MyJournals Home  

RSS FeedsRemote Sensing, Vol. 11, Pages 129: Measuring Urban Subsidence in the Rome Metropolitan Area (Italy) with Sentinel-1 SNAP-StaMPS Persistent Scatterer Interferometry (Remote Sensing)

 
 

11 january 2019 15:00:23

 
Remote Sensing, Vol. 11, Pages 129: Measuring Urban Subsidence in the Rome Metropolitan Area (Italy) with Sentinel-1 SNAP-StaMPS Persistent Scatterer Interferometry (Remote Sensing)
 


Land subsidence in urban environments is an increasingly prominent aspect in the monitoring and maintenance of urban infrastructures. In this study we update the subsidence information over Rome and its surroundings (already the subject of past research with other sensors) for the first time using Copernicus Sentinel-1 data and open source tools. With this aim, we have developed a fully automatic processing chain for land deformation monitoring using the European Space Agency (ESA) SentiNel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS). We have applied this automatic processing chain to more than 160 Sentinel-1A images over ascending and descending orbits to depict primarily the Line-Of-Sight ground deformation rates. Results of both geometries were then combined to compute the actual vertical motion component, which resulted in more than 2 million point targets, over their common area. Deformation measurements are in agreement with past studies over the city of Rome, identifying main subsidence areas in: (i) Fiumicino; (ii) along the Tiber River; (iii) Ostia and coastal area; (iv) Ostiense quarter; and (v) Tivoli area. Finally, post-processing of Persistent Scatterer Inteferometry (PSI) results, in a Geographical Information System (GIS) environment, for the extraction of ground displacements on urban infrastructures (including road networks, buildings and bridges) is considered.


 
86 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 130: Recent Surface Deformation in the Tianjin Area Revealed by Sentinel-1A Data (Remote Sensing)
Remote Sensing, Vol. 11, Pages 128: Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam) (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten