Margherita Fiani

The aim of our work is to develop a methodology to identify the areas most prone to natural rockfall retention through the integrated use of remote sensing data. The area chosen as a case study is located in Campania (Italy) nearby Mount San Liberatore. In this area, which is itself geomorphologically predisposed to landslide risk, there are several rockfall risk hotspots, so defined because of the high exposed value constituted by an articulated infrastructure network located along the northwest slope of the mountain. The area is largely covered by dense vegetation, of which holm oak is the most representative type, characterized by a taproot apparatus that, giving it strength and stability, makes it an ideal tree for slope protection. Based on high-resolution multispectral satellite images, vegetation indices (VIs) were calculated to estimate health status, approximate age, average height, robustness, and vigor. Morphometric parameters suitable for describing slope dynamics were also calculated, derived from LiDAR data. The classification of areas with similar characteristics was carried out using Self-Organizing Maps. The results made it possible to identify all those areas where there is a greater contribution of protective forests in the mitigation of rockfall risk and, consequently, to identify areas to carry out a combined strengthening of protective actions.