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The main aim of this work was to assess rockfall susceptibility along coastal cliffs by testing various applications created by international scientific community. The selected study area is located in the southern part of the Cilento coast (southern Italy). It represents an important tourist attraction, belonging to the "Cilento,-Vallo di Diano National Park". This area was also certificated as the only national park in the Mediterranean area included in the UNESCO World Heritage List in 1998, and in 2010 also gained the title of Geopark. The coast is characterized by several pocket beaches, often bounded by high cliffs, that during the summer season are frequented by a large number of people reaching them by foot or by boats. For this reason, real condition of risk associated to rockfalls exist all along this coastal segment. A multi-scale methodological approach was used in this study. First of all, a geomorphological analysis of the whole coastal segment between Capo Palinuro to the north and Scario village to the south, was carried out. In this phase all the cliffs and the pocket beaches were characterized and mapped, accounting for their lithology and morphometry. Then, the analysis focused on 4 test pocket beaches (Buondormire, Arco Naturale, Risima, Punta Garagliano) in order to define rockfall susceptibility scenarios by means of thematic maps overlay (Di Crescenzo & Santo, 2007). Great resolution photomosaics of each cliffs were carried out allowing the elaboration of detailed geological, geomorphological and susceptibility maps. The third step included a very detailed scale analysis of a cliff by means of remote sensing techniques such as Digital Photogrammetry (Structure from Motion) and LiDAR application (TLS). The selected test area was the Palinuro Natural Arch. Key insights into the use of TLS in rock slope investigation include the capability of remotely obtaining the orientation of slope discontinuities, which constitutes a great step forward in rock mechanics (Abellán et al., 2014). The structural conditions of the rock masses forming the sea cliff were defined by means of various methodologies using 3D point cloud data. Subsequently, these data allowed to identify the main failure mechanisms of the rock face. Finally, the kinematically unstable areas were highlighted using a script that computes an index of susceptibility to rockfalls based on the spatial distribution of failure mechanisms (Matasci, 2015). The comparison with measurements collected in the field allowed the validation of all the data coming from remote sensing analysis. |
