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Contribution of anthropogenic consolidation processes to subsidence phenomena from multi-temporal DInSAR: a GIS-based approach

The paper introduces an approach based on the combination of multi-temporal Differential Interferometric Synthetic Aperture Radar and geographical information systems analysis to investigate and separate several contributions to subsidence phenomena over the municipality of Ravenna (Emilia Romagna,...

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Published in:GIScience and remote sensing 2022-12, Vol.59 (1), p.1901-1917
Main Authors: Grassi, Francesca, Mancini, Francesco, Bassoli, Elisa, Vincenzi, Loris
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Language:English
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description The paper introduces an approach based on the combination of multi-temporal Differential Interferometric Synthetic Aperture Radar and geographical information systems analysis to investigate and separate several contributions to subsidence phenomena over the municipality of Ravenna (Emilia Romagna, Italy). In particular, the relationship between displacements detected over built environment and consolidation processes after construction was assessed and filtered out from the subsidence map to quantify the local overestimation of subsidence phenomena due to the mentioned processes. It requires descriptive attributes related to the age of construction and intended uses. The outcomes of the present study highlight ground consolidation processes that seem to be active over areas settled in the last 30 years with a component contributing to vertical rates up to 3 mm/yr. Such contribution represents the 20% of the cumulative displacements reported for coastal villages where different sources of subsidence increase the vulnerability to coastal erosion. We discuss the contribution of consolidation processes over a couple of recently settled areas to separate among contributions and avoid the misinterpretation of effects due to other anthropogenic sources of subsidence.
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subjects consolidation processes
mt-InSAR
Sentinel-1
subsidence components
title Contribution of anthropogenic consolidation processes to subsidence phenomena from multi-temporal DInSAR: a GIS-based approach
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