This paper presents a new co-registration procedure of complementary point clouds captured by both Terrestrial (TLS) and Airborne Laser Scanning (ALS) technologies. Starting from the geographic position of the TLS point cloud, a geometric features recognition algorithm, which evaluates digital terrain models obtained from both ALS and TLS, was developed and implemented in a new GIS software (ForeSight®). As a case study, we tested this new approach using point clouds acquired from both hand-held mobile TLS and ALS sensors over 24 test sites located in a protected area in southern Italy, with the ultimate goal of characterizing the different forest stand structures. From each aligned point cloud, a plot-level spatially explicit index (Enhanced Structural Spatial Index, ESCI) was derived to assess the three-dimensional structure of the considered forest stands. Then, we compared structural features derived from the ESCI index with different computed ALS metrics. Finally, the most correlated ALS metrics were used as predictors to produce an ESCI-map of the entire region of interest. © 2021 Elsevier B.V.

Vegetation structure is one of the key factors in forest ecosystems. Especially understory structure has major implications for wildlife habitat selection, reproduction, and survival. Structural indices traditionally used to characterize understory vegetation are based on field vegetation surveys describing general features such as leaf area index (LAI), canopy cover or vegetation height, hiding much of the three-dimensional vegetation structure complexity. The application of terrestrial laser scanning (TLS) in forest ecological and management applications is becoming more effective. In this study, we use TLS data to quantify spatial attributes of forest subcanopy in four different forest strata ranging from 0.5 m to 10 m from the ground. We collected data in 12 plots of mature European beech (Fagus sylvatica L.) forests and 12 plots of mature black pine (Pinus nigra subsp. laricio Maire) forests, located in the Sila National Park, Italy. We propose a TLS-based approach to estimate a fine-scale vegetation density using the Plant Density Index (PDI) and to test the PDI at different height classes. We found a significant relationship between the PDI and the number of trees belonging to the dominant layer, using the Spearman correlation coefficient (r = 0.83, p<inf>val</inf> = 0.001). Basing on PDI values, a cluster analysis of the four subcanopy strata was carried out for deriving clusters of structurally homogeneous forest plots. Results identified three clusters in terms of the vegetation features in the horizontal height classes: the first cluster primarily includes Beech forests characterized by plots with the highest tree densities; the second one includes both Beech and Pine forests characterized by dense ground vegetation and shrubs and an intermediate tree density; the third group is represented by Pine forests with massive presence of vegetation lower strata and moderate tree density. Then, PCA allowed identifying the relationship between the considered subcanopy layers and forest plots. © 2021 Elsevier B.V.