3D displacement and deformation mechanism of deep-seated gravitational slope deformation revealed by InSAR: a case study in Wudongde Reservoir, Jinsha River

Ren, Kaiyu; Yao, Xia; Li, Renjiang; Zhou, Zhenkai; Yao, Chuangchuang; Jiang, Shu

doi:10.1007/s10346-022-01905-8

Cited by 20 publications

(9 citation statements)

References 26 publications

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“…Although the 3‐D movements

{X}^{i}

can be in theory estimated by weighted least square (WLS) adjustment, similar imaging geometries of different datasets usually leads to an ill‐posed problem (Zheng et al., 2017). The SPF assumption is a common method for deriving the 3‐D deformations in deep‐seated slow‐moving landslides (X. Hu et al., 2018; Ren et al., 2022). The assumption under consideration utilizes topographical information to constrain surface deformation.…”

Section: Methodsmentioning

confidence: 99%

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Enhanced Kinematic Inversion of 3‐D Displacements, Geometry, and Hydraulic Properties of a North‐South Slow‐Moving Landslide in Three Gorges Reservoir

Zheng

Lü

et al. 2023

JGR Solid Earth

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Complete three‐dimensional (3‐D) movements of slow‐moving landslides are critical to enhancing the understanding of the landslide mechanism. Multi‐source synthetic aperture radar (SAR) observations provide an opportunity to derive 3‐D movements. However, deriving the complete 3‐D movements faces potential challenges of incoherent phases and an ill‐posed inverse problem, which may result in incomplete and inaccurate results, especially for slopes facing north/south. Here, we propose a topography‐constrained strain model, which exploits the spatial relationship of 3‐D deformations between neighboring points as well as the assumption of the surface parallel flow of landslide, to derive complete 3‐D movements. Both synthetic and real datasets over the north‐south Xinpu landslide complex are utilized, to assess if the implemented method can overcome the ill‐posed condition and retrieve the complete 3‐D movement field. With the multi‐source SAR datasets, the performance of various datasets and the potential of NISAR in deriving time series and 3‐D movements are assessed. Based on the derived complete 3‐D movements and long‐term InSAR measurements, the landslide metrics, including elementary parameters of landslide geometry, spatial‐temporal patterns of movement, thickness, and hydraulic diffusivity, are derived to reveal that (a) the thickest landslide mass concentrates in the toe of the landslide, and (b) the effects of precipitation are more significant than those of the water level fluctuation in the Xinpu landslide complex, Three Gorges Reservoir areas.

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“…Although the 3‐D movements

{X}^{i}

Section: Methodsmentioning

confidence: 99%

“…The SPF assumption is a common method for deriving the 3-D deformations in deep-seated slow-moving landslides (X. Hu et al, 2018;Ren et al, 2022). The assumption under consideration utilizes topographical information to constrain surface deformation.…”

Section: Background Of 3-d Deformation Inversion and Surface-parallel...mentioning

confidence: 99%

Enhanced Kinematic Inversion of 3‐D Displacements, Geometry, and Hydraulic Properties of a North‐South Slow‐Moving Landslide in Three Gorges Reservoir

Zheng

Lü

et al. 2023

JGR Solid Earth

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“…Considering the relatively long data gap in the ascending orbit data after May 2021, cumulative deformations were calculated from October 2018 to May 2021 using data from three orbits. These results were then compared with those obtained from other methods, such as the SPFM [32], SPFM coupled with the least-squares method (SPF-LSM) [60], and the APFM [62]. The Adjacent Orientation Consistency Model (AOCM) mentioned in this paper does not utilize slope and aspect as the displacement direction for surface deformation.…”

Section: The 3d Deformation Characteristics Of the Xiaomojiu Landslidementioning

confidence: 99%

“…The second method, which involves adding constraints through prior information to reduce the required true observations [30], has become a standard technique for landslide deformation inversion. For example, in east-west landslides, the north-south horizontal deformation is often neglected [20], or models like the Surface Parallel Flow Model (SPFM) are used [31][32][33]. SPFM is frequently applied to translational landslides [34] but is unsuitable for rotational landslides with arcuate sliding surfaces [35,36].…”

Section: Introductionmentioning

confidence: 99%

Deformation Characteristics and Activation Dynamics of the Xiaomojiu Landslide in the Upper Jinsha River Basin Revealed by Multi-Track InSAR Analysis

Ma,

Peng,

et al. 2024

Remote Sensing

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The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels’ in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin.

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“…The river flows within deep canyon areas and plateaus [13,14], and the maximum height difference between canyons ranges from 2000-3000 m. The river valley is deeply incised, and the slopes of both banks are steep; it can thus easily become unstable under the actions of unloading, gravity, rainfall, and river erosion due to the influences of various factors, such as topography, stratum lithology, and geological structures [15,16]. In addition, the impoundment of reservoirs in canyons can aggravate the destruction of bank slopes [17,18]. The Jinsha River confluence area has a broken structure, weak rock formations, and frequent geological disasters in ultra-high locations.…”

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of Landslides in the Ahai Reservoir Area of the Jinsha River Basin Using a Combination of DS-InSAR, Optical Images, and Field Surveys

Zuo

Da-ming

et al. 2022

Remote Sensing

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We employed ascending and descending Sentinel-1A, optical image data, and field investigation methods to identify and monitor landslides in the Jinsha River Basin to overcome the difficulties associated with the use of a single method and its inaccuracies in identifying landslides in the alpine and canyon areas. Using distributed scatterer-synthetic aperture radar interferometry (DS-InSAR), Sentinel-1A ascending and descending data were integrated to obtain surface deformation information within the study area from July 2017 to May 2019. Thereafter, high-resolution optical image data were introduced to interpret landslides, and field investigations were conducted to validate landslides. These combined methods enabled the assessment of spatiotemporal evolutionary characteristics, and their accuracy in identifying typical landslides was verified. The results showed that the use of both ascending and descending data effectively avoided certain problems, such as the inability to identify certain landslide hazards or the retrieval of incomplete identification results due to geometric distortion associated with single-track SAR imaging. The combined use of these methods effectively improves the timeliness and verification of the accuracy of landslides. Fifteen landslides were identified in the study area, which had different degrees of tension cracks, vertical dislocations, and slip marks that were verified in the field. Of these, two landslides show serious deformation characteristics that currently pose a serious threat to lives and infrastructure. Follow-up monitoring of these landslides is essential. These findings will assist in obtaining comprehensive information about the distribution of landslides and their deformation developmental trends in the Ahai Reservoir area of the Jinsha River Basin and show that the combined methods can be employed to prevent and control landslides in this area.

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3D displacement and deformation mechanism of deep-seated gravitational slope deformation revealed by InSAR: a case study in Wudongde Reservoir, Jinsha River

Cited by 20 publications

References 26 publications

Enhanced Kinematic Inversion of 3‐D Displacements, Geometry, and Hydraulic Properties of a North‐South Slow‐Moving Landslide in Three Gorges Reservoir

Enhanced Kinematic Inversion of 3‐D Displacements, Geometry, and Hydraulic Properties of a North‐South Slow‐Moving Landslide in Three Gorges Reservoir

Deformation Characteristics and Activation Dynamics of the Xiaomojiu Landslide in the Upper Jinsha River Basin Revealed by Multi-Track InSAR Analysis

Identification and Analysis of Landslides in the Ahai Reservoir Area of the Jinsha River Basin Using a Combination of DS-InSAR, Optical Images, and Field Surveys

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