Recent subsidence in Tianjin, China: observations from multi-looking TerraSAR-X InSAR from 2009 to 2013

Zhu, Chuanguang; Zhang, Yonghong; Zhang, Jixian; Zhang, Liya; Long, Sichun; Wu, H. R.

doi:10.1080/01431161.2015.1109729

Cited by 10 publications

(14 citation statements)

References 24 publications

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“…The linear-trend phase is considered to be the result of orbital inaccuracies and is eliminated using a bilinear equation. A multilook of 3×1 (azimuth × slant range) is applied to the interferometric pairs to reduce the phase noise and improve the coherence [32]. In addition, the phase noise is further minimized by applying the Goldstein adaptive filter with the parameter α equal to 0.6 [33].…”

Section: B Sar Data and Dinsar Processing Resultsmentioning

confidence: 99%

Retrieval and Prediction of Three-Dimensional Displacements by Combining the DInSAR and Probability Integral Method in a Mining Area

Zhu

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring ground displacement produced by underground mining is essential to ensure the safety of infrastructure over mining areas. Differential synthetic aperture radar (DInSAR) can only obtain the 1-D [i.e., along the line-of-sight (LOS) direction] displacement component. In this study, we present an improved algorithm for retrieving and predicting 3-D displacement fields induced by underground mining based on the LOS displacement derived from DInSAR and the probability integral method (PIM). Whole parameters included in the standard PIM model are involved in the improved algorithm. In addition, the interaction between multiple working panels is considered and incorporated into the model. Next, a stochastic optimization technique hybridizing the cultural algorithm and random particle swarm optimization has been designed to retrieve model parameters, which can be used to retrieve and predict the 3-D displacement field. Simulated experiments show that the root mean square errors (RMSEs) are 10, 12, and 17 mm in the vertical, east-west, and north-south directions, respectively, by comparing the simulated and retrieved 3-D displacement. Furthermore, the capability of the proposed method is investigated and validated in the Xuehu mining area of China using three ALOS PALSAR acquisitions. Our results agree well with leveling measurements in the vertical direction with an RMSE of 38 mm. Although the retrieved horizontal displacement cannot be validated due to a lack of field surveys, these displacement fields coincide spatially with the evolution of mining excavation. Index Terms-Cultural algorithm and random particle swarm optimization (CA-rPSO), mining displacement, probability integral method (PIM), three-dimensional (3-D) displacement.

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Section: B Sar Data and Dinsar Processing Resultsmentioning

confidence: 99%

Retrieval and Prediction of Three-Dimensional Displacements by Combining the DInSAR and Probability Integral Method in a Mining Area

Zhu

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…After the 1980s, some measures, such as injection and restricting exploitation of groundwater, have been taken to reduce the subsidence rate. However, the ground subsidence rate is still very high (approximately 137 mm/year), and the phenomenon of subsidence has even worsened since 2010 (Zhu et al, 2015). The performance of the presented method will be investigated using ASAR and TSX images with the ground coverage represented by red and blue rectangles, respectively, in Figure 4.…”

Section: Study Area and Available Datamentioning

confidence: 99%

Time Series Multi-Sensors of Interferometry Synthetic Aperture Radar for Monitoring Ground Deformation

Zhu

Long

Zhang

et al. 2022

Front. Environ. Sci.

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A stack of images is a prerequisite for the multi-temporal interferometric synthetic aperture radar (MT-InSAR) due to the wrapped nature of the interferometric phase. Although the SBAS technique can relieve the requirement of the amount of SAR data, dozens of SAR acquisitions could be regarded as the minimum requirement. However, due to the limitation of the imaging capability of the spaceborne SAR system, the amount of available SAR data acquired from only one SAR sensor is often not enough to satisfy the requirement for phase unwrapping based on the Nyquist sampling assumption. Fortunately, there sometimes may be more than one SAR stack, that is, stacks of SAR data acquired from different SAR systems. In this study, we propose a methodology to detect ground deformation by combining multiple SAR images acquired from different satellite systems for MT-InSAR analysis. First, the low-pass deformation is estimated based on time series SAR acquisitions with low spatial resolution and long wavelengths such as ENVISAT ASAR (ASAR). This information is then incorporated into the processing of time series of SAR acquisitions with high spatial resolution and short wavelength, such as TerraSAR-X (TSX). Specifically, the low-pass deformation will be subtracted from each differential interferogram generated from short-wavelength SAR images, and the rest of the MT-InSAR analysis will be based on the double-differentiation interferograms. Then, the residual deformation will be calculated from these double-differentiation interferograms and together with the low-pass deformation forms the full deformation. As the principal component of deformation has already been subtracted, the phase gradient of those double-differentiated interferograms will be smooth enough to facilitate the phase unwrapping. Between January 2009 and September 2010, 14 ASAR images and 11 TSX images acquired from Tianjin, China are selected as the test data. A root means square error (RMSE) of 9.1 mm/year is achieved from 11 TSX images, while a root means square error of 3.7 mm/year is achieved from 14 ASAR images. However, an RMSE of 1.6 mm/year is achieved when integrating 11 TSX images and 14 ASAR images for MT-InSAR analysis. The experiments show that the proposed method can effectively detect ground deformation.

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“…This concurs with the findings of Guo et al [12], who derived the land subsidence of Tianjin from 2015 to 2016 based on Sentinel-1A using the method of SBAS, showing that the subsidence rate of the central urban area varies from −6.1 to 3.9 mm/year and the subsidence rate of the high settlement area in the suburbs ranges between −126.3 and −65.7 mm/year. Due to Tianjin and the central government adopting a variety of measures to control subsidence [3,6,26] since the 1980s (such as deriving alternative surface water, restriction on groundwater extraction, manual recharge of aquifers, and establishment of a settlement monitoring network), the urban settlement in Tianjin is presently under control [27,29]. However, subsidence in the suburbs of Tianjin has not been effectively managed and the issue is becoming serious in recent years.…”

Section: Recent Ground Subsidence Characteristics and Its Influencing...mentioning

confidence: 99%

“…There is no doubt that human activities have increased the demand for geological and water resources. In the evolution of urbanization, Tianjin has experienced large scale groundwater extraction, inducing serious land subsidence [26]. Land subsidence has been affecting Tianjin for the past 60 years.…”

Section: Introductionmentioning

confidence: 99%

Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods

et al. 2020

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Multi-temporal InSAR (MT-InSAR) methods have been widely used in remote sensing monitoring of ground subsidence, which occurs at many places around the world. Land subsidence, caused by excessive extraction of groundwater, has always been a problem to be solved in Tianjin, China. Although the subsidence in the urban area has been controlled at a low rate, the subsidence issue has not been effectively solved in the suburban area recently, which should be paid much attention. This paper aims to present two multi-temporal differential interferometry techniques, persistent scatterer (PS) and small baseline subset (SBAS), for monitoring the latest surface subsidence in a Tianjin study area on the basis of 20 Sentinel-1A images obtained from March 2017 to March 2019. Our research showed that the average velocity map obtained from the SBAS method closely followed the outcomes of the PS technique from the perspective of identifying similar subsidence patterns. Subsidence rate gradually increased from the urban area of Tianjin to the suburbs and high subsidence zones were mainly distributed at the junction of the Wuqing, Xiqing and Beichen districts. In the past two years, the annual average subsidence rate in the high settlement area mostly exceeded −50 mm/year, which caused serious damage to local infrastructures. Besides, high-resolution remote sensing images combined with field investigations further verified the successful application of MT-InSAR technology in Tianjin’s subsidence monitoring. Effective ground subsidence control measures need to be taken as soon as possible to prevent the situation from getting worse.

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Recent subsidence in Tianjin, China: observations from multi-looking TerraSAR-X InSAR from 2009 to 2013

Cited by 10 publications

References 24 publications

Retrieval and Prediction of Three-Dimensional Displacements by Combining the DInSAR and Probability Integral Method in a Mining Area

Retrieval and Prediction of Three-Dimensional Displacements by Combining the DInSAR and Probability Integral Method in a Mining Area

Time Series Multi-Sensors of Interferometry Synthetic Aperture Radar for Monitoring Ground Deformation

Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods

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