We investigate a large geodetic data set of interferometric synthetic aperture radar (InSAR) and GPS measurements to determine the source parameters for the three main shocks of the 2016 Central Italy earthquake sequence on 24 August and 26 and 30 October (M_w 6.1, 5.9, and 6.5, respectively). Our preferred model is consistent with the activation of four main coseismic asperities belonging to the SW dipping normal fault system associated with the Mount Gorzano‐Mount Vettore‐Mount Bove alignment. Additional slip, equivalent to a M_w ~ 6.1–6.2 earthquake, on a secondary (1) NE dipping antithetic fault and/or (2) on a WNW dipping low‐angle fault in the hanging wall of the main system is required to better reproduce the complex deformation pattern associated with the greatest seismic event (the M_w 6.5 earthquake). The recognition of ancillary faults involved in the sequence suggests a complex interaction in the activated crustal volume between the main normal faults and the secondary structures and a partitioning of strain release.

We investigated the contribution of earthquake-induced surface movements to the ground displacements detected through Interferometric Synthetic Aperture Radar (InSAR) data, after the M_w 3.9 Ischia earthquake on 21 August 2017. A permanent displacement approach, based on the limit equilibrium method, allowed estimation of the spatial extent of the earthquake-induced landslides and the associated probability of failure. The proposed procedure identified critical areas potentially affected by slope movements partially overlapping the coseismic ground displacement retrieved by InSAR data. Therefore, the observed ground displacement field is the combination of both fault slip and surficial sliding caused by the seismic shaking. These findings highlight the need to perform preliminary calculations to account for the non-tectonic contributions to ground displacements before any estimation of the earthquake source geometry and kinematics. Such information is fundamental to avoid both the incorrect definition of the source geometry and the possible overestimation of the coseismic slip over the causative fault. Moreover, knowledge of the areas potentially affected by slope movements could contribute to better management of a seismic emergency, especially in areas exposed to high seismic and hydrogeological risks.

In this paper, we propose a combined use of real aperture radar (RAR) and synthetic aperture radar (SAR) sensors, within an interferometric processing chain, to provide a new methodology for monitoring urban environment and historical buildings at different temporal and spatial scales. In particular, ground-based RAR measurements are performed to estimate the vibration displacements and the natural oscillation frequencies of structures, with the aim of supporting the understanding of the building dynamic response. These measurements are then juxtaposed with ground-based and space-borne SAR data to monitor surface deformation phenomena, and hence, point out potential risks within an urban environment. In this framework, differential interferometric SAR algorithms are implemented to generate short-term (monthly) surface displacement and long-term (annual) mean surface displacement velocity maps at local (hundreds m ² ) and regional (tens km ² ) scale, respectively. The proposed methodology, developed among the activities carried out within the national project Programma Operativo Nazionale MASSIMO (Monitoraggio in Area Sismica di SIstemi MOnumentali), is tested and discussed for the ancient structure of Saint Augustine compound, located in the historical center of Cosenza (Italy) and representing a typical example of the Italian Cultural Heritage.

In this work, we use X and C-band SAR data provided by the COSMO-SkyMed and ENVISAT missions to detect and measure some ground deformation phenomena along six coastal areas of Italy. In particular, we exploit multi-temporal interferometric synthetic aperture radar (InSAR), i.e., small baseline subsets (SBAS) and interferometric point target analysis (IPTA) methods, to retrieve the deformation rate maps and time series for each investigated area. Multi-temporal InSAR analysis revealed local subsidence and uplifting effects in Ravenna Coastal Areas, Fiumicino, Campi Flegrei, Sibari Plain, Augusta Bay, and Taranto Gulf. Our work is meant as a demonstrator to show how InSAR-based analysis can provide a detailed understanding of the coastal hazards. Such analysis also opens up new monitoring scenarios such as the possibility of designing a near real-time surveillance service based on Sentinel-1 SAR data.

The knowledge of the topographic features, the building properties, and the road infrastructure settings are relevant operational tasks for managing post-crisis events, restoration activities, and for supporting search and rescue operations. Within such a framework, airborne remote sensing tools have demonstrated to be powerful instruments, whose joint use can provide meaningful analyses to support the risk assessment of urban environments. Based on this rationale, in this study, the operational benefits obtained by combining airborne LiDAR and hyperspectral measurements are shown. Terrain and surface digital models are gathered by using LiDAR data. Information about roads and roof materials are provided through the supervised classification of hyperspectral images. The objective is to combine such products within a geographic information system (GIS) providing value-added maps to be used for the seismic vulnerability assessment of urban environments. Experimental results are gathered for the city of Cosenza, Italy.

The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification. The availability of very high resolution images provided by last-generation satellite synthetic aperture radar (SAR) and optical sensors makes this analysis promising from an application perspective and simultaneously challenging from a processing viewpoint. Indeed, pursuing such a goal requires the development of novel methodologies able to exploit the large amount of detailed information provided by this type of data. To take advantage of the temporal and spatial information associated with such images, the proposed method integrates multisensor, multisource, and contextual information. Markov random field modeling is adopted here to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times. The performances of the proposed approach are evaluated on multisensor pairs of COSMO-SkyMed SAR and Pléiades optical images acquired over Jérémie, in the aftermath of and during the three years after Hurricane Matthew. The effectiveness of the change detection results is analyzed both quantitatively, through the computation of accuracy measures on a test set, and qualitatively, by visual inspection of the classification maps. The robustness of the proposed method with respect to different algorithmic choices is also assessed, and the detected changes are discussed in relation to the recovery endeavors in the area and ground-truth data collected in the field in April 2019.