Ground Displacement Trends in an Urban Environment Using Multi-Temporal InSAR Analysis and Two Decades of Multi-Sensor Satellite-Based SAR Imagery
Event Title
9th International Workshop Fringe 2015 Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop
Year (definitive publication)
2015
Language
English
Country
Italy
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Abstract
This exploratory research used three sets of single polarized synthetic aperture radar (SAR) satellite data and a multi-temporal radar interferometry (InSAR) methodology to determine the spatial evolution and ground displacement trends of several industrial parks located in the metropolitan area of Bucharest, Romania. From 70 candidate areas, 20 large industrial parks were selected for analysis and interpretation. InSAR analysis used SAR data acquired between 1992 and 2014 by ERS-1/-2, ENVISAT, and TerraSAR-X satellites. Ground movement patterns identified before and after 2000 were linked to groundwater table investigations based on 25 water wells, located on or in the proximity of these areas. The analysis revealed an initial subsidence or no change in uplift areas before 2000, followed by a return to zonal movement. This trend may also be related to the shutting down of industries that consumed large amounts of water, which increased deep groundwater pressure. Only one continuous subsidence trend was identified for an industrial area located south of the city, an area which continues to be active over time. Ongoing research is focused on using traditional geological and geomorphologic investigations, as well as comparisons with field Global Navigation Satellite System (GNSS) data.
Ground Displacement Trends in an Urban Environment Using Multi-Temporal InSAR Analysis and Two Decades of Multi-Sensor Satellite-Based SAR Imagery (PDF Download Available). Available from: https://www.researchgate.net/publication/274566303_Ground_Displacement_Trends_in_an_Urban_Environment_Using_Multi-Temporal_InSAR_Analysis_and_Two_Decades_of_Multi-Sensor_Satellite-Based_SAR_Imagery [accessed Jan 14, 2016].
Acknowledgements
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Keywords
Ground deformation, INSAR, patterns, chaos theory, nonlinear time series