Monitoring and Stability Analysis of the Deformation in the Woda Landslide Area in Tibet, China by the DS-InSAR Method

Liu, Youfeng; Yang, Honglei; Shizheng, Wang; Xu, Linlin; Peng, Junhuan

doi:10.3390/rs14030532

Cited by 29 publications

(21 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the three landslides, we conclude that A2, B2 and C2, located in the middle of the slope, have the largest time series deformation. Combined with the existing research results of A and B [7], it is inferred that the deformation is most obvious in the central area of each landslide. In addition, we related the average deformation rate of A1-C3 to the local weather data and the results are shown in Fig.…”

Section: Deformation Detection and Analysismentioning

confidence: 56%

“…From October to December, the weather is again dominated by snowfall, and the average deformation rate decreased accordingly. Combining the results of existing analyses of the relationship between rainfall and surface deformation in the region [7], we conclude that rainfall is one of the main influencing factors to accelerate landslide deformation.…”

Section: Deformation Detection and Analysismentioning

confidence: 62%

“…4). The area has a typical alpine valley landscape, featured by a narrow section of the Jinsha River passing through deep valleys [7]. The geological structure dominated by schist, slate and shale of the Upper Triassic Lanashan Formation and crystalline tuff and feldspathic quartz sandstone of the Upper Triassic Tumugou Formation.…”

Section: A Study Area and Data Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Deformation Detection and Analysismentioning

confidence: 56%

Section: Deformation Detection and Analysismentioning

confidence: 62%

Section: A Study Area and Data Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering the compromise between the computation cycle and the accuracy of the algorithm, we choose a window size of 400 pixels, a step of 20 pixels, and a search range of 50 pixels. Such settings allow obtaining a displacement value of up to 5 m. In general, in less than 1 month, it seems unlikely that the displacement of a landslide that has not experienced overall failure to exceed this displacement threshold (Kang et al, 2017; Y. Liu et al, 2022; Tang et al, 2015). Meanwhile, DSM‐derived hillshade was selected as the correlation image pair because it represents the surface morphological features that are more favourable for correlation and can yield better sPOT results than other image pairs (Leprince et al, 2007; Lucieer et al, 2014; Turner et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

et al. 2023

View full text Add to dashboard Cite

Detecting slope deformation is an important issue in engineering. Timely deformation detection can effectively avoid catastrophic slope failure and ensure the safety of a project and engineering personnel. In this study, deformation detection for a quarry slope is implemented using the integration of subpixel offset tracking (sPOT) and unsupervised change detection algorithms based on unmanned aerial vehicle (UAV) image datasets. The sPOT algorithm is used to give the surface displacement field of the slope with subpixel accuracy, and the unsupervised change detection algorithm yields the ground object reconstruction area of the slope to verify and explain the sPOT result. The integrated analysis method in this paper is highly applicable and only requires a minimum of two UAV datasets as raw data. Combining the advantages of the sPOT and unsupervised change detection algorithms, the proposed method has the ability to detect and analyse slow and rapid slope deformation with good accuracy.

show abstract

“…It helps to locate and monitor underground mining subsidence in a wide area with low cost and high efficiency that conventional geophysical techniques, such as leveling [19], three-dimensional laser scanning [20], and the global navigation satellite system (GNSS) [21], are unlikely to do that [22]. However, the application of conventional DInSAR is mainly limited by spatiotemporal incoherence and atmospheric phase screening [23][24][25]. Wu et al obtained the ground surface subsidence of the Kailuan mining area in Tangshan city, China from two ERS-1/2 images with a time span of half a year, using processed DInSAR and analyzing the expansion and evolution process of the subsidence, as well as the influence of spatiotemporal incoherence, atmospheric phase delay and DEM error on DInSAR results [26].…”

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

View full text Add to dashboard Cite

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.

show abstract

Monitoring and Stability Analysis of the Deformation in the Woda Landslide Area in Tibet, China by the DS-InSAR Method

Cited by 29 publications

References 48 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

Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

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

Contact Info

Product

Resources

About