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Volume 44 Issue 6
Dec.  2025
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Article Navigation >  CARSOLOGICA SINICA  > 2025  >  44(6): 1321-1330, 1343
YAN Haoyuan, FENG Han, XIE Mingli, LIU Zheyan. Analysis of dynamic evolution characteristics of mining-induced landslide based on multi-source remote sensing data: A case study of Jianshanying landslide[J]. CARSOLOGICA SINICA, 2025, 44(6): 1321-1330, 1343. doi: 10.11932/karst20250611
Citation: YAN Haoyuan, FENG Han, XIE Mingli, LIU Zheyan. Analysis of dynamic evolution characteristics of mining-induced landslide based on multi-source remote sensing data: A case study of Jianshanying landslide[J]. CARSOLOGICA SINICA, 2025, 44(6): 1321-1330, 1343. doi: 10.11932/karst20250611
shu

Analysis of dynamic evolution characteristics of mining-induced landslide based on multi-source remote sensing data: A case study of Jianshanying landslide

doi: 10.11932/karst20250611
  • 1.

    Guizhou Provincial First Institute of Surveying and Mapping, Guiyang , Guizhou 550025, China

  • 2.

    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China

  • Received Date: 2025-01-20
  • Accepted Date: 2025-07-21
  • Rev Recd Date: 2025-07-10
    Abstract
  • Landslides occurring on mountain slopes due to deformation and failure of overlying rock masses under the influence of underground mining activities are referred to as mining-induced landslides. Slopes exhibiting deformation caused by such activities but have not yet experienced sliding failure are classified as mining-induced slopes. Mining-induced landslides are characterized by complex deformation and failure mechanisms, as well as prolonged development periods. They cause irreversible damage to the mountain geological environment. These slopes often undergo extended periods of stress adjustment and deformation evolution, leading to the formation of numerous mining-induced fractures within the slope mass. Simultaneously, multiple collapse troughs and subsidence basins develop on the slope surface, significantly reducing the integrity of the rock mass. Under favorable free-face conditions at the front of a slope, sudden-onset geological disasters are highly likely to occur. Mining landslides represent a critical area of research on geological hazards. Analyzing both their surface deformation and historical deformation holds significant importance for the identification and early warning of mining-induced landslides. In recent years, to facilitate convenient and intuitive identification, investigation, and monitoring of geological hazards, technologies such as Spaceborne Interferometric Synthetic Aperture Radar (InSAR), high-resolution optical satellite imagery, Unmanned Aerial Vehicle (UAV) aerial photography, and Airborne Light Detection and Ranging (LiDAR) have been widely used. Notably, combining space-borne, airborne, and ground-based platforms facilitates multi-method, multi-scale, and long-term monitoring of the deformation and failure characteristics of geological hazards. This integrated approach addresses a critical need and represents a key direction for advancing early identification, and disaster prevention and mitigation of landslide hazards. Jianshanying landslide is located in Fa’er Town, Liupanshui City, Guizhou Province. The landslide site has experienced deformation for many years due to repeated multi-seam mining operations. Ultimately, it slid in September 2020 as a result of the combined effects of long-term mining activities and rainfall. This landslide is a typical example of mining landslide occurring in the karst terrain in Guizhou Province. This study adopts an integrated space-air-ground approach to conduct a multi-dimensional analysis of the historical dynamic changes of Jianshanying landslide. Research shows that mining-induced landslides like Jianshanying landslide, caused by repeated multi-seam mining, exhibit significant deformation magnitudes. Prolonged mining activities combined with rainfall increase the susceptibility to landslides. Comparative analysis of multi-temporal optical remote sensing imagery and UAV data can reveal the macro-scale deformation variations at different locations of the landslide over time. Long time-series Synthetic Aperture Radar (InSAR) technology can accurately capture the spatio-temporal evolution patterns of surface deformation. Additionally, surface point cloud data acquired by airborne LiDAR can provide detailed information on elevation changes and micro-geomorphological characteristics of the landslide. Through a comprehensive interpretation of the aforementioned remote sensing means, combined with field investigations and analysis of the landslide’s engineering geological and mining conditions, the following conclusions are established: as early as before 2013, Jianshanying landslide exhibited relatively obvious disaster features, such as the main scarp, landslide boundaries, and terraces. With the progression of mining activities, by 2016, Jianshanying landslide gradually transformed from multiple deformation bodies into a single integrated mass. Rear tension cracks appeared around 2016 as visibly open fissures. After 2017, these cracks accelerated in both widening and extension. The entire mass displaced downward, resulting in local collapses. Ultimately, under the combined action of long-term repeated mining and rainfall, the landslide occurred in 2020. The deformation of Jianshanying landslide experienced four stages: caving-subsidence deformation, tensile cracking deformation, creep deformation, and shear sliding failure. From the perspective of movement mechanism, it is a typical retrogressive landslide (push-type landslide).

     

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