# Review of Satellite Interferometry for Landslide Detection in Italy
## 1 Introduction  
Landslides are responsible for huge human and economic losses worldwide, producing direct and indirect impacts on the population and the local communities. Italy is one of the European countries that is most severely and frequently hit by landslides, primarily triggered by heavy rainfalls [1] and favored by its peculiar geological, morphological, and tectonic setting. The Italian landslide inventory (IFFI, _Inventario Fennomi Franosi Italiani_) includes more than 480,000 phenomena collected since 1999. The IFFI database covers almost 7% of the entire national territory [2]. Known and most-of-all unknown or newly activated landslides have a considerable impact on regional and local entities, posing a frequent and not totally forecastable risk to the population [3]. [PERSON] et al. [1] reported that, in a 60-year time span between 1950 and 2009, more than 6000 people were killed by landslides with an average of 16 deadly events per year. Some events such as the 1963 Vajont outburst, the 1987 Val di Pola landslide, or the 1998 Sarno mudslides caused thousands of victims [4; 5; 6]. All the Italian regions are hit by landslides with different temporal frequency. [PERSON] [7] collected thousands of events causing casualties and reported that Veneto (including almost 2000 victims for the Vajont outburst), Campania, and Lombardy are the regions that paid the highest human cost. Landslide typology and occurrence are strongly related to the geological and tectonic setting. Overall, rockfalls, rockslides, and rock avalanches in the Alpine arc and fast flows originated in volcanic ashes in the Campania region are the most hazardous and impactful phenomena [7]. This brief overview of the economic and social impact of landslides in Italy shows the strong need for proper monitoring tools that could support landslide risk management procedures and long-term risk reduction practices. The motion of a landslide can be analyzed by using ground-based and remotely sensed data [8]. The first group includes all the techniques exploiting instruments or sensors directly connected to the terrain or installed on buildings/objects. In this case, the monitoring results are the direct reflection of the motion undergone by the field instrumentation. The second group relies on the use of no-contact techniques, e.g., on the use of active or passive sensors that receive and record a signal interacting with the landslide surface or with objects involved by the landslide motion. The emitting/receiving sensor can be ground-based or mounted on a drone, an aircraft, or a satellite. In the last decade, Interferometric Synthetic Aperture Radar (InSAR) techniques have become widely used and broadly recognized tools for landslide mapping and monitoring. This is mainly due to the development of D-InSAR (Differential Interferometry) techniques that are based on the analysis of a couple of radar images acquired by an SAR (Synthetic Aperture Radar) sensor. The primary goal of such techniques is to derive an interferogram that expresses the phase difference for each image pixel between two passages of the satellite on the same area. By using multiple SAR images and the interferograms derived, it is possible to retrieve the temporal evolution of ground displacements over time. The time series of deformation are extracted from those pixels of an SAR image maintaining an optimal level of coherence (i.e., how stable the signal is) through the whole monitoring period. The selection of these pixels can be performed by using various algorithms [9] usually grouped in Point-like (or Persistent) Scatterers Interferometry (PSI [10]) and Distributed Scatterers Interferometry (DSI [11]) approaches. The final output of these multi-temporal Advanced Differential InSAR (A-DInSAR) analyses is a deformation map composed of thousands/millions of points, each one characterized by a value of annual velocity and by a time series of displacement.