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Proceedings Paper

Potential inundated coastal area estimation in Shanghai with multi-platform SAR and altimetry data
Author(s): Guanyu Ma; Tianliang Yang; Qing Zhao; Julia Kubanek; Antonio Pepe; Hongbin Dong; Zhibin Sun
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Paper Abstract

As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030. Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.

Paper Details

Date Published: 1 September 2017
PDF: 20 pages
Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 104050V (1 September 2017); doi: 10.1117/12.2272961
Show Author Affiliations
Guanyu Ma, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Joint Research Institute for New Energy and the Environment (China)
Tianliang Yang, Ministry of Land and Resources (China)
Shanghai Institute of Geological Survey (China)
Shanghai Engineering Research Ctr. of Land Subsidence (China)
Qing Zhao, East China Normal Univ. (China)
Joint Lab. for Enviornmental Remote Sensing and Data Assimilation (China)
Joint Research Institute for New Energy and the Environment (China)
Julia Kubanek, McGill Univ. (Canada)
Antonio Pepe, Institute for Electromagnetic Sensing of the Environment, Italian National Research Council (Italy)
Hongbin Dong, Shanghai Shixi High School (China)
Zhibin Sun, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 10405:
Remote Sensing and Modeling of Ecosystems for Sustainability XIV
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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