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The redaction of landslide inventory is a fundamental task for risk management and territorial planning activities. The availability of synthetic aperture radar imagery, especially after the launch of Sentinel-1 mission, enables to systematically update landslide inventories covering wide areas in a reduced time frame and at different scales of analysis. In this work, SAR data processed from the fully automatic P-SBAS pipeline have been adopted to update the Italian national landslide database. Specifically, a matrix has been introduced by comparing past landslide state of activity obtained with Envisat data (2003–2010) and that computed with Sentinel-1 (2014–2018). The state of activity was defined by obtaining the projected velocity along the slope dip direction. The analysis involved about 56,000 landslides which showed at least one Sentinel-1 measurement point, of which 74% were classified as dormant, having annual average velocity 7 mm/year). Furthermore, a landslide reliability matrix was introduced on the landslide inventory updated with S1 data, using the measurement point (MP) density within each landslide and the standard deviation of the mean Vslope value of each landslide. In this case, the analysis revealed that more than 80% of landslides has values of reliability from average to very high. Finally, the 2D horizontal and vertical components were computed to characterize magnitude and direction of every type of landslides included in this work, showing that spreadings, deep-seated gravitation slope deformations, and slow flows showed a main horizontal movement, while complex and translational/rotational slides had more heterogeneity in terms of deformation direction. Hence, the work demonstrated that the application of fast and automatically nationwide Sentinel-1 MTInSAR (multi-temporal interferometry SAR) may provide a fundamental aid for landslide inventory update.

Landslide inventory maps are fundamental tools for territorial planning, recording the location, the state of activity and the type of mass movement that affects an area (Guzzetti et al., 2012). In the last decades, the satellite Remote Sensing has represented one of the most useful techniques for studying landslides with its capability to detect large portions (km-scale) of the Earth surface: in this sense, DInSAR (Differential Interferometry Synthetic Aperture Radar) data are capable of retrieving surface displacements with centimeter to millimeter accuracy. The launch of Sentinel-1 (S1) satellites and the flourishing of fully automatic processing chains has encouraged the development of national scale monitoring service for the study of natural and anthropogenic hazards. Accordingly, the Parallel Small Baseline Subset (P-SBAS) processing chain, in the framework of an Operative Agreement with the Italian Ministry of Economic Development (MiSE) aimed at generating the displacement time-series and corresponding velocity maps of the entire Italian territory, has significantly boosted the systematic update of the landslide state of activity.In this work, the Italian national database of landslides (IFFI landslide inventory) has been updated up to 2018 by exploiting national scale P-SBAS S1 analysis. In particular, the past landslide state of activity, which was obtained by exploiting the Envisat data (2003-2010 temporal range), has been compared with the one retrieved with P-SBAS S1 results (2014-2018 temporal range). With this comparative analysis, more than 56,000 landslides have been identified. The 74% of the studied landslides has been classified as dormant, having annual average velocity (projected along the slope direction) 7 mm/year). In addition, a landslide reliability matrix was introduced to assess the quality of the new updated inventory, by using the point density and the standard deviation of the mean Vslope value of each considered landslide. Finally, the 2D horizontal (along the E-W direction) and vertical components of the MPs have been computed, aiming at the characterization of each landslide's movement direction and magnitude. The obtained results show the heterogeneity and the complexity of the Italian territory, with major differences among each region and between the Alpine and Apennine sectors. The work demonstrates that nation-wide monitoring service Sentinel-1 DInSAR data, such as those generated by the P-SBAS method, can be very useful to systematically update landslide inventories, providing significant support to risk reduction practices.

To extract the most information from a national InSAR dataset it is imperative to understand the mechanisms leading to motion and how these manifest in an InSAR dataset such as the EGMS. The British Geological Survey (BGS) have been at the forefront of UK InSAR ground motion interpretation for the past 22 years; in projects such as ESA’s Terrafirma, thematic FP7 projects e.g. PanGeo and SubCoast, and provide expert advice to the UK government surrounding potential fracking sites and CCS sites along with a sustained research programme. BGS also produce national hazard susceptibility mapping, known as GeoSure, which is routinely used by insurance companies to assess ground motion hazards. The study of many epochs of InSAR data for many study sites provide the opportunity to examine how patterns of subsidence have evolved with time and how this relates to the processes taking place. This presentation will illustrate the evolution of ground motions within the UK over the last 30 years, case studies will highlight the lessons learnt especially with respect to how the change in geological process manifests in the InSAR signal. For example, a striking change in ground motion patterns over a time period occurred in the Newcastle and Durham Coalfield (Gee et al., 2017) where a dramatic change in the pattern of motion was observed between subsidence in the 1990’s ERS data and uplift in the 2000’s ENVISAT data. This was found to relate to change in minewater pumping. Therefore, to create a national interpreted ground motion product it is important to not only understand the mechanism of motion but also understand how that process changes over time. Recent research has focused on the prediction of which UK hazards will be visible and measurable by InSAR (Novellino et al., 2023), and on the prediction of likely natural motion rates for the natural geological subsidence processes (Jones et al., 2013). BGS have developed automatic AI and ML tools which examine not only the InSAR average velocity but also the time series to group areas of similar motion characteristics and to then detect when changes occur (Festa et al., 2023, Hussain et al., 2021.). The application of such tools to the EGMS time series and integration of results with BGS GeoSure national hazard susceptibility datasets provide a pathway to the ongoing interpretation of national GB wide InSAR datasets. The above experience puts the BGS in a unique position for the exploitation of the new Copernicus European Ground Motion Service which represents the first freely available national UK InSAR dataset. Over the coming years it is BGS’ ambition is to apply our experience of UK hazard susceptibility, how these hazards manifest in InSAR data, our fledgling automated interpretation tools and visibility mapping to produce the first dynamic country-wide interpreted ground motion information layer; a value added product which not only tells the user what the motion is but also identifies the likely reason for the motion along with forecasts of how such motion might evolve in the coming years.

The recent release of European Ground Motion Service (EGMS) products implemented under the responsibility of the Copernicus Land Monitoring Service (CLMS) guarantees free and accessible Europe-wide ground motion data for ground deformation analysis at the local and regional scales. The need for value-adding services and tools for optimal dissemination of radar data from the Copernicus Sentinel-1 satellite mission urges the scientific community to find efficient solutions. A desktop R-based application with a user-friendly interface capable of automatically downloading and transforming EGMS products delivered as large .csv tiles, equivalent to a radar burst into geospatial databases, is presented here. EGMStream is a self-contained desktop app that enables users to systematically store, customize, and convert ground movement data into geospatial databases, burst per burst or for an area of interest directly selectable on the app interface.

The launch of the Sentinel-1 constellation by the European Copernicus Program, primarily devoted to scientific community research, has allowed acquiring huge volumes of radar images with worldwide coverage and a short temporal sampling (12 days reduced to 6 days thanks to their position at 180° in the same orbit). The systematic collection of imagery and the repeated processing of each new pair of images acquired opened the unprecedent possibility of conducting a continuous monitoring of Earth surface deformations, such as subsidence and slope instabilities over different geomorphological settings. At present, Tuscany, Veneto and Valle d’Aosta regions (Italy) are benefiting from systematical Sentinel-1-based monitoring of geological and geomorphological hazards. The promising outcomes so far obtained constitute a decisive step towards near-real-time monitoring and therefore a valid support for geohazard risk management activities. Retracing the pattern set by the encouraging results from the three Italian Regions, an operating workflow chain is proposed in the framework of an operational monitoring service, from the collection of satellite images to the possibility of conducting field surveys. The procedure is based on 4 different steps: i) continuous collection of Sentinel-1 images, constant data processing through an MT-InSAR (Multi-Temporal Interferometric Synthetic Aperture Radar) technique and exploitation of a data-mining algorithm able to retain only meaningful Measurement Points (MP) in terms of abrupt change of displacement rate; ii) radar-interpretation of the filtered MP for the detection of the possible causes of the anomalies through the use of ancillary informative layers or pre-existing databases; iii) dissemination of the relevant radar-interpreted information to hydrological risk managing actors by a direct alert or periodic bulletins; iv) field investigation, preliminary risk assessment and possible remedial works design. The procedure was successfully applied gathering evidence of its usefulness in practical terms. The cases of the Bosmatto landslide (Valle d’Aosta Region) and the case of the Zeri Landslide (Tuscany Region) which belong to two alpine and apennine environments, respectively, are reported. In the first example, in response to a relevant acceleration interpreted from the MP available on the area of interest, an alert was sent to the regional officers who increased their awareness related to the risk posed by the Bosmatto Landslide. In the second example, a monitoring bulletin which is periodically delivered for the Tuscany Region pointed out the meaningfulness and persistency of anomalies identified in the Zeri municipality. This led the regional authorities to conduct a site investigation oriented to the assessment of preliminary risks. The presented results highlight the effective benefits-cost ratio, the high precision and the short amount of time required to complete the procedure representing a best practice for the early detection of ground deformation events.

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.

Continuous monitoring of the Earth surface is fundamental for the development and the evolution of the society, to reduce the risks posed by major geo-hazards like landslides, subsidence and sinkholes, which have a large impact on urban areas and can cause direct and indirect socio-economic losses. The start of spaceborne Synthetic Aperture Radar systems represented a milestone for the control of the territory, since SAR-based monitoring enables accurate measurement of the surface deformation over large areas, with a frequency dependent on the revisit time of the satellites. In this sense, the launch of the European Space Agency Sentinel-1 mission, characterized by a 6-days repeat pass, portrayed a great innovation and a step towards near-real-time monitoring. In this work, we present the first results of the continuous monitoring of the Veneto region (Northeastern Italy) performed by means of Sentinel-1 data, in the framework of an operational monitoring service. The procedure applied is based on a systematic processing chain made of four steps: i) Continuous generation of Sentinel-1 ground deformation maps, providing Measurement Points (MP) characterized by annual average velocity (mm/yr) and displacement Time Series (TS); ii) TS screening and classification, applied after each new satellite acquisition, to identify any change in the deformation pattern, according to a selected threshold; iii) constant update of the “anomalies” and their classification, according to the type of deformation; iv) warning to local authorities, in case of persistent and significant anomalous trends which require further investigations and field surveys. Its first application on the Veneto region shows promising outcomes, evidencing those areas characterized by movements that can be detected by SAR satellites. A few examples of this operational procedure are here shown, such as the cases of Lamosano, where a translational slide involves the local village, or of Recoaro Terme, where the Mt. Rotolon landslide is constantly studied. Moreover, subsidence is also a major threat in Veneto region, testified by the long-term phenomena of the NE plain (Verona and Vicenza provinces) and by the city of Venice, where the interaction of tides and subsidence causes the periodical flooding (“acqua alta”) of the renowned UNESCO site. The presented results want to demonstrate that the constant and continuous monitoring of the territory through Sentinel-1 data represents a best practice for the detection of ground deformation events, aiming at the natural risk mitigation for the development of the human environment.

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