Leveraging heterogeneous LiDAR data to model successional stages at tree species level in temperate forests

In the context of the ongoing biodiversity crisis, understanding forest ecosystems, their tree species composition, and especially the successional stages of their development is crucial. They collectively shape the biodiversity within forests and thereby influence the ecosystem services that forests provide, yet this information is not readily available on a large scale. Remote sensing techniques offer promising solutions for obtaining area-wide information on tree species composition and their successional stages. While optical data are often freely available in appropriate quality over large scales, obtaining light detection and ranging (LiDAR) data, which provide valuable information about forest structure, is more challenging. LiDAR data are mostly acquired by public authorities across several years and therefore heterogeneous in quality. This study aims to assess if heterogeneous LiDAR data can support area-wide modeling of forest successional stages at the tree species group level. Different combinations of spectral satellite data (Sentinel-2) and heterogeneous airborne LiDAR data, collected by the federal government of Rhineland-Palatinate, Germany, were utilized to model up to three different successional stages of seven tree species groups. When incorporating heterogeneous LiDAR data into random forest models with spatial variable selection and spatial cross-validation, significant accuracy improvements of up to 0.23 were observed. This study shows the potential of not dismissing initially seemingly unusable heterogeneous LiDAR data for ecological studies. We advocate for a thorough examination to determine its usefulness for model enhancement. A practical application of this approach is demonstrated, in the context of mapping successional stages of tree species groups at a regional level.