Integrated SDSS for environmental risk analysis in sustainable coastal area planning

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Abstract

The work deals with the development and implementation of a Spatial Decision Support System (SDSS) platform for coastal environmental risk analysis through the integration of multisource satellite data (Sentinel-1 and 2 and COSMOSkyMed) coupled with open source coastal hydrodynamic model addressed to flooding, erosion and pollution. The processing results allow us to cope with longshore pollutant dynamics connected to bathing use, to derive the shoreline changes and back-dune vegetation mapping, rocky coast movements detection as well as coastal area changes derived through advanced images segmentation techniques, multi-band change-detection and Persistent Scattered Interferometric Synthetic Aperture Radar technologies (PSInSAR). The SDSS provides cyclical production and updating in phase with satellite data acquisition frequency of the coastal scenarios for flooding risk analysis. All of these issues well enable operative products to be employed in the knowledge chain for sustainable coastal area planning activities. Moreover, self-consistent applicative tools, provided with proper graphical interface developed in IDL and integrated in SDSS, lead displaying and automatic extraction of the coastline sequence from Sentinel-1 data. Thus the comparison of two or more shorelines, even if multi-sources, provides the computation of coastal erosion and aggradation as well as the areas prone to coastal flooding. Finally, some interoperable tools for morpho-hydrodynamic modelling assimilation have been developed and implemented to reproduce flooding and pollution risk scenarios as well as coastal resilience assessment at different return time.

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Greco, M., Martino, G., Guariglia, A., Trivigno, L., Sansanelli, V., Losurdo, A., & Mussuto, G. (2018). Integrated SDSS for environmental risk analysis in sustainable coastal area planning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10964 LNCS, pp. 671–684). Springer Verlag. https://doi.org/10.1007/978-3-319-95174-4_51

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