Model test study on monitoring dynamic process of slope failure through spatial sensor network

Lü, Ping; Wu, Hangbin; Qiao, Gang; Li, Weiyue; Scaioni, Marco; Feng, Tiantian; Liu, Shijie; Chen, Wen; Li, Nan; Liu, Chun; Tong, Xiaohua; Hong, Yang; Li, Rongxing

doi:10.1007/s12665-015-4369-8

Cited by 32 publications

(14 citation statements)

References 52 publications

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“…To monitor the landslide with geotechnical equipment, a Spatial Sensor Network similar to the MUNOLD (Multi-Sensor Network for Observing Landslide Disaster) raised by Lu et al [30] is currently being deployed in Xishancun Landslide. There are four boreholes (Bh1, Bh2, Bh3 and Bh4) located on the lower and middle parts of the landslide and distributed evenly along the slope ( Figure 2).…”

Section: Geotechnical Monitoringmentioning

confidence: 99%

Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring

Lü

Chun

et al. 2016

Remote Sensing

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Early detection and early warning are of great importance in giant landslide monitoring because of the unexpectedness and concealed nature of large-scale landslides. In China, the western mountainous areas are prone to landslides and feature many giant complex landslides, especially following the Wenchuan Earthquake in 2008. This work concentrates on a new technique, known as the "hybrid-SAR technique", that combines both phase-based and amplitude-based methods to detect and monitor large-scale landslides in Li County, Sichuan Province, southwestern China. This work aims to develop a robust methodological approach to promptly identify diverse landslides with different deformation magnitudes, sliding modes and slope geometries, even when the available satellite data are limited. The phase-based and amplitude-based techniques are used to obtain the landslide displacements from six TerraSAR-X Stripmap descending scenes acquired from November 2014 to March 2015. Furthermore, the application circumstances and influence factors of hybrid-SAR are evaluated according to four aspects: (1) quality of terrain visibility to the radar sensor; (2) landslide deformation magnitude and different sliding mode; (3) impact of dense vegetation cover; and (4) sliding direction sensitivity. The results achieved from hybrid-SAR are consistent with in situ measurements. This new hybrid-SAR technique for complex giant landslide research successfully identified representative movement areas, e.g., an extremely slow earthflow and a creeping region with a displacement rate of 1 cm per month and a typical rotational slide with a displacement rate of 2-3 cm per month downwards and towards the riverbank. Hybrid-SAR allows for a comprehensive and preliminary identification of areas with significant movement and provides reliable data support for the forecasting and monitoring of landslides.

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Section: Geotechnical Monitoringmentioning

confidence: 99%

Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring

Lü

Chun

et al. 2016

Remote Sensing

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“…For real-time monitoring and early warning purposes, this platform was designed and implemented to include an artificial rainfall system, a sensor network, a subsystem for data collection and communication, a data server for storage, and a screen panel for visualization, for more details readers can refer to Qiao et al [27] and Lu et al [64]. The sensor network contains contact sensors that were installed in the landslide mass and used to record the environmental conditions to derive the geotechnical parameters of the landslide body, and the detailed research and discussion in this aspect can be found in [28,64]. On the other hand, the non-contact/imaging sensors (cameras and video) capture the geometric changes in the slope surface during a simulated landslide deformation process.…”

Section: Landslide Platform Set Up and Simulation Experimentsmentioning

confidence: 99%

Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide

Qiao

Feng

et al. 2016

Remote Sensing

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Landslides are one of the most destructive geo-hazards that can bring about great threats to both human lives and infrastructures. Landslide monitoring has been always a research hotspot. In particular, landslide simulation experimentation is an effective tool in landslide research to obtain critical parameters that help understand the mechanism and evaluate the triggering and controlling factors of slope failure. Compared with other traditional geotechnical monitoring approaches, the close-range photogrammetry technique shows potential in tracking and recording the 3D surface deformation and failure processes. In such cases, image matching usually plays a critical role in stereo image processing for the 3D geometric reconstruction. However, the complex imaging conditions such as rainfall, mass movement, illumination, and ponding will reduce the texture quality of the stereo images, bringing about difficulties in the image matching process and resulting in very sparse matches. To address this problem, this paper presents a multiple-constraints based robust image matching approach for poor-texture close-range images particularly useful in monitoring a simulated landslide. The Scale Invariant Feature Transform (SIFT) algorithm was first applied to the stereo images for generation of scale-invariate feature points, followed by a two-step matching process: feature-based image matching and area-based image matching. In the first feature-based matching step, the triangulation process was performed based on the SIFT matches filtered by the Fundamental Matrix (FM) and a robust checking procedure, to serve as the basic constraints for feature-based iterated matching of all the non-matched SIFT-derived feature points inside each triangle. In the following area-based image-matching step, the corresponding points of the non-matched features in each triangle of the master image were predicted in the homologous triangle of the searching image by using geometric constraints, followed by a refinement course with similarity constraint and robust checking. A series of temporal Single-Lens Reflex (SLR) and High-Speed Camera (HSC) stereo images captured during the simulated landslide experiment performed on the campus of Tongji University, Shanghai, were employed to illustrate the proposed method, and the dense and reliable image matching results were obtained. Finally, a series of temporal Digital Surface Models (DSM) in the landslide process were constructed using the close-range photogrammetry technique, followed by the discussion of the landslide volume changes and surface elevation changes during the simulation experiment.

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“…The average thickness of the sliding body is 55m and the scale amounted to 1.7×10 8 m³. A Spatial Sensor Network MUNOLD (Lu et al 2015) is currently being deployed in monitoring this landslide. Figure 3 shows the deformations rates acquired from the SBAS technique over the dataset-acquired period for Xishancun landslide.…”

Section: Geological Setting Of the Xishancun Landslidementioning

confidence: 99%

Application of Time Series Insar Technique for Deformation Monitoring of Large-Scale Landslides in Mountainous Areas of Western China

Lü

Liu

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Western China is very susceptible to landslide hazards. As a result, landslide detection and early warning are of great importance. This work employs the SBAS (Small Baseline Subset) InSAR Technique for detection and monitoring of large-scale landslides that occurred in Li County, Sichuan Province, Western China. The time series INSAR is performed using descending scenes acquired from TerraSAR-X StripMap mode since 2014 to get the spatial distribution of surface displacements of this giant landslide. The time series results identify the distinct deformation zone on the landslide body with a rate of up to 150mm/yr. The deformation acquired by SBAS technique is validated by inclinometers from diverse boreholes of in-situ monitoring. The integration of InSAR time series displacements and ground-based monitoring data helps to provide reliable data support for the forecasting and monitoring of largescale landslide.

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Model test study on monitoring dynamic process of slope failure through spatial sensor network

Cited by 32 publications

References 52 publications

Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring

Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring

Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide

Application of Time Series Insar Technique for Deformation Monitoring of Large-Scale Landslides in Mountainous Areas of Western China

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