A new approach to assess landslide susceptibility based on slope failure mechanisms

Qiang, Zhimin; Hu, Juyang; Cui, Peng; Zhou, Bin; Jiang, Yao; Qin, Mingyue; Liu, Yanguo; Liu, Cong

doi:10.1016/j.catena.2021.105388

Cited by 47 publications

(20 citation statements)

References 63 publications

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“…This observation clearly shows the influence of the orientation parameters of the satellite LOS on the effectiveness of the InSAR technology in monitoring the ground surface deformation, and it also highlights the significance of the conversion analysis of the surface deformation rates of the TS-InSAR analysis results in landslide identification. According to the landslide source areas of Dadu River Basin identified by Zou et al [49] (see Figure 12a), landslides are widely distributed in the study area. As the TS-InSAR technique requires input SAR image pairs with good coherence, it is suitable for detecting slow-moving landslides.…”

Section: Discussionmentioning

confidence: 95%

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An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

et al. 2022

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The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

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Section: Discussionmentioning

confidence: 95%

“…Hence, the combined use of multiple remote sensing technologies may provide a promising approach for detecting multiple types of landslides. [49]); (b) InSAR-derived LOS deformation rate maps for the upper reach of the Dadu River Basin (modified from [46]) (note: white boxes contain the names of the detected landslides).…”

Section: Discussionmentioning

confidence: 99%

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

et al. 2022

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“…To solve the above problems, some simplified methods based on empirical [33,34] or semi-empirical [35,36] approaches have been proposed. The Flow-R, a distributed empirical model for the regional assessment of gravitational hazards [37], has been widely used in landslide susceptibility mapping by scholars due to its low data requirements and openness [38,39]. The propagation area is simulated by frictional laws and flow direction algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Simple Method of Mapping Landslides Runout Zones Considering Kinematic Uncertainties

Liu

Gao

et al. 2022

Remote Sensing

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Landslides can be triggered by natural and human activities, threatening the safety of buildings and infrastructures. Mapping potential landslide runout zones are critical for regional risk evaluation. Although remote sensing technology has been widely used to discover unstable areas, an entire landslide runout zone is difficult to identify using these techniques alone. Some simplified methods based on empirical models are used to simulate full-scale movements, but these methods do not consider the kinematic uncertainties caused by random particle collisions in practice. In this paper, we develop a semi-empirical landslide dynamics method considering kinematic uncertainties to solve this problem. The uncertainties caused by the microtopography and anisotropy of the material are expressed by the diffusion angle. Monte Carlo (MC) simulations are adopted to calculate the probability of each cell. Compared with the existing Flow-R model, this method can more accurately and effectively estimate runout zones of the Yigong landslide where random particle collisions are intense. Combining the D-InSAR technique, we evaluate the runout zones in the Jinsha River from June 2019 to December 2020. This result shows that the method is of great significance in early warning and risk mitigation, especially in remote areas. The source area of the landslide and DEM resolution together affect the number of MC simulations required. A landslide with a larger volume requires a larger diffusion angle and more MC simulations.

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“…Currently, many studies on ecological sensitivity evaluation have been reported and were mainly focused on the following aspects: (1) ecological sensitivity evaluation of a certain region, such as Jianghan Plain, China [1], Three Gorges Reservoir Area in China [2], Denizli Province, Turkey [3], Pingtan Island (China) and Nile Delta (Egypt) [4], along the Yangtze River in China [5], etc. ; (2) Special research should be conducted on sensitive factors, such as soil erosion [6][7][8], geological disasters [9][10][11][12], cultivated land resources [13][14][15], and water resources [16][17][18]; (3) Spatial planning or spatial pattern optimization based on ecological sensitivity analysis, such as the optimization of spatial patterns of resource-based cities [19], optimization of land use spatial patterns [20], urban development boundary planning [21], landscape spatial planning [22], etc. ; (4) Ecological sensitivity evaluations of specific projects, such as power grid projects [23], airport reclamation projects [24], and hydroelectric power plant projects [25].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method and Application of Ecological Sensitivity of Intercity Railway Network Planning

2022

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In the planning stage of the intercity railway network, the ecological sensitivity evaluation of the planning scheme is not only the key content to explore the ecological environmental rationality of the planning scheme but also a scientific means to promote the sustainable development of intercity railway networks. The purpose of this study is to establish an evaluation method that can quantitatively evaluate the ecological sensitivity of intercity railway network planning to put forwards targeted optimization and adjustment suggestions for the planning scheme. Taking the intercity railway network planning of Guizhou Province as an example, its ecological sensitivity is predicted and evaluated. Six types of ecologically sensitive areas were selected as ecological sensitivity evaluation factors, including protected areas, drinking water sources, geological disaster-prone areas, soil erosion areas, cultivated land resource distribution areas and coal resource distribution areas. Based on the GIS overlay method, the quantitative measurement methods of each evaluation factor are established in turn, and the single factor sensitivity evaluation index is obtained. In addition, the weighted superposition model is used to quantitatively calculate the ecological sensitivity of the planned lines of the intercity railway network in Guizhou Province. Finally, the short board factor of each planned line is obtained, and targeted optimization and adjustment suggestions are put forwards. The research content of this paper can provide a theoretical reference for the practical evaluation of the ecological sensitivity of intercity railway network planning.

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A new approach to assess landslide susceptibility based on slope failure mechanisms

Cited by 47 publications

References 63 publications

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

A Simple Method of Mapping Landslides Runout Zones Considering Kinematic Uncertainties

Evaluation Method and Application of Ecological Sensitivity of Intercity Railway Network Planning

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