The first results of the regional Operative Project (OP) “Implementation of hydraulic risk forecast and prevention system”, as part of the extended operative programme MATER aimed to develop a methodology for environmental and territorial analysis related to the exploitation of natural resources, are shown in the present paper. The main target deals with the development and implementation of a Spatial Decision Support System (SDSS) platform for coastal risk forecasting and prevention through the integration of multisource satellite data (Sentinel-1, Sentinel-2 and COSMO Sky Med) coupled with post processing open source hydrodynamic models. The processing results include the coastline and back-dune vegetation mapping, rocky coast movements detection as well as new landfills, buildings and spills derived by the implementation of innovative images segmentation techniques, multi-band change-detection and PSInSAR (Persistent Scattered Interferometric Synthetic Aperture Radar) typologies. The SDSS provides significant advantage of cyclical production and/or updating in phase with satellite data acquisition frequency. Further, through self-consistent applicative tools, provided with proper graphical interface developed in IDL and integrated in SDSS, it is possible to display and automatically extract the coastline sequence from Sentinel-1 data, compare two shoreline acquisitions, even if multi-sources, and calculate the coastal erosion and aggradation. Finally, some interoperable tools for morpho-hydrodynamic modelling assimilation have been developed and implemented in order to reproduce flooding risk scenarios for the coastal resilience assessment at different return time. For such a purpose, in the start-up phase, Delft3D (Deltares- NL) was employed for storm surge modelling, coastal morphological evolution and coastal inundation analyses. The SDSS is interconnected with a friendly and intuitive WebGIS compatible to the Regional Spatial Data Infrastructure.

Development of an Integrated SDSS for Coastal Risks Monitoring and Assessment

Martino Giovanni
Investigation
;
2017-01-01

Abstract

The first results of the regional Operative Project (OP) “Implementation of hydraulic risk forecast and prevention system”, as part of the extended operative programme MATER aimed to develop a methodology for environmental and territorial analysis related to the exploitation of natural resources, are shown in the present paper. The main target deals with the development and implementation of a Spatial Decision Support System (SDSS) platform for coastal risk forecasting and prevention through the integration of multisource satellite data (Sentinel-1, Sentinel-2 and COSMO Sky Med) coupled with post processing open source hydrodynamic models. The processing results include the coastline and back-dune vegetation mapping, rocky coast movements detection as well as new landfills, buildings and spills derived by the implementation of innovative images segmentation techniques, multi-band change-detection and PSInSAR (Persistent Scattered Interferometric Synthetic Aperture Radar) typologies. The SDSS provides significant advantage of cyclical production and/or updating in phase with satellite data acquisition frequency. Further, through self-consistent applicative tools, provided with proper graphical interface developed in IDL and integrated in SDSS, it is possible to display and automatically extract the coastline sequence from Sentinel-1 data, compare two shoreline acquisitions, even if multi-sources, and calculate the coastal erosion and aggradation. Finally, some interoperable tools for morpho-hydrodynamic modelling assimilation have been developed and implemented in order to reproduce flooding risk scenarios for the coastal resilience assessment at different return time. For such a purpose, in the start-up phase, Delft3D (Deltares- NL) was employed for storm surge modelling, coastal morphological evolution and coastal inundation analyses. The SDSS is interconnected with a friendly and intuitive WebGIS compatible to the Regional Spatial Data Infrastructure.
2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/131337
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