A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

Zhang, Rui; Liu, Guoxiang; Li, Zhilin; Zhang, Guo; Lin, Hui; Yu, Bing; Wang, Xiaowen

doi:10.3390/rs70100211

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…However, the high cost of HPC equipment hinders the popularization of this strategy. Another way is to improve the TS-InSAR method itself, by introducing sequential adjustment theory for real-time InSAR data processing [20][21][22], introducing a geological model or time-series filtering algorithm for high-dimensional deformation calculation [23][24][25][26], or realizing a high-precision TS-InSAR deformation calculation using block solutions [27,28]. These strategies can improve the efficiency of the TS-InSAR solution to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China

et al. 2022

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In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the WAVS–InSAR strategy). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km2 and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology.

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Section: Introductionmentioning

confidence: 99%

A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China

et al. 2022

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“…It has its unique advantages in large-scale surface displacement monitoring. The time series InSAR technology (represented by PSI [ 4 ], SBAS [ 5 ] and others [ 6 , 7 , 8 ]) has been widely used in many areas such as high-speed rail settlement monitoring [ 9 , 10 ], urban subsidence monitoring [ 11 , 12 , 13 , 14 , 15 ] and landslide displacement monitoring [ 16 , 17 , 18 , 19 , 20 , 21 ]. A large number of studies have used time series InSAR to monitor surface deformation in permafrost regions with L-band (e.g., [ 22 , 23 , 24 ]) and C-band (e.g., [ 23 , 25 , 26 , 27 , 28 ]) images.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Highway Stability in Permafrost Regions with X-band Temporary Scatterers Stacking InSAR

Dai

Liu

et al. 2018

Sensors

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Interferograms with short wavelength (e.g., X-band) are usually prone to temporal decorrelation in permafrost regions, leading to the unavailability of sufficient high-coherence interferograms for performing conventional time series InSAR analysis. This paper proposes the utilization of temporary scatterers for the stacking InSAR method, thus enabling extraction of subsidence in a permafrost region with limited SAR images and limited high-coherence interferograms. Such method is termed as the temporary scatterers stacking InSAR (TSS-InSAR). Taking the Gonghe-Yushu highway (about 30 km), part of G214 National Highway in Qinghai province (in a permafrost region), as a case study, this TSS-InSAR approach was demonstrated in detail and implemented. With 10 TerraSAR-X images acquired during the period from May 2015 to August 2015, the subsidence along this highway was extracted. In this case the lack of a consistent number of SAR acquisitions limits the possibility to perform other conventional time series InSAR analysis. The results show that the middle part of this highway is in the thermokarst and seasonal frozen soil area, and its accumulated subsidence reach up to 10 cm in 110 days. The thawing phenomena is still the main reason for the instability of highway. The results demonstrate that the TSS-InSAR method can effectively extract the subsidence information in a challenging scenario with limited X-band SAR images and limited high-coherence interferograms, where other time series InSAR-based techniques cannot be applied in a simple way.

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Block PS-InSAR ground deformation estimation for large-scale areas based on network adjustment

Hou

Zhu

et al. 2021

J Geod

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A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

Cited by 7 publications

References 19 publications

A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China

A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China

Monitoring Highway Stability in Permafrost Regions with X-band Temporary Scatterers Stacking InSAR

Block PS-InSAR ground deformation estimation for large-scale areas based on network adjustment

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