Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China

Abstract

Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir.

Characterizing the Development Pattern of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges Reservoir

“…Second, groundwater seepage caused by the drawdown of the reservoir water level decreased landslide stability. When the reservoir level drops rapidly, the dissipation of pore water pressure in the landslide body is slower than the decrease of external hydrostatic pressure [18,43,44]. The transient seepage induced by this lagging effect creates seepage force along the sliding direction [45], which increased the slip force and further reduced slope stability.…”

“…Although colluvial landslides show evidence of deformation at low velocities, in situ monitoring is helpful in detecting their temporal evolution [13,14]. Some studies have shown that remotely sensed techniques also play an essential role in the observation of landslide deformation [15][16][17][18]. Capturing the relationship between the hydrological and kinematic responses of these slow-moving colluvial landslides is key to analyzing their development [19,20].…”

Knowledge Discovery of Global Landslides Using Automated Machine Learning Algorithms

“…ML has been used in landslide research for landslide detection [7], characterization [8], susceptibility assessment [9]- [11], prediction [12], and early warning systems [13]. The most common ML algorithms used are support vector machines [7], [9]- [11], followed by logistic regression [7], [10], [11], random forests [7], [9], artificial neural networks [9], [11], and Bayesian networks [9], [10].…”

Multi-Sensor InSAR Assessment of Ground Deformations around Lake Mead and Its Relation to Water Level Changes

“…Ground deformation resulting from variations in the volume of surface water resources, such as impounded reservoirs, can also trigger landslides and sudden mass movements; if these feed into lakes. Associated large surges of water can lead to dam instability, threatening lives and properties along the shores [9][10][11][12].…”