Leveraging readily available heterogeneous LiDAR data to enhance modeling of successional stages at tree species level in temperate forests

Abstract

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 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.

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest‐dwelling bats in Germany

“…The full set of environmental variables consisted of eight different variable groups (Figure 3): Spectral remote sensing data from a Sentinel-2 time series (ESA), indices derived from airborne LiDAR data (GeoBasis-DE/LVermGeoRP, 2021), climate data (DWD Climate Data Center [CDC] 2014[CDC] , 2023a[CDC] , 2023b[CDC] , 2023c[CDC] , 2023d, bioclimatic data (Fick & Hijmans, 2017), a global canopy height map produced with GEDI data (Potapov et al, 2021), indices derived from a tree species map (TSM; covering the six most important tree species in the study area; Bald et al, 2024; (OpenStreetMap, 2023). Detailed information on the acquisition and processing of these variables is provided in the Appendix.…”

“…Furthermore, variables derived from a tree species map (TSM) covering all forested areas in Rhineland-Palatinate were utilized. This map represents six main tree species groups, including Douglas fir, pine, spruce, beech, oak, and other deciduous trees (Bald et al, 2024). The…”

“…In this study, we tested this hypothesis by comparing the influence of environmental variables that are representing the same information, but at different levels of detail (e.g., tree species composition vs. mixed, coniferous, and deciduous forest classes) on model performance and the resulting HSM maps for three forest-dwelling bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland-Palatinate, Germany. A customized and targeted land cover dataset that provides forest habitat information at the tree species level for the entire federal state of Rhineland-Palatinate, Germany (Bald et al, 2024) was used, in combination with high-information remote sensing datasets such as light detection and ranging (LiDAR), also available for the entire federal state of Rhineland-Palatinate, to optimally reflect the ecological requirements of the target species.…”