In recent years, due to adverse geological conditions, intense human engineering activities, and extreme weather conditions, catastrophic landslides have frequently occurred in southwest China, causing severe loss of life and property. Identifying the kinematic features of potential landslides can effectively support landslide hazard prevention. This study proposes a remote sensing identification method for rotational, planar traction, and planar thrust slides based on geomorphic features as well as vertical and slope-oriented deformation rates. Rotational landslides are characterized by similar vertical and horizontal deformation rates, with vertical deformation mainly occurring at the head and gradually decreasing along the slope, while horizontal deformation mainly occurs at the foot and gradually increases along the slope. As for the planar slide, the dominant deformation is in the horizontal direction. It is further classified into the planar traction and planar thrust types according to the driving position. The vertical deformation of planar traction slides is concentrated at the foot, while the vertical deformation of planar thrust slides is concentrated at the head of the landslide. We identified 1 rotational landslide, 10 planar traction landslides and 10 planar thrust landslides in the basalt weathering crust area of Guizhou. Field investigations of three landslides verified the method’s accuracy. Combining two-dimensional rainfall and time-series deformations, we found that there is a significant positive correlation between landslide deformation acceleration and precipitation. The landslide kinematic identification method proposed in this paper overcomes the shortcomings of the inability to accurately characterize landslide motion by line-of-sight displacement and realizes the non-contact identification of active landslide motion patterns, which is an essential reference value for geological disaster prevention and control in the study area.

The Zongling collapse group of Guizhou Province, located in southwestern China, is characterized by ‘frequent damaging slope failures’ that threaten the surrounding area. Its geological and topographical features are both typical and representative of southwestern China. Based on high-resolution imagery from unmanned aerial vehicles and field investigations, this study identifies the deformation characteristics of three collapse failure modes: shearing, toppling and falling. Furthermore, it proposes the evolutionary mechanisms of high and steep rock slope failures. The paper demonstrates the benefits of the combination of unmanned aerial vehicles and field inspection in establishing failure mechanisms.

Mining-induced geological hazards in the mountainous regions are often characterized by wide impact zones and complex subsurface structures, which pose significant challenges for the precise identification of landslides and the analysis of their formation mechanisms. To address this issue, we takes the Leji landslide in southwestern China as a case study and integrates SBAS-InSAR technology, two-dimensional decomposition modeling, UAV photogrammetry, field geological investigation, and AMT sounding to establish a multidimensional “Space-Air-Ground-Subsurface” detection strategy. This multi-dimensional framework enables the systematic acquisition of both surface deformation and subsurface structural information of the Leji landslide, thereby elucidating its controlling factors and causative mechanisms. The results reveal that the central parts of Landslide I and Landslide II exhibit the most significant deformation, with surface displacement dominated by downslope subsidence. The maximum annual average subsidence rates range between –60 mm/y and –80 mm/y. The cumulative deformation zones retrieved by SBAS-InSAR closely coincide with the mining areas detected by AMT. Through data fusion, the boundary angles of the mining areas were determined as 77° in the upslope direction and 48° in the downslope direction along the dip, and 77° and 55° in the strike direction. Comprehensive analysis indicates that the Leji landslide is a Quaternary soil creep landslide formed under the combined influence of fault–fold structures, frequent heavy rainfall, and both open-pit and underground mining activities, and it remains in an active state. This study demonstrates that the “Space-Air-Ground-Subsurface” collaborative observation system effectively overcomes the limitations of single techniques in landslide mechanism research, providing a reliable technical pathway and scientific basis for understanding the development mechanisms and disaster risk mitigation of mining-induced landslides.