，这些技术在枯木制图中的应用仍处于初期阶段。本研究首次探索利用机器学习方法，结合机载激光扫描数据与彩色红外影像，自动将针叶单木分为五个腐朽阶段（活立木、衰退木、枯立木、树皮松动、树皮脱落）。首先，通过融合ALS点云与CIR影像生成CIR彩色点云。随后，利用彩色点云采用半自动方法进行单木分割，并将分割结果投影至四个正交平面以呈现树木的二维侧视图。最后，使用三种机器学习算法对多光谱点云及投影影像进行分类。所有模型均取得良好效果，其中CNN、RF和PointNet的总体精度分别达到90.9%、90.6%和80.6%。实验结果表明，基于影像的方法显著优于基于点云的方法，而光谱影像纹理对树木腐朽阶段分类的成功具有最高相关性。因此，本模型可借助现代机载遥感技术，用于自动确定单木腐朽阶段，并实现景观尺度上枯木数量与质量的评估。