## 4 Results
Maps of absolute value of displacement rates along slope direction in the analyzed period (January 2012-January 2015) and in the last year (2014) are shown in Figure 6.  
It is worth to point out that scatterers localized within the considered area correspond to man-made features, such as road surface, buildings, retaining walls, and electricity pylons. Assessment of Ground Instability Kinematics by Available Landslide Inventory Map and Satellite Interferometric Products  
As established in the second step of the proposed approach, displacement rate of interferometric products measured in the last year (Figure 6c) has been taken into account to investigate kinematics of ground instability phenomena, in January 2015. Interferometric products highlighted the occurrence of very slow-moving phenomena, with annual displacement velocities in the order of some mm/year. In fact, the maximum displacement rate in the last year is about 18 mm/year. A geostatistical cluster analysis of identified targets was performed to select stable and unstable pixels (Figure 7). Firstly, to define the threshold value discriminating stable from unstable targets, the distribution of the modulus of velocities along slope direction versus their coefficient of variation (CV), given by the ratio between the standard deviation \(\sigma\) and the velocity modulus \(|\mu|\), for all selected scatterers, has been considered. The threshold value determined is about 2 mm/year and corresponds to a value for which standard deviation is higher than mean velocity value (positive correlation coefficient, CV \(\geq\) 1) (Figure 7a). The comparison between displacement velocity threshold value and velocities measured in the last year (2014) for each target, highlighted that about 48% out of 1876 targets recorded within the considered buffer area was classified as unstable. It should be noted that such analysis is feasible only for pixels inside which at least one target has been identified. About 690 pixels within the fishnet grid could be monitored by using satellite products (Figure 7b): Most of them are located on a linear infrastructure pavement, while a few pixels have been identified in the surrounding areas characterized by the presence of diffuse vegetation. Subsequently, the clustering process based on Homogeneity Index definition, allowed to identify anomalous and unstable pixels, defined as the ones with at least 50% of \"unstable\" targets inside them (HI \(\geq\) 0.5) (Figure 7c). About 380 pixels were classified as unstable, while 310 as stable. \begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite** & **COSMO-SkyMed (ASI Agency)** \\ \hline Microwave band & X \\ Acquisition mode & Ascending \\ Incidence angle (\({}^{\circ}\)) & 29.34 \\ Revisiting time (days) & 16 \\ Temporal span & 25 January 2012–01 January 2015 \\ Number of scenes & 52 \\ Cell resolution in azimuth and range (m) & \(3\times 3\) \\ Processing approach & CPT-TSC \\ PS density (PS/km\({}^{2}\)) & \(\sim\)600 \\ Measurement precision: & \\ _-linear velocity_ & \(\pm\)1.5 mm/yr \\ _-single displacement_ & \(\pm\)4.0 mm \\ _-geolocation (x,y,z)_ & \(\pm\)1.5 m \\ Correction factor [C] & (0,0, +1.0) \\ V\({}_{\text{-SLOPE}}\) PS data velocity range (mm/year) & (\(-\)14.5, +3.8) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main parameters of used SAR dataset. Figure 6: Example of buffer zone and fishnet grid (**a**) along a section of “P.R. 264” and displacement velocity maps along slope direction of targets identified by COSMO-SkyMed ascending images into buffer zone: In the period January 2012–January 2015 (**b**) and in 2014 (**c**). Figure 7: Detection of ground instability kinematics in January 2015: V-slope versus CV distribution of the interferometric population for identification of velocity threshold discriminating stable from unstable targets (**a**); map of pixels which can be monitored (**b**), map of stable/unstable pixels (**c**) and kinematics zoning map (**d**).