Landslide Assessment and Monitoring along the Jinsha River, Southwest China, by Combining InSAR and GPS Techniques

• Published in: Journal of sensors 2022-02, Vol.2022, p.1-16
• Main Authors: Ma, Yuanyuan, Li, Fei, Wang, Zemin, Zou, Xiaoqing, An, Jiachun, Li, Bing
• Format: Article
• Language: English
• Subjects: Global positioning systems; GPS; Hydroelectric power; Hydroelectric power stations; Hydrology; Interferometric synthetic aperture radar; Interferometry; Landslides; Landslides & mudslides; Monitoring; Mountains; Movement; Precipitation; Rain; Remote sensing; Rough terrain; Stability analysis; Time series; Unmanned aerial vehicles; Water level fluctuations
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2022/9572937

This paper focuses on ancient landslides located along the Jinsha River between the Ahai hydropower station (AHHs) and Liyuan hydropower station (LYHs). High-precision landslide monitoring and accurate understanding of inducing factors are important for landslide stability analysis. However, it is often difficult to monitor and analyze landslide movement due to rough terrain and the complex inducing factors in the mountain area. In this paper, the ancient landslides can be monitored by distributed scatterers-based interferometric synthetic aperture radar (DS-InSAR) technology and global positioning systems (GPS). DS-InSAR can obtain enough measurement points based on the persistent scatterers and distributed scatterers. Besides, we present the results of GPS measurement as a comparison and supplement to DS-InSAR. Our results illustrate that DS-InSAR measurement and GPS measurement show high-level consistency. To comprehensively analyze the triggering factors of landslide deformation, we derive the spatiotemporal movement characteristics of the XinJian (XJ) landslide and find that the XJ landslide movement is very nonuniform, which is closely related to soil weathering. The XJ landslide movement undergoes periodic acceleration. We deduce that the motion of the landslide may be affected by precipitation and water level fluctuation and indicate that the combination of precipitation and water level fluctuation is the most serious triggering factor. During the period from August to September, the rate of landslide movement reached a peak value, which was highly consistent with the precipitation and water level records. Additionally, the rescaled range method (R/S) is used to analyze the stability of the XJ landslide. The results show that hydrological conditions are an essential factor in the stability of the landslide. In other words, the more precipitation there is, the larger the water level changes and the more unstable the landslide.