# Review of Satellite Interferometry for Landslide Detection in Italy
## 6 How InSAR Products Have Been Used for Landslide Studies in Italy
### Landslide Monitoring  
The term \"monitoring\" can have different facets. Some authors use \"monitoring\" to indicate the analysis of multi-temporal InSAR datasets that are not updated and refer to a defined time period. In this review, these applications can fall into the categories \"back-analysis\" or \"landslide characterization.\" For the authors, \"monitoring\" refers to the most novel applications of satellite interferometry, focused on time series data mining and near-real-time approaches. It is worth recalling that, as defined by the Decree of the President of the Council of Ministers (DPCM) of 27 February 2004, any monitoring activity with a temporal update of less than 30 days is considered real-time.  
Interferometric data can evidence accelerations and temporal changes relevant for the detection of a modified behavior of the landslide, potentially leading to its failure [183; 184].  
A satellite monitoring system requires a dedicated processing platform (with high capacity requirements) and an algorithm able to detect time series changes. Some authors have worked on the definition of methods applied in Italian case studies to highlight such temporal shifts.  
[PERSON] et al. [185] proposed a manual classification of time series to identify trend changes induced by ground motion. The same author improved this methodology by proposing a semi-automatic method, based on the definition of two statistical indexes (Deviation Indexes, D\({}_{\text{I}}\)) able to quantify the trend differences before and after a breaking time defined by the user [186]. [PERSON] et al. [89] further automatized this process and developed a standalone application able to classify a set of time series according to their temporal pattern: (i) uncorrelated, (ii) linear, (iii) bilinear, (iv) quadratic, and (v) discontinuous. The software was tested on Envisat InSAR products and applied in some municipalities affected by landslides in the Emilian Apennine.  
These tools were developed when satellites had long repeat pass times and a real monitoring service based on such methods that could not be deployed. Recently, the launch of the Sentinel-1 constellation greatly reduces the temporal acquisition frequency, up to 6 days. This factor, altogether with the open policy on data distribution and the wide-area capabilities, made Sentinel-1 images the perfect candidates for the launch of the first satellite monitoring systems. This could not be possible without the consolidation of processing algorithms and the availability of cloud computing services.  
The Tuscany region is the first worldwide example of such services. Sentinel-1-derived InSAR products are used for the early detection of landslides and other ground motions [187]. [PERSON] et al. [155] showed that it is now possible to detect landslide temporal changes accordingly to external triggering factors, e.g., an intense rainfall event. The main limitation of this method is related to the magnitude of the acceleration. Indeed, if the trend change exceeds the phase unwrapping capability of the deformation model applied, phase aliasing cannot be avoided, and the acceleration cannot be properly detected.  
Table 1 proposes a summary of the reference papers used in this review to present each class.