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TitleTime-series analysis of subsidence associated with rapid urbanization in Shanghai, China measured with SBAS InSAR method
AuthorDong, S; Samsonov, S; Yin, H; Cao, Y
SourceEnvironmental Earth Sciences vol. 72, issue 2, 2013 p. 677-691, https://doi.org/10.1007/s12665-013-2990-y
Year2013
Alt SeriesEarth Sciences Sector, Contribution Series 20150206
PublisherSpringer Nature
Documentserial
Lang.English
Mediapaper; digital; on-line
File formatpdf
AreaShanghai; China
Lat/Long WENS 121.1167 121.8333 31.5000 30.8333
Subjectsgeophysics; remote sensing; subsidence; subsidence rates; urban geology; urban planning; land use; deltas; deformation; InSAR; ALOS PALSAR; interferometry
Illustrationslocaiton maps; flow charts; geophysical images; plots; histograms; satellite images
Released2013 12 10
AbstractRiver delta plains (deltas) are susceptible to subsidence producing undesirable environmental impact and affecting dense population. The City of Shanghai, located in the easternmost of Yangtze Delta in China, is one of the most developed regions in China that experiences the greatest land subsidence. Excessive groundwater withdrawal is thought to be the primary cause of the land subsidence, but rapid urbanization and economic development, mass construction of skyscrapers, metro lines and highways are also contributing factors. In this paper, a spatial-temporal analysis of the land subsidence in Shanghai was performed with the help of the Small Baseline Subset Interferometric Synthetic Aperture Radar. Twenty L-band ALOS PALSAR images acquired during 2007-2010 were used to produce a linear deformation rate map and to derive time series of ground deformation. The results show homogeneous subsidence within the research area, but exceptionally rapid subsidence around skyscrapers, along metro lines, elevated roads and highways was also observed. Because groundwater exploitation and rapid urbanization responsible for much of the subsidence in the Shanghai region are expected to continue, future subsidence monitoring is warranted.
GEOSCAN ID296911