Surface subsidence monitoring with an improved distributed scatterer interferometric SAR time series method in a filling mining area

Fan, Hongdong; Liu, Youfeng; Xu, Yaozong; Yang, Honglei

doi:10.1080/10106049.2021.2007300

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…The framework of NL-filter is to assign weighting strategy based on window similarity [33]. The application of NLfilter to phase optimization in InSAR technology has been one of the classic and potent methods [11], [30]. It traverses the pixels in the interferogram for phase optimization and its universal formula is denoted as: ( , )…”

Section: A Nl Methodsmentioning

confidence: 99%

“…In addition, within the interferogram domain, the NL method takes the similarity between windows as weights and applies the weighted average to estimate the optimized interferogram and denoising phase. Unlike the traditional spatial domain phase optimization method, the NL method efficiently extracts and optimizes the phase by taking the window as a unit, which in turn preserves the fringe texture information of the interferogram [30].…”

Section: Introduction Ime Series Insar Technology Has Been Increasinglymentioning

confidence: 99%

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NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

Liu

Yang

Jiang

et al. 2022

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

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When the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technology is used for surface deformation monitoring, the accuracy strongly depends on the quality of phase optimization. Especially in the low coherence region, how to conveniently and effectively improve the quality of phase optimization has been a hot and difficult research topic in recent years. This paper proposes a hybrid phase optimization method for the DSInSAR technology, which chooses either non-local (NL) or minimum mean square error (MMSE) method for each pixel according to the distribution of statistically homogeneous pixel (SHP) in two windows of different sizes. This hybrid method (NL-MMSE) is not limited by the number of interferogram and is not influenced by multi-master images. The NL-MMSE method was applied to the deformation monitoring in the Jinsha River basin, Tibet, China, using 28 Sentinel-1A SAR images acquired between February and December 2020. Compared with the NL and MMSE methods, the NL-MMSE method provides better phase quality of the interferogram stack and is able to extract more temporal coherence points for subsequent deformation inversion. The deformation monitoring results showed there are three obvious large-scale landslide deformation and dozens of small-scale deformation in the study area. It is demonstrated that the NL-MMSE method based on DSInSAR technology can accurately monitor the detailed deformation characteristics of the low coherence surface, and can be applied and promoted as an effective means of identifying and monitoring geological hazards.

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Section: A Nl Methodsmentioning

confidence: 99%

Section: Introduction Ime Series Insar Technology Has Been Increasinglymentioning

confidence: 99%

NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

Liu

Yang

Jiang

et al. 2022

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

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“…3-D laser scanning technology captures dense, irregular data, requiring larger data storage space and longer data processing times [7]. The main advantage of space-borne interferometric synthetic aperture radar (InSAR) systems is its wide spatial coverage, which is suitable for surface deformation monitoring in large study areas, such as cities, large slopes, and mining areas [8], [9], [10], [11]. However, the limitations of fixed orbit, long revisit time, low data resolution, and insensitivity to deformation in the north-south direction have hindered the wide application of space-borne InSAR systems in site-specific deformation monitoring.…”

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of Deformation Areas in the Construction Stage of Pumped Storage Power Station Using GB-InSAR Technology

Yang

Liu

Fan

et al. 2023

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

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To meet the needs of the rapid development of new energy sources, China is currently accelerating the construction of pumped storage power stations (PSPS). However, the complex geological environment and large-scale construction can easily cause deformation areas, such as collapse and sliding, on the surface of PSPS. This article adopts ground-based interferometric synthetic aperture radar (GB-InSAR) technology to monitor surface deformation using 206 ground-based radar (GBR) data collected from 2022/07/31 10:24 to 2022/08/06 14:57 with a PSPS in the construction stage in western China. In the selection of permanent scatterers (PSs) for GBR data processing, statistical homogeneous pixels (SHPs) information is considered in this article. Based on the fast SHP selection (FaSHPS) algorithm, we introduce intensity deviations (D I ) to exclude pixels with unstable intensity performance in the temporal dimension and propose the D I + F aSHP S algorithm, which is experimentally proven to have higher SHPs selection accuracy and computational efficiency. By combining with the traditional PSs selection algorithm, the distribution density of monitoring points is effectively improved. In addition, based on the deformation results and on-site survey, this article analyzes and discusses the identified deformation areas in detail from the aspects of natural geography and man-made activities. The results show that the surface is relatively stable overall, except for the effects of man-made activities. This study has demonstrated the remarkable reliability and superiority of the GB-InSAR technology in identifying and analyzing deformation areas in the construction stage of PSPS.

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“…However, a series of problems, such as ground subsidence, landslides, soil erosion, and destruction of structures resulting from the mining and utilization of coal resources, have introduced a large negative effect on the lives and property of local residents [3]. Therefore, it is of great practical significance to monitor the surface deformation caused by coal mining [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield

Tang

et al. 2022

Remote Sensing

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Intensive and large-scale underground coal mining has caused geological disasters such as local ground subsidence, cracks and collapse in the Datong coalfield, China, inducing serious threats to local residents. Interferometric synthetic aperture radar (InSAR) has the capability of surface deformation detection with high precision in vast mountainous areas. DInSAR, stacking-InSAR and SBAS-InSAR are commonly used InSAR-related deformation analysis methods. They can provide effective support for mine ecological security monitoring and prevent disasters. We use the three methods to conduct the deformation observation experiments in the Datong coalfield. Sentinel-1A data from November 2020 to October 2021 are used. As a result, a total of 256 deformations in the Datong coalfield were successfully detected by the three methods, of which 218 are mining deformations, accounting for 85% of the total deformations. By comparing the results of the three methods, we found that DInSAR, stacking-InSAR, and SBAS-InSAR detected 130, 256, and 226 deformations in the Datong coalfield, respectively, while the deformations caused by coal mining were 128, 218, and 190. DInSAR results with long spatiotemporal baselines are seriously incoherent. SBAS-InSAR results of displacement rate are more precise than stacking-InSAR, and the mean standard deviation is 1.0 mm/a. However, for areas with lush vegetation or low coherence, SBAS-InSAR has poor performance. The detection deformation area results of DInSAR and SBAS-InSAR are subsets of stacking-InSAR. The displacement rates obtained by stacking-InSAR and SBAS-InSAR are consistent; the mean difference in the displacement rate between the two methods is 2.7 mm/a, and the standard deviation is 5.1 mm/a. The mining deformation locations and their shapes in the study area can be identified with high efficiency and power by stacking-InSAR. Therefore, with a comprehensive understanding of the advantages and limitations of the three methods, stacking-InSAR can be an effective and fast method to identify the level, location and range of mining deformation in lush mountainous areas.

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Surface subsidence monitoring with an improved distributed scatterer interferometric SAR time series method in a filling mining area

Cited by 10 publications

References 32 publications

NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

Identification and Analysis of Deformation Areas in the Construction Stage of Pumped Storage Power Station Using GB-InSAR Technology

Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield

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