Tree-Related Microhabitats and Multi-Taxon Biodiversity Quantification Exploiting ALS Data

• Published in: Forests Vol. 15; no. 4; p. 660
• Main Authors: Parisi, Francesco, D’Amico, Giovanni, Vangi, Elia, Chirici, Gherardo, Francini, Saverio, Cocozza, Claudia, Giannetti, Francesca, Londi, Guglielmo, Nocentini, Susanna, Borghi, Costanza, Travaglini, Davide
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Airborne lasers; Algorithms; Beetles; Biodiversity; biodiversity conservation; Biological diversity; Birds; canopy height; Climate change; Coleoptera; Dependent variables; Environmental aspects; Environmental policy; Forest conservation; Forest management; forests; Forests and forestry; Habitats; Identification and classification; image analysis; Italy; Measurement; Microhabitats; Monitoring; Optical radar; Species richness; Statistical analysis; statistics; Taxa; Trees; Italy
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15040660

The quantification of tree-related microhabitats (TreMs) and multi-taxon biodiversity is pivotal to the implementation of forest conservation policies, which are crucial under the current climate change scenarios. We assessed the capacity of Airborne Laser Scanning (ALS) data to quantify biodiversity indices related to both forest beetle and bird communities and TreMs, calculating the species richness and types of saproxylic and epixylic TreMs using the Shannon index. As biodiversity predictors, 240 ALS-derived metrics were calculated: 214 were point-cloud based, 14 were pixel-level from the canopy height model, and 12 were RGB spectral statistics. We used the random forests algorithm to predict species richness and the Shannon diversity index, using the field plot measures as dependent variables and the ALS-derived metrics as predictors for each taxon and TreMs type. The final models were used to produce wall-to-wall maps of biodiversity indices. The Shannon index produced the best performance for each group considered, with a mean difference of −6.7%. Likewise, the highest R2 was for the Shannon index (0.17, against 0.14 for richness). Our results confirm the importance of ALS data in assessing forest biodiversity indicators that are relevant for monitoring forest habitats. The proposed method supports the quantification and monitoring of the measures needed to implement better forest stands and multi-taxon biodiversity conservation.