## 2 Dataset and Methods
### Land Surface Quantitative Analysis  
The objective classifications of terrain types, at the base of the quantitative geomorphic analysis, require the use of both local and statistical terrain parameters helpful for defining the topographic signature of different surface processes and landforms [32]. Nonetheless, given the strong variability of landforms in size and tridimensional shape, due to different morphogenetic factors, many terrain parameters have been proposed to provide a quantitative description of topography [32; 33]. In this research, two main geomorphometric parameters were selected among numerous DEM-based terrain parameters, considering the specific research aim as well as the spatial scale of the analyses performed. The selected parameters were calculated starting from the available 2 m-cell-sized DEM.  
The first parameter is the slope angle, derived by means of the surface slope algorithm running in the Spatial Analyst extension of the GIS platform. For each DEM cell, this algorithm computes the first derivative of the elevation surface, or the maximum change in elevation between a cell and its eight neighbors (i.e., the steepest downhill descent from the cell) [34].  
The second parameter is the surface roughness that can be successfully used to define different landslide features and to examine the landslide activity [35; 36]. Different methods have been proposed to compute the surface roughness [37; 38; 39], although one of the most used methods is simply based on the statistical dispersion of heights or slopes. In this work, the surface roughness was calculated as the standard deviation of the elevation values within a \(4\times 4\) moving window. This method, among many calculation methods revised by [36], was demonstrated as one of those useful for evidencing the topographic signature of large landslides. In order to obtain the surface roughness values, the focal statistics tool running in the ArcGIS 10.6 platform was adopted.  
For supporting the spatial interpretation of both parameters, color-coded maps were produced and superimposed with a hillshade map, thus favoring the visualization of both terrain morphology and the spatial variation of parameters. Descriptive statistics for both parameters were calculated and reported as an inset for each map. Finally, for both parameters the number of pixels per bin (i.e., pixels in each parameter class) was reported using separate histograms for the southern and northern coastal sectors.  
A composite altimetric profile parallel to the coastline was created, starting from the 2 m-cell-sized DEM. This composite profile reports the elevation distribution for progressive distance along three different tracks, thus allowing for a more accurate description of coastal morphology. The first track permits delineation of the cliff escarpment morphology (i.e., upslope zone); the second track favors the description of the morphology along the intermediate slope portion (i.e., midslope zone); and the third track portrays the base of the coastal slope, at about 50 m in from the present shoreline (i.e., footslope zone). The extraction and visualization of both slope angle and surface roughness values along two distinct transects support the study of the distribution of these parameters along different portions of the slope. This visualization increases the evidence of parameter value variation due to the presence of landslides of different type, size, and state of activity. The parameters were extracted along the midslope zone (i.e., at the +200 m a.s.l. contour line) and along the footslope (i.e., at the +20 m a.s.l. contour line).