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Abstract The study aims to analyse ground displacement conditions observed over an Underground Gas Storage (UGS) site located at Hatfield Moors (United Kingdom), with a focus on understanding its implications for decarbonization efforts. The location serves as an active onshore storage site and was used as an analogy to assess ground motion implications around Carbon Capture and Storage (CCS) by the British Geological Survey (BGS) as part of the SENSE (Assuring integrity of CO2 storage sites through ground surface monitoring) project. Given the value of continuous and real-time monitoring of ground movements induced by gas storage activities, the study leverages satellite Interferometric Synthetic Aperture Radar (InSAR) data to assess the environmental impact of UGS operations. Using free and open-source Sentinel-1 satellite data, ground motion patterns over Hatfield Moors are analysed, highlighting displacements ranging from − 5.0 to -10.0 mm/year within the peat bog. In addition, the Time Series (TS) of ground displacement from January 2018 to December 2022 reveals a seasonality in ground motion, with uplift observed in late winter and subsidence in late summer, showing a periodicity of approximately 1 year and a magnitude of +/-10.0 mm. Through in-depth analysis, the study highlights the need to understand the underlying causes of ground fluctuations at gas storage sites. This paper shows that InSAR has the versatility to integrate seamlessly with different monitoring tools and methodologies, opening avenues for comprehensive and holistic analyses. Cross-correlation analyses further elucidate temporal relationships between different datasets by evaluating InSAR time series, UGS injection/withdrawal data and piezometric data. This involves decomposing the TS into distinct components, including trend, seasonality and residuals. The case of Hatfield Moors shows a significant discrepancy between the UGS data and the InSAR TS, while also demonstrating a clear correlation between the groundwater data and the InSAR TS. By integrating insights from geology, hydrology and remote sensing technologies, the study navigates the complexities inherent in areas of overlapping phenomena. Accurate interpretation is essential for informed decision making, particularly at sites such as Hatfield Moors, where the convergence of natural peat motion and storage operations highlights the need for interdisciplinary analysis to understand the underlying causes of ground fluctuation.

Abstract Spaceborne-based monitoring for environmental purposes has become a well-established practice. The recent progress of synthetic aperture radar (SAR) sensors, including through the European Space Agency’s (ESA) Sentinel-1 constellation, has enabled the scientific community to identify and monitor several geohazards, including subsidence ground deformations. A case study in the Tuscany Region, Italy, highlights the effectiveness of interferometric synthetic aperture radar (InSAR) in detecting abrupt increases in ground deformation rates in an industrial area of Montemurlo municipality. In this case, InSAR data enabled prompt identification of the phenomenon, supporting the authorities in charge of environmental management to thoroughly investigate the situation. First, an on-site validation was performed via field surveys confirming the presence of cracks and fissures on some edifices. Further analysis, including water pumping rates, settlement gauge and topographic levelling, corroborated the InSAR data's findings regarding vertical deformation. Integration of collected data allowed for spatial identification and assessment of the subsidence bowl and its source depth recognized by the remote sensing data. The Montemurlo case offers a procedural guideline for managing abrupt accelerations, identified by InSAR data in subsidence-prone areas due to fluid overexploitation. In fact, these data proved useful in helping local authorities responsible for hydrogeomorphological risk management. With the exacerbation of deformation issues in subsidence-prone regions due to climate change, early detection and monitoring of such phenomena are increasingly crucial, with InSAR data playing a central role in achieving this goal.

Landslides are a major natural hazard, a worldwide problem with direct and indirect consequences, causing fatalities, colossal socio-economic damage [...]

Abstract Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide events in newspapers or crowdsourcing platforms allows fast observation, surveying and classification. Currently, few studies have been produced on the combination of social media data and traditional sensors. This gap indicates that it is unclear how their integration can effectively provide emergency managers with appropriate knowledge. In this work, rainfall, human lives, and earmarked fund data sources were correlated to “landslide news”. Analysis was applied to obtain information about temporal (2010–2019) and spatial (regional and warning hydrological zone scale) distribution. The temporal distribution of the data shows a continuous increase from 2015 until 2019 for both landslide and rainfall events. The number of people involved and the amount of earmarked funds do not exhibit any clear trend. The spatial distribution displays good correlation between “landslide news”, traditional sensors (e.g., pluviometers) and possible effects in term of fatalities. In addition, the cost of soil protection, in monetary terms, indicates the effects of events.

Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

The need for implementing efficient value-adding tools able to optimise Earth Observation data usage, compels the scientific community to find innovative solutions for the downstream of Earth Observation information. In this paper we present an unsupervised and automated approach based on Principal Component Analysis (PCA) and K-means clustering to detect patterns of natural or anthropogenic ground deformation from Interferometric Synthetic Aperture Radar (InSAR) Time Series. For our proof-of-concept, we focus on the Valle d’Aosta region (Northwest Italy) where mass wasting processes frequently occurs, interacting with human activities and infrastructures. The large volumes of Sentinel-1 data produced allows for retrieving horizontal and vertical Time Series from multi-geometry data fusion of Line-of-Sight (LOS) InSAR measurements. The added benefit of combining ascending/descending InSAR data and interpolating displacements in time at different time steps is here explored prior to data dimensionality reduction and feature extraction through PCA. The retrieved principal components serve as a continuous solution for cluster membership indicators in the K-means clustering method, allowing to define spatially and temporally coherent displacement phenomena. The signal of the ground deformation clusters is then deconstructed into the underlying trend and seasonality components to enhance the interpretability of the classified satellite InSAR features. Using InSAR Time series data spanning 2014–2020, the proposed approach detects several slope movements and anthropogenic deformations with both linear and seasonal displacement behaviours. The results demonstrate the potential applicability of our transferable approach to the development of automated ground motion analysis systems.

Urban landslides have brought challenges to developing countries undergoing urbanization. Rapid approaches to assess ground deformation are required when facing the challenge of insufficient geological survey methods. Additionally, it is indeed a challenge to map landslide-affected areas, especially precipitation-induced landslides, through optical remote sensing methods. This study applied SAR change detection methods to map the slope failure event of the San Jorge Kantutani landfill site in La Paz, Bolivia, which occurred in April 2019, and Multi-Temporal Synthetic Aperture Radar Interferometry (MTInSAR) methods to assess pre- and post-failure ground stability related to this event. We found that the amplitude information of high-resolution COSMO-SkyMed SAR imagery and its texture information can be very useful in landslide mapping, especially in situations in which optical images are not available because of complex meteorological conditions and the similar spectral characteristics between the original land cover and landslide deposits. The MTInSAR analyses found that there was already significant deformation of more than 50 mm/year along the slope direction over this site before the landslide, and such deformation could be clearly discriminated from the surrounding environment. After the landslide event and the remobilization of the landslide deposit, the slope still shows a deformation velocity of more than 30 mm/year. The SAR amplitude change detection and MTInSAR fully exploited the SAR data in landslide studies and were useful in back analyzing the occurred landslides; this could be a good method for monitoring the ground stability of La Paz or even on a national scale over the long term for reducing the catastrophic effects of geological hazards in this landslide-prone city.

Abstract The concept of climate change has grown in recent decades, influencing the scientific community to conduct research on meteorological parameters and their variabilities. Research on global warming, as well as on its possible economic and environmental consequences, has spread over the last 20 years. Diffused changes in trends have been stated by several authors throughout the world, with different developments observed depending on the continent. Following a period of approximately 40 days of almost continuous rain that occurred from October to November 2019 across the Italian territory and caused several hazards (e.g., floods and landslides), a relevant question for decision-makers and civil protection actors emerged regarding the relative frequencies of given rainfall events in the Warning Hazard Zones (WHZs) of Italy. The derived products of this work could answer this question for both weather and hydrogeological operators thanks to the frequency and spatio-temporal distribution analyses conducted on 10-year daily rainfall data over the entire Italian territory. This work aspires to be an additional tool used to analyse events that have occurred, providing further information for a better understanding of the probability of occurrence and distribution of future events.

Gas consumption is subject to large seasonal fluctuations between the summer season (period with lower request) and the winter season (time with increased consumer demand). Underground gas storage applications (UGS) help to ensure a steady and reliable supply of natural gas, even during periods of peak demand, smoothing price fluctuations and providing a means of balancing the supply and demand of natural gas on a daily, weekly, or seasonal basis. However, UGS activities can induce vertical ground displacement, which is usually strictly associated with the injection and withdrawal of gas into/from the reservoir. It is necessary to carefully monitor and manage the potential impact of UGS activities on the subsurface and surface to ensure the stability and safety of the local environment. The Interferometric Synthetic Aperture Radar (InSAR) technique can provide a wide range of high-precision information on seasonal surface deformation associated with UGS activities useful for increasing the amount of information on ground deformation monitoring. This study introduces a unique and replicable approach to investigating freely available ground movement data for a fractured aquifer reservoir located in the Madrid Basin (Guadalajara, Spain), which is currently employed for seasonal underground gas storage applications. Notably, this study gives a comprehensive comparison of InSAR results of UGS activity in a deep aquifer, leveraging data that are entirely open-source and easily accessible. The Yela UGS project exploits a carbonate reservoir (dolomite) managed, since 2012, by Enagás, the Spanish main Transmission System Operator (TSO). InSAR data from 2015 to 2021 provided a full and coherent ground deformation pattern of the area. Based on this data, a fully integrated volumetric variation model was developed, elucidating the effects of gas storage activity. A significant correlation between the periodic injection/withdrawal rates of natural gas and InSAR ground deformation over time was identified.

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.

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.

Natural gas is an indispensable resource not evenly distributed in the world. The gas supply chain is characterized by large imbalances between supply and demand, where the underground gas storage (UGS) application plays a key role for creating strategic reserves, taking advantage of geological structures. On the contrary, human activities will require clean energy with near-zero greenhouse gas emissions to be environmentally viable. A key element of this strategy is the carbon capture and storage (CCS) application useful for confining CO2 into the geosphere to reduce anthropogenic emissions. The development of appropriate injection methods and long-term monitoring systems for leak detection of the underground storage of natural gas and CO2 is important to prevent negative effects, such as ground deformations and micro seismic events. In this work, a variety of monitoring applications were gathered and critically analyzed for a total of 60 scientific contributions spanning the world. This bibliographic work shows an analytical and statistical overview of the most common use of UGS and CCS, representing the different goals of these two applications and analyzing the main monitoring techniques used in the gathered contributions. Currently, UGS monitoring requires further development, especially through multidisciplinary approaches useful for identifying possible effects on the surface and gas leaks at depth; meanwhile, CCS solutions are still at the experimental stage, also because of the high costs for large-scale applications that still need specific research. The state of the art of these two very different practices can improve the further development of new monitoring approaches or additional methods.

The Sciara del Fuoco (SdF) collapse scar at Stromboli is an active volcanic area affected by rapid morphological changes due to explosive/effusive eruptions and mass-wasting processes. The aim of this paper is to demonstrate the importance of an integrated analysis of multi-temporal remote sensing (photogrammetry, COSMO-SkyMed Synthetic Aperture Radar amplitude image) and marine geophysical data (multibeam and side scan sonar data) to characterize the main morphological, textural, and volumetric changes that occurred along the SdF slope in the 2020–2021 period. The analysis showed the marked erosive potential of the 19 May 2021 pyroclastic density current generated by a crater rim collapse, which mobilized a minimum volume of 44,000 m3 in the upper Sciara del Fuoco slope and eroded 350,000–400,000 m3 of material just considering the shallow-water setting. The analysis allowed us also to constrain the main factors controlling the emplacement of different lava flows and overflows during the monitored period. Despite the morphological continuity between the subaerial and submarine slope, textural variations in the SdF primarily depend on different processes and characteristics of the subaerial slope, the coastal area, the nearshore, and “deeper” marine areas.

In the last decade satellite remote sensing has become an effective tool for monitoring geo-hazard-induced ground motions, and has been increasingly used by the scientific community. Direct and indirect costs due to geo-hazards are currently rising, causing serious socio-economics and casualty losses. Therefore, creating a priority list turns out to be essential to highlight the most relevant ground deformations and to better focus risk management practices at regional scale. The Sentinel-1 constellation, thanks to the 6-days repeatability and the free availability of the data, allows to easily update the geo-hazard-induced ground motions, compared to other kind of satellite sensors. In this work, a hot-spot-like method is presented by filtering a large stack of Sentinel-1 images processed by means of the SqueeSAR algorithm. Three periods, with six months repetitiveness, have been analysed in order to evaluate the behaviour and evolution of deformation clusters. The target area is Tuscany Region, located in the central part of Italy and affected by a wide gamma of geohazards, ranging from landslides to large subsidence areas. The final output is a geo-database of ground motions that can be used by regional authorities to prioritize and to effectively plan local risk reduction actions.

The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory.

Abstract We present the continuous monitoring of ground deformation at regional scale using ESA (European Space Agency) Sentinel-1constellation of satellites. We discuss this operational monitoring service through the case study of the Tuscany Region (Central Italy), selected due to its peculiar geological setting prone to ground instability phenomena. We set up a systematic processing chain of Sentinel-1 acquisitions to create continuously updated ground deformation data to mark the transition from static satellite analysis, based on the analysis of archive images, to dynamic monitoring of ground displacement. Displacement time series, systematically updated with the most recent available Sentinel-1 acquisition, are analysed to identify anomalous points (i.e., points where a change in the dynamic of motion is occurring). The presence of a cluster of persistent anomalies affecting elements at risk determines a significant level of risk, with the necessity of further analysis. Here, we show that the Sentinel-1 constellation can be used for continuous and systematic tracking of ground deformation phenomena at the regional scale. Our results demonstrate how satellite data, acquired with short revisiting times and promptly processed, can contribute to the detection of changes in ground deformation patterns and can act as a key information layer for risk mitigation.

In the last decades, ground deformations were investigated, analysed and monitored using several methods. As a consequence of a spreading urbanization, several phenomena, e.g. landslide and subsidence, were emphasized or triggered causing not only socio-economic damages, but, in some cases, also casualties. The investigation and mapping of these phenomena are important for both local authorities and civil protection in order to promote a higher conscientious urban planning and to highlight the more hazardous areas. Furthermore, the information are a key point for social development connected to the awareness of the environment and the related risk. The Achaia prefecture, in the north-eastern Peloponnese (Greece), close to the Gulf of Patras, is an area strongly affected by subsidence and landslides. Furthermore, this is an earthquake-prone area, a factor that can trigger some mass movements. For this region, a landslide inventory was realized with the help of the interpretation of Persistent Scatterers data, for the period 1992–2008, and high-resolution optical satellite images, available until 2016, in addition to the investigation of the landslide State of Activity. Moreover, for the coastal area, a section was investigated to evidence subsidence.

The accurate evaluation of landslide-induced damage is a necessity for planning of proper and effective mitigation measures. It requires the implementation of field investigations to identify structural failures to more effectively trace landslide boundaries. Many methods have been proposed to classify landslide-induced damage of buildings. The existing methods demonstrate several advantages and drawbacks depending on the parameters considered, as lack of some important features and difficulties in applicability. A new classification approach of landslide-induced damage of facilities is proposed, which specifically focuses on assessing of damage degree and its relationship to the ground motion intensity and impact severity. The new approach is designed in two steps: a chart utilized during surveys to quantify cracks on structures and ground surface; an a posteriori ranking of structures performed using a cell-grid matrix. Furthermore, a damage recording scheme useful for field surveying is proposed. This approach considers several parameters derived from different existing methodologies by smoothing out drawbacks and homogenizing the considered features. The resulting approach provides a new procedure of landslide-induced damage assessment adoptable in case of private dwellings, as it does not require internal accessibility, and it is exploitable for different landslide events and for different kinds of structures and facilities.

Multi-temporal Interferometric Synthetic Aperture Radar (MTInSAR) is a solid and reliable technique used to measure ground motion in many different environments. Today, the scientific community and a wide variety of users and stakeholders consider MTInSAR a precise tool for ground motion-related applications. The standard product of a MTInSAR analysis is a deformation map containing a high number of point-like measurement points (MP) which carry information on ground motion. The density of MPs is uneven, and they cannot be extracted continuously at large scale due to geometrical distortions and unfavourable landcover. It is a good practice to assess the feasibility of the interferometric analysis ahead of data processing. This technical note proposes a ready-to-use set of tools aimed at updating existing methods for modelling the effects of local topography and land cover on MTInSAR approaches. The goal of the tools is to provide InSAR experts and non-experts with a fast and automatic way to derive visibility maps, useful for pre-processing screening of a target area, and to forecast the expected density of MP over a specified area. Moreover, the visibility maps are a valid support for users to better understand the available standard and advanced interferometric results. Two workflows are proposed: the first generates the so-called Rindex map (Ri_m) to estimate the influence of topography on MP detection, the second is used to derive a land cover-calibrated Ri_m seen as a probabilistic model for MP detection (MPD_m). The proposed set of tools was applied in the context of the Alpine arc, whose climatic, morphological, and land cover characteristics represent a challenging environment for any interferometric approach.

The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach.

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.

Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.

The use of InSAR (Interferometric Synthetic Aperture Radar) products has greatly increased in the last years because of the technological advances in terms of both acquisition sensors and processing algorithms. The development of multi-interferogram techniques and the availability of free SAR analysis tools has significantly increased the number of worldwide applications of satellite measurements for mapping and monitoring geohazards. InSAR techniques excel in determining ground deformation in urban areas, where the coherence of the radar images is high, and the obtainable results are particularly reliable. Thus, measuring urban subsidence has always been one of the main targets of the InSAR analysis. In this paper, we present a brief review on the applications, in the last decades, of both single and multi-interferogram techniques to monitor ground lowering in urban areas along the Italian Peninsula. Because of its geological context, Italy is prone to slow natural subsidence phenomena sometimes aggravated and accelerated, especially along the coasts and in urbanized areas, by anthropogenic factors (i.e., groundwater overexploitation, consolidation in recent urban expansion, geothermal activities). The review will show how the interferometric data allowed the scientific community to increase the knowledge of the phenomena, map their spatial distribution, and reconstruct their temporal evolution. The final goal of the review is to demonstrate the added value of InSAR data in supporting groundwater management and urban development in Italy.

Synthetic Aperture Radar Interferometry (InSAR) techniques have been long exploited for detecting and mapping slow-moving ground surface displacements due to their millimeter accuracy, non-invasiveness and wide area coverage. A review on different applications of Persistent Scatterers InSAR approaches, proposed and applied over Tuscany region (Italy) across time, is here presented. The study area is characterized by both subsidence of alluvial plains and landslides on hilly and mountainous reliefs. Tuscany has a leading role in Italy in the field of interferometric applications: the first InSAR analyses, which date back to 2003, were performed at local basin scale, by exploiting various PSI-based approaches for risk mapping. The first InSAR applications at regional scale date back to 2009, relying on historical SAR archives of ERS and ENVISAT satellites for updating subsidence and landslide inventory maps at a certain temporal date. Nowadays, the availability of Sentinel-1 SAR data with a regular and systematic 6-days acquisitions plan, allows near-real time monitoring of deformative scenario at regional scale rather than solely mapping of geo-hydrological phenomena. Most recent innovative InSAR applications over Tuscany region scan the territory, exploiting the regular repeat pass of Sentinel-1, and promptly highlight the sites affected by the highest ground movements with high temporal frequency. Such approaches permit us to pass from a static ‘picture’ of regional slope instability to a weekly updated ‘movie’ with improved detail, useful for civil protection practices. These last ongoing works significantly enhance the value of multi-temporal InSAR approaches for investigating and managing geo-hazards over the Region.

This study aimed at evaluating and mapping Ground Subsidence Susceptibility (GSS) in the Grosseto plain (Tuscany Region, Italy) by exploiting multi-temporal satellite InSAR data and by applying two parallel approaches; a bivariate statistical analysis (Frequency Ratio) and a mathematical probabilistic model (Fuzzy Logic operator). The Grosseto plain experienced subsidence and sinkholes due to natural causes in the past and it is still suffering slow-moving ground lowering. Five conditioning subsidence-related factors were selected and managed in a GIS environment through an overlay pixel-by-pixel analysis. Firstly, multi-temporal ground subsidence inventory maps were prepared in the study area by starting from two inventories referred to distinct temporal intervals (2003−2009 and 2014−2019) derived from Persistent Scatterers Interferometry (PSI) data of ENVISAT and SENTINEL-1 satellites. Then, the susceptibility modelling was performed through the Frequency Ratio (FR) and Fuzzy Logic (FL) approaches. These analyses led to slightly different scenarios which were compared and discussed. Results show that flat areas on alluvial and colluvial deposits with thick sedimentary cover (higher than 20 m) on the bedrock in the central and eastern sectors of the plain are the most susceptible to land subsidence. The obtained FR- and FL-based GSS maps were finally validated with a ROC (Receiver Operating Characteristic) analysis, in order to estimate the overall performance of the models. The AUC (Area Under Curve) values of ROC analysis of the FR model were higher than the ones of FL model, suggesting that the former is a better and more appropriate predictor for subsidence susceptibility analysis in the study area. In conclusion, GSS maps provided a qualitative overview of the subsidence scenarios and may be helpful to predict and preliminarily identify high-risk areas for environmental local authorities and decision makers in charge of land use planning in the study area. Finally, the presented methodologies to derive GSS maps are easily reproducible and could also be applied and tested in other test sites worldwide, in order to check the modeling performance in different environmental settings.

This work describes a new procedure aimed to semi-automatically identify clusters of active persistent scatterers and preliminarily associate them with different potential types of deformational processes over wide areas. This procedure consists of three main modules: (i) ADAfinder, aimed at the detection of Active Deformation Areas (ADA) using Persistent Scatterer Interferometry (PSI) data; (ii) LOS2HV, focused on the decomposition of Line Of Sight (LOS) displacements from ascending and descending PSI datasets into vertical and east-west components; iii) ADAclassifier, that semi-automatically categorizes each ADA into potential deformational processes using the outputs derived from (i) and (ii), as well as ancillary external information. The proposed procedure enables infrastructures management authorities to identify, classify, monitor and categorize the most critical deformations measured by PSI techniques in order to provide the capacity for implementing prevention and mitigation actions over wide areas against geological threats. Zeri, Campiglia Marittima−Suvereto and Abbadia San Salvatore (Tuscany, central Italy) are used as case studies for illustrating the developed methodology. Three PSI datasets derived from the Sentinel-1 constellation have been used, jointly with the geological map of Italy (scale 1:50,000), the updated Italian landslide and land subsidence maps (scale 1:25,000), a 25 m grid Digital Elevation Model, and a cadastral vector map (scale 1:5000). The application to these cases of the proposed workflow demonstrates its capability to quickly process wide areas in very short times and a high compatibility with Geographical Information System (GIS) environments for data visualization and representation. The derived products are of key interest for infrastructures and land management as well as decision-making at a regional scale.

Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data are delivered, passing from a static view of the territory to a continuous streaming of ground motion measurements from space. Tuscany Region is the first worldwide example of a regional scale monitoring system based on satellite interferometric data. The processing chain here exploited combines a multi-interferometric approach with a time-series data mining algorithm aimed at recognizing benchmarks with significant trend variations. The system is capable of detecting the temporal changes of a wide variety of phenomena such as slow-moving landslides and subsidence, producing a high amount of data to be interpreted in a short time. Bulletins and reports are derived to the hydrogeological risk management actors at regional scale. The final output of the project is a list of potentially hazardous and accelerating phenomena that are verified on site by field campaign by completing a sheet survey in order to qualitatively estimate the risk and to suggest short-term actions to be taken by local entities. Two case studies, one related to landslides and one to subsidence, are proposed to highlight the potential of the monitoring system to early detect anomalous ground changes. Both examples represent a successful implementation of satellite interferometric data as monitoring and risk management tools, raising the awareness of local and regional authorities to geohazards.

Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and mapped. The possibility to recognize, investigate, and monitor these phenomena play a key role to avoid further occurrences and consequences. The number of applications of Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) analysis for landslides monitoring and mapping greatly increased in the last decades thanks to the technological advances and the development of advanced processing algorithms. In this work, landslide-induced damage on structures recognized and classified by field survey and velocity of displacement re-projected along the steepest slope were combined in order to extract fragility curves for the hamlets of Patigno and Coloretta, in the Zeri municipality (Tuscany, northern Italy). Images using ERS1/2, ENVISAT, COSMO-SkyMed (CSK) and Sentinel-1 SAR (Synthetic Aperture Radar) were employed to investigate an approximate 25 years of deformation affecting both hamlets. Three field surveys were conducted for recognizing, identifying, and classifying the landslide-induced damage on structures and infrastructures. At the end, the damage probability maps were designed by means of the use of the fragility curves between Sentinel-1 velocities and recorded levels of damage. The results were conceived to be useful for the local authorities and civil protection authorities to improve the land managing and, more generally, for planning mitigation strategies.

Subsidence phenomena, as well as landslides and floods, are one of the main geohazards affecting the Tuscany region (central Italy). The monitoring of related ground deformations plays a key role in their management to avoid problems for buildings and infrastructure. In this scenario, Earth observation offers a better solution in terms of costs and benefits than traditional techniques (e.g., GNSS (Global Navigation Satellite System) or levelling networks), especially for wide area applications. In this work, the subsidence-related ground motions in the Firenze–Prato–Pistoia plain were back-investigated to track the evolution of displacement from 2003 to 2017 by means of multi-interferometric analysis of ENVISAT and Sentinel-1 imagery combined with GNSS data. The resulting vertical deformation velocities are aligned to the European Terrestrial Reference System 89 (ETRS89) datum and can be considered real velocity of displacement. The vertical ground deformation maps derived by ENVISAT and Sentinel-1 data, corrected with the GNSS, show how the area affected by subsidence for the period 2003–2010 and the period 2014–2017 evolved. The differences between the two datasets in terms of the extension and velocity values were analysed and then associated with the geological setting of the basin and external factors, e.g., new greenhouses and nurseries. This analysis allowed for reconstructing the evolution of the subsidence for the area of interest showing an increment of ground deformation in the historic centre of Pistoia Town, a decrement of subsidence in the nursery area between Pistoia and Prato cities, and changes in the industrial sector close to Prato.