Digital Canopies: Evaluating the Impact of Different LiDAR-Derived CHMs for Old-Growth Forest Mapping and Monitoring

The importance of old-growth forests in biodiversity conservation and climate change mitigation is currently at the forefront of global attention. In line with the latest European strategies for monitoring and restoring vital ecosystems, understanding and preserving such forests emerges as an environmental imperative. Proficiency in remote sensing technologies allows for a highly detailed understanding of the structural characteristics of these forests, providing several metrics and parameters to characterize the old-growthness degree and mapping old-growth forests. We present the first results of ongoing research to test the feasibility of using UAV (unpiloted aerial system)-based LiDAR (light detection and ranging) surveys in detecting and mapping the heterogeneity in structure parameters of primary old-growth beech forests. The main aim is to test different software/environment solutions to obtain a LiDAR-based canopy height model (CHM) to serve as the basis for crown delineation, tree height measurements, height distribution, and stand structure heterogeneity analyses. We tested the CHM obtained using commercial software (i.e., eCognition) and the potentiality of two free open-source tools, lidR (developed in R enviroment) and WhiteboxTools Open Core for Python (WdW; developed in Python enviroment). The proposed approach was tested in the old-growth beech forest of Val Cervara, within the protected area of the Abruzzo, Lazio, and Molise National Park in Italy. This work aims to be an initial contribution to assess the quality of data and analysis procedures for a better understanding of these valuable ecosystems, given the growing use and development of digital technologies for studying and monitoring forest ecosystems and the resulting increased availability of data.