A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment

The traditional approach for coastal monitoring consists in ground investigations that are burdensome both in terms of logistics and costs, on a national or even regional scale. Earth Observation (EO) techniques can represent a cost-effective alternative for a wide scale coastal monitoring. Thanks t...

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Bibliographic Details
Main Authors: Maria Francesca Bruno, Matteo Gianluca Molfetta, Luigi Pratola, Michele Mossa, Raffaele Nutricato, Alberto Morea, Davide Oscar Nitti, Maria Teresa Chiaradia
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/6/1399
Description
Summary:The traditional approach for coastal monitoring consists in ground investigations that are burdensome both in terms of logistics and costs, on a national or even regional scale. Earth Observation (EO) techniques can represent a cost-effective alternative for a wide scale coastal monitoring. Thanks to the all-weather day/night radar imaging capability and to the nationwide acquisition plan named MapItaly, devised by the Italian Space Agency and active since 2010, COSMO-SkyMed (CSK) constellation is able to provide X-band images covering the Italian territory. However, any remote sensing approach must be accurately calibrated and corrected taking into account the marine conditions. Therefore, in situ data are essential for proper EO data selection, geocoding, tidal corrections and validation of EO products. A combined semi-automatic technique for coastal risk assessment and monitoring, named COSMO-Beach, is presented here, integrating ground truths with EO data, as well as its application on two different test sites in Apulia Region (South Italy). The research has shown that CSK data for coastal monitoring ensure a shoreline detection accuracy lower than image pixel resolution, and also providing several advantages: low-cost data, a short revisit period, operational continuity and a low computational time.
ISSN:1424-8220