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The application of remote sensing methods provides useful information that can be used for numerous research. Thus, spatial changes in soil, vegetation, hydrography and such can be analyzed. By analyzing the data obtained by remote sensing methods, high-quality and important data can be obtained for monitoring changes in soil movement caused by landslides. This method provides the possibility of determining the state of the observed space over a longer period of time. Historical aerial imagery has a high level of spatial detail analysis. Comparative analysis of the aerial imagery from the past, recent ones and other surveys can certainly provide information on the trend of ground movement, as well as lead to conclusions for taking specific measures. The present paper gives an example of the analysis of the particular area of the “Umka” landslide based on historical surveys. The “Umka” landslide is located along the right bank of the Sava River near the city of Belgrade, which, with its long-term activity, jeopardizes residential buildings, infrastructure facilities and the population that still lives on it.

Dam monitoring represents a crucial issue in order to avoid catastrophic failures due to infrastructure aging or earthquake damages. Differential SAR Interferometry (DInSAR) is a technique suitable for critical infrastructure monitoring, also for the availability of free data and tools, that can be used by experts in SAR remote sensing and also by geologists and civil engineers, after having acquired the right confidence and experience in these data processing and tool use. In order to apply the DInSAR technique, in its basic and simple version, to critical infrastructure monitoring, it is very important to assess its performance. Nevertheless, validation results are not largely available in literature, because heterogeneous technical competencies are required to this aim and in situ measurements must be collected and made available. In this paper, we propose a highly reproducible DInSAR workflow that can be effectively used for dam monitoring, by validating its results with in situ measurements on some significant case studies in Italy.

Landslides are frequent events that may cause human casualties and injuries as well as damage to urban and man-made structures, with extensive loss of economic resources. For this reason, landslide mapping is a primary tool for hazard and risk assessment. Italian Ministry of Environment, thanks to great availability and functionality of Synthetic Aperture Radar (SAR) data promoted the Not-ordinary Plan of Environmental Remote Sensing (Piano Straordinario di Telerilevamento Ambientale, PST-A in Italian) in 2008, as to constitute a national database of active or potential instability phenomena affecting the Italian territory, based on the exploitation of interferometric products (ERS and ENVISAT). In this paper, the PST-A-3 is described. A procedure based on the integration of engineering-geological approaches and SAR interferometry data belonging to COSMO-SkyMed constellation (100 frames 40 × 40 km) has been here implemented over 7,400 km 2 of the Italian territory. First, landslides have been mapped by field geologists, defining type and state of activity. Simultaneously to field surveys, remote sensing data have been analyzed as to detect areas with considerable displacement registered by the satellite. Both products have been overlaid, also quantifying the coincidence between the events reported according to the two detection methodologies and subtracting those landslide not recordable by the satellite, finally obtaining an updated landslide inventory map with 4,522 newly detected phenomena. Therefore, PST-A-3 proves to be a valuable system for local authorities, in order to provide a contribution to risk management but also for the forecasting of landslide events, as testified by two case studies selected. Thanks to the PST-A experience, the use of such strategy to other countries could represent a valid contribution to land management at worldwide scale.

Kinematic characterization of a landslide at large, small, and detailed scale is today still rare and challenging, especially for long periods, due to the difficulty in implementing demanding ground surveys with adequate spatiotemporal coverage. In this work, the suitability of space-borne synthetic aperture radar sub-pixel offset tracking for the long-term monitoring of the Slumgullion landslide in Colorado (US) is investigated. This landslide is classified as a debris slide and has so far been monitored through ground surveys and, more recently, airborne remote sensing, while satellite images are scarcely exploited. The peculiarity of this landslide is that it is subject to displacements of several meters per year. Therefore, it cannot be monitored with traditional synthetic aperture radar differential interferometry, as this technique has limitations related to the loss of interferometric coherence and to the maximum observable displacement gradient/rate. In order to overcome these limitations, space-borne synthetic aperture radar sub-pixel offset tracking is applied to pairs of images acquired with a time span of one year between August 2011 and August 2013. The obtained results are compared with those available in the literature, both at landslide scale, retrieved through field surveys, and at point scale, using airborne synthetic aperture radar imaging and GPS. The comparison showed full congruence with the past literature. A consistency check covering the full observation period is also implemented to confirm the reliability of the technique, which results in a cheap and effective methodology for the long-term monitoring of large landslide-induced movements.

The paper shows an innovative methodology which can be used to forecast the spatial and temporal evolution of the buildings’ structural vulnerability in urban settlements affected by slow-moving intermittent landslides. Multi-level and integrated analysis, of conventional and innovative monitoring techniques with structural analysis methods for damage assessment allows to estimate, at different levels of accuracy, future conditions of damage and local failures of single buildings and building aggregates. To this purpose, in the last decade, Satellite Radar Interferometry and in particular the Differential SAR Interferometry (DInSAR) technique has been successfully applied as a remote-sensing tool in several analyses aimed at prediction and mitigation of buildings’ damage induced by slow-moving landslides. The proposed methodology has been here applied to the urban settlement of Moio della Civitella (Salerno province, Italy).

The Emilia Romagna Region (N-E Italy) and in particular the Adriatic Sea coastline of Ravenna, is affected by a noticeable subsidence that started in the 1950s, when the exploitation of on and off-shore methane reservoirs began, along with the pumping of groundwater for industrial uses. In such area the current subsidence rate, even if lower than in the past, reaches the -2 cm/y. Over the years, local Authorities have monitored this phenomenon with different techniques: spirit levelling, GPS surveys and, more recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, confirming the critical situation of land subsidence risk. In this work, we present the comparison between the results obtained with DInSAR and GPS techniques applied to the study of the land subsidence in the Ravenna territory. With regard to the DInSAR, the Small Baseline Subset (SBAS) and the Coherent Pixel Technique (CPT) techniques have been used. Different SAR datasets have been exploited: ERS-1/2, ENVISAT, TerraSAR-X and Sentinel-1. Some GPS campaigns have been also carried out in a subsidence prone area. 3D vertices have been selected very close to existing persistent scatterers in order to link the GPS measurement results to the SAR ones. GPS data were processed into the International reference system and the comparisons between the coordinates, for the first 6 months of the monitoring, provided results with the same trend of the DInSAR data, even if inside the precision of the method.

Surface movements and landslides monitoring with satellite-based DInSAR techniques is becoming more and more widespread among research groups and scientific community. The SAR Differential Interferometry (DInSAR) is one of the most powerful devices for monitoring deformation processes on the Earth surface. Here, a dataset of TerraSAR-X StripMap imagery covering almost the whole Crotone province territory, located in the south of the Italian peninsula, has been selected. The time span goes from April 2008 to June 2010 and from August 2013 to October 2014. Crotone province is severely affected by landslide phenomena, due to the geological and geomorphological context, as well as to the physical and mechanical properties of the involved materials. According to an accurate bibliographic and newspaper research, in agreement with field surveys, as to find the most suitable cases for the use of the satellite interferometry, different case studies have been selected. Hence, the attention has been concentrated on several urban settlements. The most important are Papanice, Cutro, Santa Severina, San Mauro Marchesato, Ciro, Crucoli, in which, several landslide evidences, affecting roads and buildings, have been recognized. Particular attention has been focused on slow and intermittent landslides, highly detectable by DInSAR techniques. In this work, two different multitemporal interferometry (MTI – Wasowsky et al., 2014) approaches and two different software packages have been used and compared in order to identify benefits/constraint of each MTI approach and each software. Such approaches are: a) the “permanent” (or “persistent”, or “point-like”) scatterers (PS - Ferretti et al., 2001) and the “small baseline subset approach” (SBAS - Berardino et al., 2002; Lanari et al., 2004) implemented both in the SARscape software, developed by the SARMAP team, and on SUBSOFT processor, this last based on the Coherent Pixel Technique (CPT - Mora et al., 2003, Blanco et al., 2008), developed by the Remote Sensing Laboratory (RSLab) group, from the Universitat Politecnica de Catalunya (UPC). For every technique, displacement time series have been obtained and compared. The interferometric analysis has shown good results, being able to identify displacement rates up to 4 cm/year, and also allowing to redraw landslide boundaries, previously identified in the Hydrogeological Setting Plan of the Calabria region (2001), updating their state of activity, as well. Moreover, being the available dataset antecedent to the landslides’ reactivations, the adopted DInSAR techniques have been able to recognize precursor stages of slope failures, hence confirming the reliability of SAR methods as powerful monitoring and prediction tools.

Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013–October 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185 mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30–40 mm/yr along the Line of Sight – LOS-of the satellite) with respect to the pre-failure phase (2008–2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.

This paper presents the application of Differential Synthetic Aperture Radar (SAR) interferometry (DInSAR) algorithms for the precise monitoring of earth dams ground deformation. The test site selected for this study is the Conza Dam, located in the southern Apennines (Italy), very close to the epicenter of the big earthquake (M-w = 6.9) which took place on November 23th, 1980, striking the Irpinia region. Among the multiple advanced DInSAR techniques developed by the SAR community during the last decade, this work uses the so-called SUBSOFT software, developed by the Remote Sensing Laboratory (RSLab) group from the Universitat Politecnica de Catalunya (UPC), which is based on the use of Coherent Pixels Technique (CPT) algorithm. The analysis is carried out using 51 ENVIronmental SATellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) images, corresponding to the period from 29th of November 2002 to the 30th of July 2010. In this framework, ground displacements recorded by a network of conventional ground-based sensors are also available for the same temporal span. Indeed, the embankment dam is well instrumented to measure internal settlements by means of cross-arms placed in six different cross-sections, and superficial displacements by means of targets for precise leveling. A statistical analysis has been performed to carry out a better comparison between the measurements obtained with the conventional field sensors and the interferometric data. The high agreement between final DInSAR displacements and in-situ instrumental data, demonstrates the reliability of such technique for the precise monitoring of civil infrastructures, and concretely, in dams with a high exposure factor and its consequent risk.