In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study aims at predicting microhabitats in a mixed and multi-layered Mediterranean forest using Airborne Laser Scanning data through the implementation of a Machine Learning algorithm. The study focuses on the identification of LiDAR metrics useful for detecting microhabitats according to the recent hierarchical classification system for Tree-related Microhabitats, from single microhabitats to the habitat trees. The results demonstrate that Airborne Laser Scanning point clouds support the prediction of microhabitat abundance. Better prediction capabilities were obtained at a higher hierarchical level and for some of the single microhabitats, such as epiphytic bryophytes, root buttress cavities, and branch holes. Metrics concerned with tree height distribution and crown density are the most important predictors of microhabitats in a multi-layered forest.

中文翻译：

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使用机载激光扫描的机器学习算法预测与树相关的微生境

在过去的几年中，与树木有关的微生境和栖息地树木的出现和丰富，已成为整个欧洲作为森林生物多样性指标的关注焦点。然而，在野外观察微生境需要时间和训练有素的人员。因此，需要用于大规模识别和映射的新型高效半自动系统。这项研究旨在通过实施机器学习算法，使用机载激光扫描数据来预测地中海混交和多层森林中的微生境。这项研究的重点是根据最近针对与树相关的微生境的分层分类系统，从单个微生境到生境树，识别可用于检测微生境的LiDAR指标。结果表明，机载激光扫描点云支持微生境丰度的预测。在较高的层次级别上，对于某些单个微生境，例如附生苔藓植物，根支撑腔和分支孔，可以获得更好的预测能力。与树的高度分布和树冠密度有关的度量标准是多层森林中微生境的最重要预测指标。