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This paper shows the results of a study aimed at quantitatively estimating—in terms of direct (repair) costs, at large scale (1:5000)—the slow-moving landslide risk to a road network assumed as undamaged as well as the consequences to the same network in damaged conditions. The newly conceived methodological approaches address some challenging tasks concerning (i) the hazard analysis, which is expressed in terms of probability of occurrence of slow-moving landslides with a given intensity level that, in turn, is established based on empirical fragility curves, and (ii) the consequence analysis, which brings to the generation of time-dependent vulnerability curves. Their applicability is successfully tested in a case study in the Campania region (southern Italy) for which both very high-resolution DInSAR data and information gathered from in situ surveys on the severity of damage sustained by the selected road sections are available. Benefits associated with the use of the obtained results in informed decision-making processes are finally discussed.

Persistent Scatterer Interferometry (PSI) data, deriving from the processing of SAR images acquired by high-resolution sensors such as TerraSAR-X, provide accurate measurements of displacements affecting structures (e.g., buildings) and linear infrastructure networks (e.g., roads, bridges, embankments, and pipelines). Such widespread displacements, when available on buildings on unstable slopes, offer new perspectives for their integration in procedures pursuing the analysis and the prediction of the physical vulnerability of exposed buildings. In this letter, both deterministic and probabilistic cause (differential settlements)-effects (damage) relationships are generated by using PSI-derived building settlements and the results of building damage surveys. The procedure is applied to El Papiol town (Spain), whose urban area has been suffering diffuse damage of different severity to buildings and roads due to extremely slow-moving landslide phenomena.

Buildings in subsiding areas may suffer from settlements causing damages of different severity levels with high impact in terms of yearly economic losses. In these contexts, a systematic damage assessment jointly with continuous monitoring of relevant parameters (e.g. settlements exhibited by points located on the roof) can be extremely useful to control the building behaviour and develop forecasting models. In this regard, the paper presents the results of an integrated analysis carried out on a subsidence-affected urban area in the Netherlands where the availability of multi-temporal building damage surveys and a long DInSAR monitoring dataset allowed both retrieving quantitative empirical relationships between the cause (magnitude of the selected intensity parameter, IP) and the effect (recorded damage severity level, DL) and generating empirical fragility and vulnerability curves. The results pointed out the importance of considering the exact dating of the onset of building damage and the corresponding magnitude of the considered IP in the generation of quantitative forecasting models.

Como historic centre, located at the SW branch of Lake Como (northern Italy), is prone to subside because of a thick sequence of late Pleistocene to Holocene glacio-lacustrine, palustrine and alluvial sediments in the subsoil. After the 1950s, the combination of natural causes and anthropogenic activities amplified subsidence-induced differential settlements at building foundation depths, resulting in damage on the superstructures. This work presents the first subsidence vulnerability analysis of the historic buildings in Como city centre by combining hydrogeological and stratigraphic features, in situ damage investigations, and remote sensing Synthetic Aperture Radar (SAR) data acquired by Cosmo-SkyMed mission. First, the relationships between local hydrogeological features and vertical displacements retrieved by SAR Interferometry (InSAR) analysis were qualitatively assessed. This highlighted that cumulative vertical InSAR-derived settlements have a stronger linear correlation with the groundwater level rather than the thickness of compressible soil units at the city scale. The largest vertical displacements are located in the NW sector of the city centre and along the shore of Lake Como, where they remark the pre-Roman shoreline. Then, the cause-effect relationships between building damage severity and Subsidence-Related Intensity (SRI) parameters were investigated using a probabilistic approach based on empirical fragility curves. To this aim, two InSAR-derived SRI parameters were tested for both masonry and reinforced concrete buildings: differential settlements and relative rotations. The former resulted to relate better to distinct damage levels in Como historic centre. The analyses performed can contribute to the management of the inestimable architectural and cultural heritage of Como historic centre.

Highly compressible clayey and peaty (soft) soils may expose structures (e.g. buildings) and infrastructures (e.g. roads and embankments) resting on them to absolute/differential settlements. These latter, with passing of time, can induce damages whose level of severity depends on several factors such as: the spatial distribution of the thickness pertaining to soft soil layers; the groundwater regime; the characteristics of the exposed structures and infrastructures along with their state of maintenance. The analysis of damages and the resulting management of the built-up environment usually require high costs due to the amount of data necessary for setting up reliable forecasting models as well as for defining the most suitable restoration works. This paper presents a multi-scale procedure tailored to analyze the settlement-induced building damage. The selected case study deals with an urban area in The Netherlands where, at medium scale, the role of soft soils in predisposing the occurrence of ground surface settlements is first investigated. Then, at large scale the relationship between cause (i.e. settlements) and effects (i.e. damage) is analyzed for building aggregates via the combination of high-resolution advanced differential interferometric synthetic aperture radar (DInSAR) and in-situ damage survey data. Finally, as main novelty of the proposed multi-scale procedure, both the above datasets are used to generate, at detailed scale, empirical fragility curves for single buildings that, once further validated, could be valuably adopted for damage forecasting purposes in similar urban areas.

Statistical analyses have been often used for landslide susceptibility zoning at small to medium scale when relevant base and thematic maps are available. Since the beginning of the last decade, images remotely acquired by spaceborne Synthetic Aperture Radar (SAR) and processed via Differential SAR Interferometry (DInSAR) proved extremely useful for non-invasive and non-destructive monitoring of displacements of the topographic surface. The present paper proposes an original procedure for the definition of the state of activity of slow-moving landslides via the combined use of multivariate statistical analyses and DInSAR data. The procedure is based on the following essential elements: distinction between terrain units used for computational purposes and the final zoning units; independent statistical and DInSAR analyses and activity models leading to first-level state of activity zoning maps; a consistency model between statistical and DInSAR analyses; two confidence and combination models leading, respectively, to second- or third-level state of activity zoning maps. The application in a test area including 19 municipalities in southern Italy, where slow-moving landslides are widespread and accurately mapped by using geomorphological criteria, allowed the generation of the three above-mentioned levels of zoning maps. The results were successfully crosschecked by exploiting a different DInSAR dataset and the results of previous works based on the use of slow-moving landslide-induced damage to facilities surveys.

Several urban areas in Euro-Mediterranean countries are affected by slow-moving landslides that, even if rarely associated with the loss of human life, can cause damage to structures and infrastructure. In such contexts, the progressive decay of the built environment can bring along a generalized increase of the physical vulnerability and, as a result, slow-moving landslide risk increases over the time. Under these conditions, as long as suitable risk mitigation measures are lacking, the level of risk (also related to earthquakes) could turn out to be no longer acceptable within an a priori unknown time interval. This problem has a relevant social-economic impact, thus requiring the adoption of risk mitigation strategies that need to be effective and, at the same time, sustainable for the involved stakeholders. In this regard, this paper proposes a multi-scale methodological approach—based on the joint use of satellite-derived displacement monitoring data and the results of building damage surveys—whose applicability is tested with reference to urban areas affected by slow-moving landslides in Calabria region (southern Italy).

The geometric and kinematic characterization of landslides affecting urban areas is a challenging goal that is routinely pursued via geological/geomorphological method and monitoring of ground displacements achieved by geotechnical and, more recently, advanced differential interferometric synthetic aperture radar (A-DInSAR) data. Although the integration of all the abovementioned methods should be planned a priori to be more effective, datasets resulting from the independent use of these different methods are commonly available, thus making crucial the need for their standardized a posteriori integration. In this regard, the present paper aims to provide a contribution by introducing a procedure that, taking into account the specific limits of geological/geomorphological analyses and deep/surface ground displacement monitoring via geotechnical and A-DInSAR data, allows the a posteriori integration of the results by exploiting their complementarity for landslide characterization. The approach was tested in the urban area of Lungro village (Calabria region, southern Italy), which is characterized by complex geological/ geomorphological settings, widespread landslides and peculiar urban fabric. In spite of the different level of information preliminarily available for each landslide as result of the independent use of the three methods, the implementation of the proposed procedure allowed a better understanding and typifying of the geometry and kinematics of 50 landslides. This provided part of the essential background for geotechnical landslide models to be used for slope stability analysis within landslide risk mitigation strategies.

This paper proposes a multi-sensor a priori PSI visibility map for Austria in order to evaluate the feasibility of Differential SAR Interferometric (DInSAR) applications for landslide-affected slopes. For this purpose, the range index RI, introduced for the determination of areas in layover and foreshortening on both ascending and descending acquisition geometries, is computed and applied to the most diffuse X-C-L band SAR sensors. A new method is introduced to improve the accuracy of those products by fusing CORINE data with sharper European JRC forest map and Imperviousness Copernicus map. The results are tested with six different available PSI datasets over Austria. Then, a priori visibility map and a PSI density map are also derived for seven different satellites by combining the RI index and an enhanced CORINE land cover map. Finally, PSI velocity values, along the Line of Sight (VLos) and projected along the steepest slope direction (VSlope), are used in order to produce a landslide velocity map for the Austrian region of Vorarlberg.

Slow-moving landslides yearly induce huge economic losses worldwide in terms of damage to facilities and interruption of human activities. Within the landslide risk management framework, the consequence analysis is a key step entailing procedures mainly based on identifying and quantifying the exposed elements, defining an intensity criterion and assessing the expected losses. This paper presents a two-scale (medium and large) procedure for vulnerability assessment of buildings located in areas affected by slow-moving landslides. Their intensity derives from Differential Interferometric Synthetic Aperture Radar (DInSAR) satellite data analysis, which in the last decade proved to be capable of providing cost-effective long-term displacement archives. The analyses carried out on two study areas of southern Italy (one per each of the addressed scales) lead to the generation, as an absolute novelty, of both empirical fragility and vulnerability curves for buildings in slow-moving landslide-affected areas. These curves, once further validated, can be valuably used as tools for consequence forecasting purposes and, more in general, for planning the most suitable slow-moving landslide risk mitigation strategies.

Secondary settlements in soft soils represent a significant fraction of the total settlement induced by external loads. Consequently, these settlements can play a key role in performance, serviceability, and safety of engineering works such as buildings, roads, embankments, and pipelines. This paper addresses the development of a predictive settlement model for a railway embankment built on soft clayey–peaty soils by following an original procedure consisting of three cascading steps: (i) preliminary detection of the most settlement-affected portions of the infrastructure; (ii) development of an equivalent subsoil model to study secondary settlements; (iii) back-calculation of the parameters of a predictive settlement model (design subsoil model) via a variational data assimilation scheme that exploits ground displacement measurements derived from differential interferometric synthetic aperture radar (DInSAR) data. The main achievement relies on the retrieval of a stochastic prediction of secondary settlements that can contribute to rationalize both conventional monitoring campaigns and management of key infrastructure.

Geological and geomorphological criteria complemented with geotechnical monitoring of ground displacements are routinely applied for geometric and kinematic characterization of landslides. However, these activities may be challenging in densely built-up historic centres. In order to tackle all constraints posed by the presence of complex urban fabric, this paper proposes a procedure for integrating the results of geological/geomorphological analyses with deep and surface ground displacement data acquired via geotechnical monitoring and spaceborne Differential Interferometric Synthetic Aperture Radar (DInSAR) data. The selected test site is a village in southern Italian Apennines characterized by complex geological/geomorphological settings, widespread distribution of landslides and a peculiar urban fabric. The results of the integration procedure show that it is possible to overcome specific limits and provide a better understanding and definition of the geometry and kinematics of slow-moving landslides to be used for slope stability analyses and landslide risk management in historic centres.

In this study Differential Interferometric Synthetic Aperture Radar (DInSAR) is used to investigate the effects of a subsidence phenomenon – related to groundwater withdrawal –on ancient low‐rise masonry buildings located in the urban area of Sarno, Italy. ERS‐1/2 SAR data from 1992 to 2002 were analysed and results were validated with geodetic data. The results encourage future developments for study and use of the multi‐temporal DInSAR technique in the mitigation of subsidence risk.

The paper aims at checking the contribution that DInSAR data, processed via different algorithms, can provide to the analysis of damages recorded to buildings located in slow-moving landslide affected areas. For this purpose, an urban area in Calabria region, southern Italy, was selected due to the availability of both DInSAR data since 1992 and historic information concerning damage data recorded via municipal ordinances. The combination of DInSAR data and the results of supplementary damage surveys allowed the preliminary investigation of a cause (maximum velocity)—effect (damage) relationship which, once validated, can be valuably used for damage analysis and forecasting.

This work shows a methodological approach for the joint use of geological and geomorphological studies and conventional/innovative monitoring data in densely urbanized areas at landslide risk. The methodology is applied to a test area in the Calabria region (southern Italy) extensively affected by several active landslides involving urban areas. These landslides have been studied and monitored via ground-based techniques for many years. In the study area the comparison and interpretation of DInSAR data with geomorphological studies and inclinometric monitoring has been carried out. The results obtained, thanks to the validation of remote sensed data via ground-truths, provide a further step towards the integrated use of DInSAR data within landslide risk mitigation strategies. © Springer-Verlag Berlin Heidelberg 2013.

Differential Interferometry Synthetic Aperture Radar is a mature remote sensing technique broadly used for the measurement of slow deformations with major application in several fields. In the last years, the application to slow moving landslides has grown quickly and several examples of the monitoring capability can be found in the literature. Main goal of this work is the description of the advances in terms of products that can be achieved from both an innovative methodological approach to DInSAR data analysis and the technological improvements related to the last generation of sensors.

The paper discusses the potential of a DInSAR (Differential Synthetic Aperture Radar Interferometry) technique for the study of subsidence phenomena over large areas. The validation tests of low resolution DInSAR data, spanning the period 1992-2001, were carried out at a "municipal scale" in the urban area of Sarno (Italy), affected by ground surface settlements ascribed to groundwater withdrawals. The analysis was then extended to the whole Campanian Plain, with the same stratigraphic and structural settings as Sarno area, in order to use the remote sensed data as a tool for detecting, at regional scale, the most critical areas in terms of rate of the recorded settlements. At least five highly urbanised areas were identified as "unstable", thus addressing further investigations to deepen the relationship between the causes and the effects of the detected phenomena.