A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study.

Several studies have verified the suitability of LiDAR for the estimation of forest metrics over large areas. In the present study we use LiDAR as support for the characterization of structure, volume, biomass and naturalistic value in mixed-coniferous forests in the Alps. Stem density, height and structure in the test plots were derived using a mathematical morphology function applied directly on the LiDAR point cloud. Volume and biomass were then computed using regression models. From these data, digital maps describing the horizontal and vertical forest structure of the forest were derived. A strong agreement (accuracy of the map = 97%; K-Cohen = 94%) between LiDAR land use map (i.e. bare soil, forest, shrubs) and ground data was found, while a moderate agreement between coniferous vs. broadleaves stand map derived from LiDAR data and ground surveys was detected (accuracy = 73%; K-Cohen = 60%). An analysis of the forest structure map derived from LiDAR data revealed a prevalence of even-age stands (66%) in comparison to the multilayered and uneven-aged forests (20%). In particular, the even-age stands, whether adult or mature, were overwhelming (33%). A moderate agreement was then detected between this map and ground data (accuracy = 68%; K-Cohen = 58%). Moreover, strong correlations between LiDAR-estimated and ground-measured volume and aboveground carbon stocks were detected. Related observations also showed that stem density can significantly be estimated considering adult and mature forests, but not when considering younger categories because of the low LiDAR posting density (2.8 points m-2). Regarding environmental issues, forest structure detection using LiDAR permitted to discriminate the different contribution to biodiversity and ecological stability. In fact, a significant difference in floristic diversity indexes (species richness, R; Shannon index, H’) was found among structural classes, in particular between pole wood (R=15 and H’=2.8; P <0.01) and multilayer forest (R=31 and H’=3.4) or thicket (R=28 and H’=3.4) where both the indexes reached their maximum values.