ALS data for detecting habitat trees in a multi-layered mediterranean forest

Among the wide pool of ecosystem services provided by forests to human wellbeing, biodiversity conservation represents one of the most important topics of Sustainable Forest Management. Monitoring forest biodiversity is a challenging task as it includes all the life forms that can be found in a forest. However, the availability of inventory data is often inadequate to assess the biodiversity value of forests, therefore requiring improvements in monitoring activities and methods. In the last decades, several improvements have been made to reduce costs for collecting data and supporting monitoring and management activities. Particularly, remote sensing techniques have provided a significant contribution to forest and natural resource management and planning. Nevertheless, most of the information concern to the forest canopy and photosynthesis responses quantified through vegetation indices. Few information are available about tree habitats and other important ecological features. This study aims to demonstrate how ALS data can contribute to assess forest biodiversity through the detection of Habitat Trees. We use the Tree-Related Microhabitats, such as cavities, dead branches, injuries and woods, as a proxy to identify Habitat Trees and correlate them to the ALS metrics. Four statistical models were implemented to assess and map the biodiversity value in a mixed and multi-layered forest in Central Apennine