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多源 InSAR 分析刻画的乌鲁米耶湖堤道的时空变形模式。

Spatiotemporal deformation patterns of the Lake Urmia Causeway as characterized by multisensor InSAR analysis.

机构信息

Department of Architecture and Building Engineering, School of Environment and Society, Tokyo Institute of Technology, 4259-G3-2 Nagatsuta, Yokohama, Kanagawa, 226-8502, Japan.

Department of GIS and Remote Sensing, University of Tabriz, Tabriz, 5166616471, Iran.

出版信息

Sci Rep. 2018 Apr 3;8(1):5357. doi: 10.1038/s41598-018-23650-6.

Abstract

We present deformation patterns in the Lake Urmia Causeway (LUC) in NW Iran based on data collected from four SAR sensors in the form of interferometric synthetic aperture radar (InSAR) time series. Sixty-eight images from Envisat (2004-2008), ALOS-1 (2006-2010), TerraSAR-X (2012-2013) and Sentinel-1 (2015-2017) were acquired, and 227 filtered interferograms were generated using the small baseline subset (SBAS) technique. The rate of line-of-sight (LOS) subsidence of the LUC peaked at 90 mm/year between 2012 and 2013, mainly due to the loss of most of the water in Lake Urmia. Principal component analysis (PCA) was conducted on 200 randomly selected time series of the LUC, and the results are presented in the form of the three major components. The InSAR scores obtained from the PCA were used in a hydro-thermal model to investigate the dynamics of consolidation settlement along the LUC based on detrended water level and temperature data. The results can be used to establish a geodetic network around the LUC to identify more detailed deformation patterns and to help plan future efforts to reduce the possible costs of damage.

摘要

我们根据从四个 SAR 传感器收集的数据,呈现了伊朗西北部乌鲁米耶湖堤道(LUC)的变形模式,这些数据以干涉合成孔径雷达(InSAR)时间序列的形式呈现。我们获取了 Envisat(2004-2008 年)、ALOS-1(2006-2010 年)、TerraSAR-X(2012-2013 年)和 Sentinel-1(2015-2017 年)的 68 幅图像,并使用小基线集(SBAS)技术生成了 227 幅滤波干涉图。LUC 的视线(LOS)沉降率在 2012 年至 2013 年间达到了 90 毫米/年的峰值,主要是由于乌鲁米耶湖的大部分水流失。我们对 LUC 上的 200 条随机选择的时间序列进行了主成分分析(PCA),并以三个主要成分的形式呈现了结果。PCA 得到的 InSAR 得分被用于热-水模型,以根据去趋势水位和温度数据研究 LUC 沿线的固结沉降动态。研究结果可用于在 LUC 周围建立大地测量网络,以识别更详细的变形模式,并有助于规划未来减少可能的损害成本的工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9db3/5882932/a8d6026d6f99/41598_2018_23650_Fig1_HTML.jpg

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