Comprehensive Remote Sensing Technology for Monitoring Landslide Hazards and Disaster Chain in the Xishan Mining Area of Beijing

Abstract

The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used multi-temporal satellite images to extract this region’s deformation information, identify landslides and analyze the deformation evolution process of these landslides. Taking the Anzigou ditch as an example, we investigate the “Quarry–Landslide–Mudslide” disaster chain model. We found that the landslide evolution process is closely related to the geological conditions, and usually goes through four stages: initial deformation, slope front swelling and collapsing, rear part connecting and rupturing, and landslide creeping. The surface deformation can be identified and tracked by high-resolution optical images and InSAR monitoring. Under the combined effects of rainfall and topographic conditions, medium and large landslides may occur and trigger a “Quarry–Landslide–Mudflow” disaster chain. The identification and analysis of these landslide hazards and the disaster chain help with geological disaster prevention, and provide reference for early identification and research of similar disasters.

Assessment of geological hazard susceptibility based on remote sensing images

“…Repeated flights of UAVs and simultaneous intensive measurements by Kyriou et al using a network of GNSS during a specific period can ensure comprehensive monitoring of landslide activity and reduce the risk of possible reactivation [5]. In studying the evolution of landslides, the use of multi-temporal satellite imagery by Jiao et al enables a more comprehensive extraction of deformation information in the area and the identification of landslides [12]. Monitoring and assessment results from remote sensing techniques can be supplemented by utilizing relevant information from historical maps and literature to facilitate a more comprehensive understanding of the extent of geomorphic disaster impacts and historical landslide events in the study area [10].…”

“…Therefore, the accuracy of optical-based automatic identification methods for landslide recognition is limited. Since certain landslide features can only be detected with the support of three-dimensional space, stereoscopic vision is often utilized to aid in this process [12,32]. After visually interpreting the remotely sensed imagery, Xu et al demarcated the location and boundaries of each landslide and conducted it partially.…”

Geological Hazard Identification and Susceptibility Assessment Based on MT-InSAR

“…Following a review of the relevant literature and considering the topography of the study area, geology, human activities, climate and other factors, 16 geological hazardinfluencing factors were selected [35][36][37][38][39]. These included elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), slope length and slope angle factor (LS-factor) [40], precipitation, normalized difference vegetation index (NDVI), distance to rivers, distance to roads, landcover, distance to faults, stratigraphic unit, and distance to mine origin.…”

A Method for Predicting Landslides Based on Micro-Deformation Monitoring Radar Data

“…In recent years, open-pit mining engineering activities have been expanding on a large scale along with the rapid development of mining technology and the continuous growth in global raw material demand, resulting in a sharp increase in the number of high and steep slopes in mining areas [5][6][7][8]. According to incomplete statistics, there are currently 10,100 non-coal metal mines and 73,548 nonmetallic mines in China, and from 2001 to 2007, there were 1951 landslide accidents in metal and non-metal open-pit mines, with 3065 casualties [9][10][11][12]. The potential safety hazards on the rock slopes of open-pit mines represent the core problem of mine safety production.…”