Long term detection of water depth changes of coastal wetlands in the Yellow River Delta based on distributed scatterer interferometry

REMOTE SENSING OF ENVIRONMENT

卷:164

Article

Xie, Chou¹  Xu, Ji²  Shao, Yun¹  Cui, Baoshan³  Goel, Kanika⁴  Zhang, Yunjun⁵  Yuan, Minghuan⁶ 

[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China

[2] Chinese Acad Surveying & Mapping, Beijing 100039, Peoples R China

[3] Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100875, Peoples R China

[4] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Oberpfaffenhofen, Wessling, Germany

[5] Univ Miami, Dept Marine Geosci, Rosenstiel Sch, Marine & Atmospher Sch, Miami, FL 33149 USA

[6] Hunan Normal Univ, Coll Resource & Environm, Changsha 410081, Hunan, Peoples R China

关键词: Wetlands;    Water depth;    Interferometry;    Distributed scatterer;    The Yellow River Delta;   

DOI  :  10.1016/j.rse.2015.04.010

来源: Elsevier

【 摘 要 】

Coastal wetland ecosystems are among the most productive yet highly threatened systems in the world, and population growth and increasing economic development have resulted to extremely rapid degradation and loss of coastal wetlands. Spacebome differential Interferometry SAR has proven a remarkable potential in wetland applications, including water level monitoring in high spatial resolution. However, due to the absence of ground observations for calibration and validation, long term monitoring of water depth, which is essential to evaluate ecosystem health of wetlands, is difficult to be estimated from spacebome InSAR data. We present a new differential synthetic aperture radar method for temporal evolution of water depth in wetlands. The presented technique is based on distributed scatter interferogram technique in order to provide a spatially dense hydrological observation for coastal wetlands, which are characterized by high temporal decorrelation. This method adapts a strategy by forming optimum interferogram network to get a balance between maximum interferometric information preservation and computational cost reduction, and implements spatial adaptive filtering to reduce noise and enhance fringe visibility on distributed scatterers. Refined InSAR observation is tied to absolute reference frame to generate long term high resolution water level time-series using stage data. We transform water level time-series to long term observation of water depth with assistance of a dense measurement network of water depth. We present water depth time-series obtained using the data acquired from 2007 to 2010 by the ALOS satellite, which supplied significant information to evaluate ecological performance of wetland restoration in the Yellow River Delta. (C) 2015 Elsevier Inc All rights reserved.

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