Two-dimensional deformation monitoring of karst landslides in Zongling, China, with multi-platform distributed scatterer InSAR technique

Chen, Hengyi; Zhao, Chaoying; Sun, Rongrong; Chen, Liquan; Wang, Baohang; Li, Bin

doi:10.1007/s10346-022-01860-4

Cited by 11 publications

(6 citation statements)

References 62 publications

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“…This deformation conforms to the general performance of slope deformation caused by mining activities (Zheng et al, 2015). Similar deformation characteristic also occurred in Jianshanying landslide (Chen et al, 2021), Zongling landslide (Chen et al, 2022a) and Kaiyang landslide (Chen et al, 2022b), which were all mining related. The reasons for the deformation above are as follows:…”

Section: The Driving Factors Of Karst Slope Instabilitysupporting

confidence: 81%

“…The slope movement perpendicular to the LOS cannot be monitored, and the descending and ascending geometries are favorable, respectively, for west and east facing slopes (Wasowski and Bovenga, 2014), which would result in large differences in the deformation results obtained from the two data (Liu et al, 2021). And compared to the C-band Sentinel-1A, the L-band ALOS/PALSAR-2 has better penetration of vegetation and can detect more covert landslides (Chen et al, 2022a). Therefore, both descending and ascending acquisitions are available the limitations related to topography (layover, shadowing) can be significantly reduced (Grebby et al, 2021), and can increase the number of identifiable landslides.…”

Section: Landslides Identification In Shuicheng Districtmentioning

confidence: 99%

“…They proposed some improved algorithms, such as the Eigenvalue decomposition based Maximum-likelihood-estimator of Interferometric phase (EMI) (Ansari et al, 2018) and the Fast statistically homogeneous pixel selection (FaSHPS) (Jiang et al, 2015). These advanced DS-InSAR technologies can significantly increase the density of monitoring targets in landslide deformation monitor, especially in lush mountain areas (Chen et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

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Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Sun

Zhao

et al. 2023

Front. Earth Sci.

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Shuicheng District is a karst mountain area, located in Guizhou Province, China. Its fragile stratum and frequent underground mining activities makes it prone to landslides. Owning to its wide coverage and frequent revisit, the InSAR technology has advantages in potential landslide identification and deformation monitor. However, affected by dense vegetation and atmospheric delay, it is much difficult to get sufficient effective targets to derive the deformation in this area. Besides, deformation derived from single orbit SAR data can result in the missing identification of some potential landslides and the misinterpreting of the real kinematics process of landslides. In this study, the multi-source SAR data, atmospheric error correction by quadratic tree image segmentation method, and phase-stacking method were selected to derive the surface deformation of this area. Besides, DS-InSAR and MSBAS method were combined to derive the deformation of Pingdi landslide. First, the potential landslides in this area were identified, surface deformation result, optical remote sensing images and geomorphological features were jointly considered. Then, the landslide distribution characteristics was analyzed in terms of slope, elevation and stratum. After that, the deformation along the LOS direction was acquired using the DS-InSAR method. The MSBAS method was used to retrieve the two-dimensional deformation of Pingdi landslide. Finally, the comprehensive analysis of triggering factors and failure process were conducted according to the spatial-temporal deformation characteristics and field investigation. The results indicated that landslides in Shuicheng district were mostly located in the junction of T1 and P3 stratum and mining related. Mining activity was the main cause of the Pingdi landslide deformation, the precipitation was the driving factor of the landslide instability. The research provides an insight into the explore the unstable slope distribution characteristic and the failure process of the landslides.

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Section: The Driving Factors Of Karst Slope Instabilitysupporting

confidence: 81%

Section: Landslides Identification In Shuicheng Districtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Sun

Zhao

et al. 2023

Front. Earth Sci.

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“…InSAR monitoring of creeping landslides is a challenging task in mountainous regions because of decorrelation [42,43]. To test the proposed approach, we take the Baige landslide as an example, which occurred on the left bank of the Jinsha River in China over two separate days, on 10 October and 3 November 2018.…”

Section: Study Area Sar Datasetmentioning

confidence: 99%

Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation

et al. 2023

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Today, synthetic aperture radar (SAR) satellites provide large amounts of SAR data at unprecedented temporal resolutions, which promotes hazard dynamic monitoring and disaster mitigation with interferometric SAR (InSAR) technology. This study focuses on big InSAR data dynamical processing in areas of serious decorrelation and large gradient deformation. A new stepwise temporal phase optimization method is proposed to alleviate the decorrelation, customized for deformation parameter dynamical estimation. Subsequently, the sequential estimation theory is introduced to the intermittent small baseline subset (ISBAS) approach to dynamically obtain deformation time series with dense coherent targets. Then, we analyze the reason for the unstable accuracy of deformation parameters using sequential distributed scatterers-ISBAS technology, and construct five indices to describe the quality of deformation parameters pixel-by-pixel. Finally, real data of the post-failure Baige landslide at the Jinsha River in China is used to demonstrate the validity of the proposed approach.

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“…However, in low coherence regions that are dominated by farmland, woodland and mountainous areas, Small Baseline Subset (SBAS) [4] and SqueeSAR [5] Manuscript Youfeng Liu, Honglei Yang and Zhaowei Lu are with the School of Land Science and Technology, China University of Geosciences, Beijing, 100083, methods are used. In recent years, the DSInSAR technique, developed on the basis of the SqueeSAR method, has been increasingly used in low coherence regions [6]- [8]. In the low coherence region, the feature backscatter is low and lacks high temporal coherence points.…”

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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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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Two-dimensional deformation monitoring of karst landslides in Zongling, China, with multi-platform distributed scatterer InSAR technique

Cited by 11 publications

References 62 publications

Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation

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

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