Ricerc@Sapienza

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 sta. For this reason, new ecient
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.