Assessing Dendrometric Parameters Using GIS and LiDAR Flight Data: A Tree-by-Tree Study in Mont Avic Regional Park, Aosta Valley, NW Italy

The application of LiDAR (Light Detection and Ranging) technology in forestry has been steadily increasing, revolutionizing how forest ecosystems are monitored and managed.

This study leverages GIS and LiDAR flight data to assess dendrometric parameters on a tree-by-tree basis in Mont Avic Regional Park, located in the Autonomous Aosta Valley Region (NW Italy). The LiDAR data, collected during 2020-2021, was processed to derive Digital Terrain Models (DTM) and Digital Surface Models (DSM), enabling the computation of a Canopy Height Model (CHM) with a Ground Sampling Distance (GSD) of 0.5 m. Although an optimal CHM for forestry dendrometric assessments typically has a GSD of around 0.15-0.25 m, the high number of LiDAR returns, exceeding 12 per square meter, compensated for this issue. The CHM was segmented using a local maxima algorithm to delineate individual tree crowns. This study specifically focused on vertical biomass (VB) assessment, utilizing the 0.5 m resolution CHM to segment canopies and calculate species-specific incidence areas and related diameters through empirical formulas. These formulas, derived from similar regions and documented in literature, correlated tree crown measurements from GIS with diameters at 1.30 m for each species. Dendrometric formulas were applied to estimate tree volume, with validation performed using ground measurement data from randomly selected, evenly distributed areas within the study site. For each tree, the following parameters were obtained: height (H), crown area (C), diameter (D), volume (V), altitude (A), coordinates (X and Y in ED50 UTM 32N), tree species, and forestry category from species maps.

In conclusion, this integrated approach combining advanced remote sensing technologies with GIS underscores the potential for comprehensive forest ecosystem monitoring and management as well as in agro-forestry. Future perspectives include monitoring fire vulnerability through the Vegetation Health Index (VHI) and analyzing trends to support agro-forestry planning and management.