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RSS FeedsRemote Sensing, Vol. 10, Pages 1191: Land Subsidence in Coastal Environments: Knowledge Advance in the Venice Coastland by TerraSAR-X PSI (Remote Sensing)

 
 

15 august 2018 10:01:21

 
Remote Sensing, Vol. 10, Pages 1191: Land Subsidence in Coastal Environments: Knowledge Advance in the Venice Coastland by TerraSAR-X PSI (Remote Sensing)
 




The use of satellite SAR interferometric methods has significantly improved the monitoring of ground movements over the last decades, thus opening new possibilities for a more accurate interpretation of land subsidence and its driving mechanisms. TerraSAR-X has been extensively used to study land subsidence in the Venice Lagoon, Italy, with the aim of quantifying the natural and anthropogenic causes. In this paper, we review and update the main results achieved by three research projects supported by DLR AOs (German Aerospace Center Announcement of Opportunity) and conducted to test the capability of TerraSAR-X PSI (Persistent Scatterer Interferometry) to detect ground movements in the complex physiographic setting of the Venice transitional coastal environment. The investigations have been focused on the historical center of Venice, the lagoon inlets where the MoSE is under construction, salt marshes, and newly built-up areas in the littoral. PSI on stacks of stripmap TerraSAR-X images covering short- to long-time periods (i.e., the years 2008–2009, 2008–2011 and 2008–2013) has proven particularly effective to measure land subsidence in the Venice coastland. The very high spatial resolution (3 m) and the short repeat time interval (11 days) of the TerraSAR-X acquisitions make it possible to investigate ground movements with a detail unavailable in the past. The interferometric products, properly calibrated, allowed for a millimetric vertical accuracy of the land movements at both the regional and local scales, even for short-term analyses, i.e., spanning one year only. The new picture of the land movement resulted from processing TerraSAR-X images has significantly contributed to update the knowledge on the subsidence process at the Venice coast.


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27 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 10, Pages 1192: Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps (Remote Sensing)
Remote Sensing, Vol. 10, Pages 1190: Lead Detection in Polar Oceans--A Comparison of Different Classification Methods for Cryosat-2 SAR Data (Remote Sensing)
 
 
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