Publication: Innovative remote sensing methodologies and applications in coastal and marine environments
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Publisher
TAYLOR & FRANCIS LTD
DOI
10.1080/10095020.2023.2244006
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Abstract
Remote sensing (RS) technologies are extensively exploited by scientists and a vast audience of local authorities, urban managers, and city planners. Coastal regions, geohazard-prone areas, and highly populated cities represent natural laboratories to apply RS technologies and test new methods. Over the last decades, many efforts have been spent on improving Earth's surface monitoring, including intensifying Earth Observation (EO) operations by the major national space agencies. They oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development opportunities in the geoscience field. For instance, within this framework, the European Space Agency (ESA) and the Ministry of Science and Technology of China (MOST) have sponsored, since the early 2000s, the DRAGON initiative jointly carried out by the European and Chinese RS scientific communities. This manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further developments. The main findings are related to i) the analysis of flood risk in China, ii) the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar (SAR) methods, iii) the analysis of the inundation risk in low-lying regions using coherent and incoherent SAR methods; and iv) the use of SAR-based technologies for marine applications.
Description
Journal or Series
GEO-SPATIAL INFORMATION SCIENCE
ISSN
1009-5020
ISBN
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openAccess
Keywords
Disaster risk management , Remote Sensing (RS) , Earth Observation (EO) , Synthetic Aperture Radar (SAR) , flooding , subsidence , coastal/marine environments , CLIMATE-CHANGE , VEGETATION DYNAMICS , TIME-SERIES , RIVER-BASIN , SPATIOTEMPORAL VARIATION , 3-NORTH REGION , EAST-AFRICA , LAND-COVER , CHINA , NDVI , Remote Sensing