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 (Mw 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 Mw ~ 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 Mw 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.

Within the National Environmental Satellite, Data, and Information Service, National Oceanic and Atmospheric Ad- ministration, multiplatform synthetic aperture radar (SAR) im- agery is being used to aid posthurricane and postaccident response efforts in the Gulf of Mexico, such as in the case of the recent Deepwater Horizon oil spill. The main areas of interest related to such disasters are the following: 1) to identify oil pipeline leaks and other oil spills at sea and 2) to detect man-made metallic targets over the sea. Within the context of disaster monitoring and response, an innovative processing chain is proposed to observe oil fields (i.e., oil spills and man-made metallic targets) using both L- and C-band full-resolution and fully polarimetric SAR data. The processing chain consists of two steps. The first one, based on the standard deviation of the phase difference between the copolarized channels, allows oil monitoring. The second one, based on the different symmetry properties that characterize man- made metallic targets and natural distributed ones, allows man- made metallic target observation. Experiments, accomplished over single-look complex L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and C-band RADARSAT-2 fully polarimet- ric SAR data gathered in the Gulf of Mexico and related to the Deepwater Horizon accident, show the effectiveness of the proposed approach. Furthermore, the proposed approach, being able to process both L- and C-band fully polarimetric and full- resolution SAR measurements, can take full benefit of both the ALOS PALSAR and RADARSAT-2 missions, and therefore, it allows enhancing the revisit time and coverage which are very critical issues in oil field observation

In this study, a sea/oil contrast model, based on the two-scale sea surface scattering Boundary Perturbation Model and an improved Marangoni damping model, is exploited to predict the X-band contrast due to an oil slick. Theoretical predictions are then compared with actual X-band synthetic aperture radar (SAR) measurements collected by COSMO-SkyMed and TerraSAR-X satellites over the polluted area off the Aberdeen coast (United Kingdom) during the Gannet Alpha oil spillage occurred in 2011. The contrast model is here verified at X-band for the first time and exploited in a very challenging scenario, i.e., when an oil slick is in place. In addition, a detailed analysis on the effect of sensor's noise equivalent sigma zero (NESZ) on the predicted and measured contrast is undertaken. Experimental results confirm model predictions, witnessing a remarkable agreement between predicted and measured contrasts. Moreover, they demonstrate that NESZ significantly affects the information content of the signal backscattered off the oil-covered area

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Abstract: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.