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The 6-days repeatability of Sentinel-1 constellation allows building up an interferometric stack with unprecedented velocity. Easily updatable hot-spot analyses, frequently repeated following the update of Sentinel-1 images, represent very useful tools for MTInSAR (Multi-Temporal Interferometric Synthetic Aperture Radar) data analysis. Mountain regions are a challenging environment for interferometric analyses because of their climatic, morphological and land cover characteristics. In this context, MTInSAR data can retrieve reliable information over wide areas, with high cost-benefits ratio and where the installation of ground-based devices is not feasible. Considering the well-known limitations of interferometric techniques (such as fast motions or temporal and spatial decorrelation), hot-spot analyses are a viable solution for semi-automatic ground movements extraction over mountain regions. In this work, we present an example of a hot-spot analysis applied to a large stack of MTInSAR products generated by means of SqueeSAR technique over an alpine region (Valle d'Aosta, north-western Italy). The obtained outputs allow detecting 277 moving areas connected to landslides and mass wasting processes in general. These products are intended to be implemented in the landslide risk management chain of the region, focusing on landslide state of activity definition and landslide mapping. [ABSTRACT FROM AUTHOR]

The results of the continuous monitoring of ground deformation throughout the Tuscany region using radar images acquired by the Sentinel-1 satellite constellation of the European Space Agency (ESA) are presented here. This new monitoring approach, based on systematic imagery processing and analysis of deformation time series, is discussed at regional (for the entire Tuscany) and at local scale in the context of a case study of the Carpineta landslide, which is a large, active earth slide in the Northern Apennines (Pistoia province). The landslide registered an acceleration during the winter and spring of 2018 as a direct consequence of rainfall and snow melt. The increase in the deformation rate of the landslide, which led to the damage of several buildings, was promptly detected and monitored due to the enhanced temporal repetitiveness offered by the Sentinel-1 constellation. The results demonstrate that advances in satellite sensors, increases in computing capacity and the refinement of processing approaches and data screening tools can contribute to the development of new paradigms in satellite-based monitoring systems. Sentinel-1 data, which are systematically acquired with short revisiting times and then promptly processed, can now be used as a tool for the systematic tracking of ground deformation at the regional scale and for the continuous monitoring of slow and very slow landslides. [ABSTRACT FROM AUTHOR]

Landslides are widespread natural hazards that generate considerable damage and economic losses worldwide. Detecting terrain movements caused by these phenomena and characterizing affected urban areas is critical to reduce their impact. Here we present a fast and simple methodology to create maps of vulnerable buildings affected by slow-moving landslides, based on two parameters: (1) the deformation rate associated to each building, measured from Sentinel-1 SAR data, and (2) the building damage generated by the landslide movement and recorded during a field campaign. We apply this method to Arcos de la Frontera, a monumental town in South Spain affected by a slow-moving landslide that has caused severe damage to buildings, forcing the evacuation of some of them. Our results show that maximum deformation rates of 4 cm/year in the line-of-sight (LOS) of the satellite, affects La Verbena, a newly-developed area, and displacements are mostly horizontal, as expected for a planar-landslide. Our building damage assessment reveals that most of the building blocks in La Verbena present moderate to severe damages. According to our vulnerability scale, 93% of the building blocks analysed present high vulnerability and, thus, should be the focus of more in-depth local studies to evaluate the serviceability of buildings, prior to adopting the necessary mitigation measures to reduce or cope with the negative consequences of this landslide. This methodology can be applied to slow-moving landslides worldwide thanks to the global availability of Sentinel-1 SAR data. [ABSTRACT FROM AUTHOR]

Underground mining is one of the human activities with the highest impact in terms of induced ground motion. The excavation of the mining levels creates pillars, rooms and cavities that can evolve in chimney collapses and sinkholes. This is a major threat where the mining activity is carried out in an urban context. Thus, there is a clear need for tools and instruments able to precisely quantify mining-induced deformation. Topographic measurements certainly offer very high spatial accuracy and temporal repeatability, but they lack in spatial distribution of measurement points. In the past decades, Multi-Temporal Satellite Interferometry (MTInSAR) has become one of the most reliable techniques for monitoring ground motion, including mining-induced deformation. Although with well-known limitations when high deformation rates and frequently changing land surfaces are involved, MTInSAR has been exploited to evaluate the surface motion in several mining area worldwide. In this paper, a detailed scale MTInSAR approach was designed to characterize ground deformation in the salt solution mining area of Saline di Volterra (Tuscany Region, central Italy). This mining activity has a relevant environmental impact, depleting the water resource and inducing ground motion; sinkholes are a common consequence. The MTInSAR processing approach is based on the direct integration of interferograms derived from Sentinel-1 images and on the phase splitting between low (LF) and high (HF) frequency components. Phase unwrapping is performed for the LF and HF components on a set of points selected through a "triplets closure" method. The final deformation map is derived by combining again the components to avoid error accumulation and by applying a classical atmospheric phase filtering to remove the remaining low frequency signal. The results obtained reveal the presence of several subsidence bowls, sometimes corresponding to sinkholes formed in the recent past. Very high deformation rates, up to −250 mm/yr, and time series with clear trend changes are registered. In addition, the spatial and temporal distribution of velocities and time series is analyzed, with a focus on the correlation with sinkhole occurrence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Satellite interferometric data as tools for landslide intensity estimation. • Intensity as input for landslide potential loss calculation. • Regional scale approach fully relying on interferometric data. • Combination of interferometric data and gravitational process models. Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR) data offer a valuable support to landslide mapping and to landslide activity estimation in mountain environments, where in situ measures are sometimes difficult to gather. Nowadays, the interferometric approach is more and more used for wide-areas analysis, providing useful information for risk management actors but at the same time requiring a lot of efforts to correctly interpret what satellite data are telling us. In this context, hot-spot-like analyses that select and highlight the fastest moving areas in a region of interest, are a good operative solution for reducing the time needed to inspect a whole interferometric dataset composed by thousands or millions of points. In this work, we go beyond the concept of MTInSAR data as simple mapping tools by proposing an approach whose final goal is the quantification of the potential loss experienced by an element at risk hit by a potential landslide. To do so, it is mandatory to evaluate landslide intensity. Here, we estimate intensity using Active Deformation Areas (ADA) extracted from Sentinel-1 MTInSAR data. Depending on the localization of each ADA with respect to the urban areas, intensity is derived in two different ways. Once exposure and vulnerability of the elements at risk are estimated, the potential loss due to a landslide of a given intensity is calculated. We tested our methodology in the Eastern Valle d'Aosta (north-western Italy), along four lateral valleys of the Dora Baltea Valley. This territory is characterized by steep slopes and by numerous active and dormant landslides. The goal of this work is to develop a regional scale methodology based on satellite radar interferometry to assess the potential impact of landslides on the urban fabric. [ABSTRACT FROM AUTHOR]

• This study presents the analysis of the ground deformation measured using 26 years of satellite SAR data. • We investigated the driven factors of subsidence in a densely urbanized area. • Data retrieved by several boreholes and geotechnical investigations were used to model the building load and to validate remote sensing data. • The comparison between the consolidation curve and the time series suggests that SAR data can be used to evaluate the consolidation process. This paper shows the results of the comparison between Multi-temporal Synthetic Aperture Radar (MTInSAR) products derived from different sensors (C-band ERS 1/2, Envisat, Sentinel-1 and X-band COSMO-SkyMed) and geotechnical data to investigate the driving factors of subsidence which affect a freight terminal located along the a coastal plain of Tuscany (central Italy). MTInSAR data have been acquired in a very long period, between 1992 and 2018 and were analyzed in terms of subsidence rates and deformation time series at building scale. The obtained results show that the oldest buildings are still affected by a deformation rate close to −5 mm/yr, whereas recent buildings register rates around −40 mm/yr. Time series of deformation suggest that the deformation rates decrease over time following time-dependent trend that approximates the typical consolidation curve for compressible soils. The geotechnical and stratigraphical analysis of the subsurface data (boreholes, cone penetration tests and dilatometer tests) highlights the presence of a 15 m thick layer formed of clay characterized by poor geotechnical characteristics. The comparison among InSAR data, subsurface geological framework and geotechnical reconstruction suggests a possible evaluation of the timing of the primary and secondary consolidation processes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In many landslide studies, the possibility to predict future behaviour is still a major concern. The most popular methods to achieve this prediction rely on the analysis of monitoring data of displacement for the detection of accelerating areas, as they can directly related to precursory movements that typically precede collapse. Methods deducing reliable failure predictions have been largely applied at local scale, where in situ monitoring systems can be installed. So far, the lack of systematic information on ground displacement acquired at regional scale was a serious limit hampering the application of failure prediction methods at wide scale. Such limitations can be partially solved through the exploitation of spaceborne platforms. Despite this, the low data sampling frequency of most of the satellite systems hampered the possibility to retrieve the necessary details of tertiary creep characterized by accelerating deformation.The launch of Sentinel-1 mission opened a new opportunity for InSAR monitoring applications thanks to the increased acquisition frequency, the regularity of acquisitions and the policy on data access. The potential of the Sentinel-1 for the detection of pre-failure accelerating creep has been recently proved through some retrospective InSAR analysis. For instance, the post-event application of the Fukuzono method to the Sentinel-1 deformation time series of the catastrophic Maoxian landslide (China), occurred on 24 June 2017, pointed out that an accurate estimation of the time of collapse was already possible at the begin of June.Despite the operational readiness of the Sentinel-1 constellation, most of the applications so far have been aimed at assessing the use of this data source to spot unstable areas or to capture pre-failure signs after major collapse, rather than providing new streamlines of information for monitoring solutions. We provide an example of the potential of multi-temporal InSAR analyses applied to Sentinel-1 data for continuous monitoring of ground deformation induced by hydrogeological processes. We exploit Sentinel-1 images to implement an operational service based on advanced interferometric products suitable for risk mitigation at the regional scale. This service relies on the systematic processing of Sentinel-1 images to create continuously updated ground deformation data. In fact, as soon as a new acquisition is available, the new image is used to update all displacement time series of all measurement points identified in the area of interest. Time series are then automatically analysed through a post-processing procedure, highlighting any anomalous trends and/or acceleration affecting the area of interest and providing possible alerts.We present and discuss this monitoring system through the case study of the Tuscany and Valle d'Aosta Region (Italy), specifically selected due to their peculiar geological settings, highly prone to slope instability phenomena. These results show that satellite radar data, systematically acquired over large areas with short revisiting time, could be used not only for mapping unstable areas, but also for actual landslide warning, at least for some typologies of sliding phenomena. This means that the transition from historical analysis of ground deformation to a continuous monitoring with prediction capabilities at regional scale using satellite radar data is now possible. [ABSTRACT FROM AUTHOR]

The potential of satellite-based interferometry for landslides activity definition is well known. Many applications have demonstrated that this technique represents a strong supporting tool for landslide risk assessment and monitoring, at both local and regional scale. The ESA' Sentinel-1 (S1) constellation acquires with high revisit-time in a conflict-free operational mode, timely distributing data without costs or restrictions. All these characteristics allow relying upon S1 service for a long-term operational and sustainable use, as a support of both disaster risk reduction and response. For this reason, more and more research projects are based on the use of S1 in geohazards monitoring and early warning. The aim of this work is to present the results achieved in the framework of the European Project (ECHO) U-Geohaz regarding landslides monitoring and early warning supporting tools based on S1. The study area of the project is the Valle d'Aosta region (VDA) that is widely affected by landslide process of different size and typology. Specifically, the Deep-Seated Gravitational Slope Deformations (DSGSDs) are the most representative in terms of area coverage. The study of the state of activity and evolution of these phenomena is very important because DSGSDs affect entire valley flanks and several villages have been built on them or nearby. The use of S1-based interferometry can potentially give a near-real time information on their activity over the whole region supporting decision makers and early warning systems. At the same time, VDA presents challenging characteristics for what concerns the radar response and thus the obtainable results. The main limiting factor is the snow coverage, which is also strongly related to the topography. The topographic relief is highly variable, ranging from 300 m a.s.l. to peaks higher than 4000 m a.s.l. with steep slopes. The regional climate is characterized by wide range of temperatures varying a lot from the mountainous zone to the bottom of the valleys, implying also a strong variation in snow precipitations. This strong variation, in space and time, affects the DInSAR results in terms of coherence and spatial coverage. In this context, a strong effort has been done to develop a processing approach to improve the spatial and temporal sampling, without losing the quality of the results. Moreover, a method that extracts the most significant Active Deformation Areas (ADA) in a semi-automatic way has been adapted and applied. The main results will be exposed. [ABSTRACT FROM AUTHOR]

A relevant change in the use of SAR interferometry for risk management and reduction has been marked by the launch of the S-1 A and S-1 B, in 2014 and 2016 respectively, which ensure a SAR acquisition every 6 days above the entire Earth and make images available few hours after the acquisition without costs and limitations. S-1 represents a reliable and low cost tool for remote monitoring at both local and national-scale, which can potentially be exploited by administrative entities and Civil Protection authorities involved in risk management activities. "U-Geohaz, Geohazard impact assessment for urban areas" is a two-year project, started the 1st of January 2018, co-funded by the European Commission, Directorate-General Humanitarian Aid and Civil Protection (ECHO). The main aim of the project is to develop a methodology based on the SAR images acquired by Sentinel-1 (S-1) constellation to assess continuously the potential impact of geohazards on urban areas and critical infrastructures. Starting from the experience and the results obtained in the previous ECHO project SAFETY (2016-2017), the aim is to make a step forward, from periodically updated maps to a near-real time geohazards mapping and monitoring. U-Geohaz will provide tools to exploit the 6-days repeatability of S1 to support early warning systems for landslide and volcanic geohazards and to evaluate the expected damage. The consortium of U-Geohaz is composed of 18 partners, from 11 European countries, including 12 Geological Surveys and 3 Civil Protection Authorities that will support the implementation of tools to be operationally useful in risk management. The use of all the developed products will be implemented in the activities of the Civil Protection Authorities involved in the project. All the implemented procedure and tools are tested and will be demonstrated on the real test scenarios of Canary Island (Gran Canaria, Tenerife and El Hierro islands, Spain) and Valle d'Aosta (Italy). The main results achieved over the two test-sites and the future steps of the project will be presented and discussed. [ABSTRACT FROM AUTHOR]