This paper presents the results of a research aimed at analysing the susceptibility to shallow landslides of a study area in the Calabria region (Southern Italy). These shallow landslides, which in some cases evolve as debris flows, periodically affect the study area, causing damage to structures and infrastructure. The involved soils come from the weathering of gneissic rocks and cover about 60% of the study area. To fulfil the goal of the research, the Transient Rainfall Infiltration and Grid-based Slope-Stability (TRIGRS) model was first used, assuming input data (including physical and mechanical parameters of soils) provided by the scientific literature. Then, the preliminary results obtained were used to properly locate in situ investigations that included sampling. Geotechnical laboratory tests allowed characterising the investigated soils, and related parameters were used as new input data of the TRIGRS model. The generated shallow landslide susceptibility scenario showed a good predictive capability based on the adoption of a cutoff-independent performance technique.

The use of unmanned aerial vehicles (UAVs) can significantly assist landslide detection and characterization in different geological contexts at a detailed scale. This study investigated the role of UAVs in detecting a first-failure landslide occurring in Calabria, South Italy, and involving weathered granitoid rocks. After the landslide event, which caused the interruption of State Road 107, a UAV flight was carried out to identify landslide boundaries and morphological features in areas where there are problems of safe access. The landslide was classified as flow-type, with a total length of 240 m, a maximum width of 70 m, and a maximum depth of about 6.5 m. The comparison of the DTMs generated from UAV data with previously available LIDAR data indicated significant topographic changes across the landslide area. A minimum negative value of −6.3 m suggested material removal at the landslide source area. An approximate value of −2 m in the transportation area signified bed erosion and displacement of material as the landslide moved downslope. A maximum positive value of 4.2 m was found in the deposition area. The landslide volume was estimated to be about 6000 m³. These findings demonstrated the effectiveness of UAVs for landslide detection, showing their potentiality as valuable tools in planning further studies for a detailed landslide characterization and for defining the most appropriate risk mitigation measures.