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The free availability of Sentinel-1 satellite radar images, combined with the wide-area coverage, the huge benefits-cost ratio, the short revisiting time and the non-invasiveness allows a continuous monitoring of ground displacements. In Italy, Sentinel-1-based continuous monitoring activities are operating in three regions and a possible chain for a practical use of satellite images for local authorities and civil protection operators is presented. A continuous monitoring is set up through the systematic collection of imagery and the repeated processing of each new pair acquired. The Persistent Scatterers (PS) resulting from such chain are subsequently analyzed by means of a data-mining algorithm for highlighting the points with relevant trend variations in the time series. In this way, a huge amount of data and wide areas may be monitored and temporal changes due to various phenomena could be assessed in a short time. The highlighted points, taking advantage of the surrounding PS data, are thus interpreted and classified according to a list of possible causes (e.g. accelerations of landslide or subsidence phenomena). The most relevant cases are notified to local authorities as potential active hazardous phenomena to be verified by means of field survey. A supporting scheme for the operative chain is here proposed and two case studies are presented. This operating workflow is intended to represent a standard procedure which could be applied on a national scale, aiming at the real-time monitoring of hazardous scenarios, and may be used as a guide for civil protection procedures for the risk reduction in large areas.

The potential of multi-interferometric approach applied to the Sentinel-1 acquisitions for the analysis of slope instabilities is presented and discussed through two different landslides of very different nature. In the first case, Sentinel-1 data, systematically acquired with short revisiting time and promptly processed allows for the quick identification of the acceleration suffered by the Carpineta landslide, a large, active earth slide in the Northern Apennines (Tuscany Region, Italy). In the second case, a post-event analyses of Sentinel-1 data permitted the identification of a clear precursory deformation signal for the Xinmo landslide (Mao County, Sichuan Province, China), a large rock avalanche occurred in the early morning of 24 June 2017. Results suggest that advances in satellite sensors, increase of computing capacity and refinement of data screening tools can contribute to the design of a new paradigm in satellite-based monitoring systems. Sentinel-1 data, systematically acquired over large areas with short revisiting time, could be used not only as a tool for mapping unstable areas, but also for landslide monitoring, at least for some typologies of sliding phenomena.

In Italy, three different operational continuous monitoring experiences based on the exploitation of Multi Temporal Synthetic Aperture Radar data (MTInSAR) Sentinel-1 data are here depicted, and the results obtained in one year have been analysed. Tuscany region (Central Italy) has been the first region to implement such service, followed by Valle d'Aosta and Veneto regions (North-West and North-East Italy, respectively). In detail, the services benefit from regularly updated deformation maps (every 12 days) to promptly detect anomalies of deformation, i.e., trend variations in the time series of displacement. In this work, anomalies detected between September 2019 and September 2020 are thus correlated with several types of factors, either related to the environment, intrinsic of the data or derived from ancillary data. A statistical analysis has been performed on the three regions, and are discretized into five macro-areas, namely: i) spatial and temporal statistics, related to the geographic setting and the temporal distribution of the anomalies; ii) parametric, i.e., related to the interferometric processing; iii) triggering factors; iv) environmental and geological factors; v) urban setting. The results derived from the analysis of this work show the obvious differences between the three regions, highlighting distinct distributions of the anomalies according to the different settings of each study area. Furthermore, results were analyzed, to provide a summary of the main findings obtained, giving a first evaluation of the services and hypothesizing future further improvements and applications. © 2021