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The topography and landforms of Guizhou Province in China are complicated, and the climatic conditions of heavy precipitation make landslide disasters in Guizhou Province occur frequently. To avoid damage to people's lives and economic property caused by disasters, a reliable early landslide identification method and landslide monitoring method are urgently needed. Traditional landslide identification and monitoring methods have limitations. InSAR technology has unique advantages in large-scale landslide identification and monitoring, but landslide identification results based on a single deformation value are one-sided. Therefore, this paper uses Sentinel-1A radar satellite image data and uses InSAR technology and optical remote sensing technology to carry out large-scale surface deformation monitoring and identification of dangerous deformation areas in Liupanshui City, Tongren City, Guiyang City and other regions in Guizhou Province. The potential landslide identification methods based on the time series normalized difference vegetation index and landslide development environment elements are combined to investigate hidden landslide hazards in the study area. In this paper, time series InSAR technology is used to monitor three key landslides in Jichang Town, Yujiaying and Fana, to grasp the movement status of the landslide in time. The method of landslide identification and monitoring in this paper is of great significance for disaster prevention and management in Guizhou Province. [ABSTRACT FROM AUTHOR]

The Jianshanying landslide in Guizhou province, China, is a typical mining-induced landslide with its complex formation in karst mountainous area. However, the spatiotemporal deformation characteristics and failure mechanism are still unknown. In this paper, the surface deformation time series from 2017 to 2020 is firstly estimated by combining multi-temporal synthetic aperture radar (SAR) interferometry and improved offset-tracking methods to overcome the difficulties caused by the large gradient surface deformation and surface material changes. Then, the spatiotemporal characteristics of Jianshanying landslide is analyzed in terms of line-of-sight (LOS) deformation with stacking InSAR method and two-dimensional (2D) deformation with improved SAR offset-tracking method. And the different deformation features between sliding body and surrounding areas are uncovered. Accordingly, we point out the "three-section" surface deformation model. We further analyze the deformation time series by considering the joint effects of rainfall and underground mining. Finally, we conceptualize the landslide failure mode for the Jianshanying landslide, which can be extended to the similar landslide in karst mountainous areas of southwestern China. [ABSTRACT FROM AUTHOR]

The western part of Guizhou is located in the second step of East Asia. Although the area is stratigraphically continuous and the surface is dominated by hard limestone and sandstone, catastrophic landslides often occur, seriously threatening residents' lives and the safety of property. Accurate identification of landslides and analysis of their developmental patterns are vital to prevent and reduce the threat of geological disasters. No active landslide survey data cover this region, so this paper identifies the active landslides in the western part of Guizhou by combining surface deformation information, multitemporal optical remote sensing images, geological lithology, and geomorphic features to obtain deformation information from multisource synthetic aperture radar surface data. This process increases the accuracy and reliability of identifying unstable slopes in areas with dense vegetation and steep terrain. By processing 283 Sentinel-1 and PALSAR-2 synthetic aperture radar data, 578 active landslides, 18 of which are high-risk large-scale landslides (landslide groups), are delineated for the first time in a range of 4.64 × 104 km2 in the study area. The active landslides mainly include natural landslides, reservoir landslides, and mining-induced landslides, accounting for 2.4%, 4.2 %, and 93.4%, respectively. The spatial distribution of landslides is banded along the cuesta at the edge of an outcrop of coal strata. Landslides are mainly distributed at elevations of 1800–2000 m, with an elevation difference of 50 ~ 100 m and a slope range of 35° ~ 40°. The landslides are characterized by steep slopes, small scales, mass occurrences, and no dominant slope direction, classifying them as cuesta landslides induced by mining disturbance. Furthermore, nuanced remote sensing interpretation of the disaster elements, such as cuesta cliff, tensile cracks, deep and sizeable tensile channels, isolated rock masses, and collapse debris, and their processes of change, reveals that coal mining-disturbed landslides in this region have experienced four primary stages: natural unloading, mining disturbance, displacement acceleration, and slope failure. This is of great significance for understanding the genetic mechanism and developmental patterns, as well as the risk assessment, of this region. [ABSTRACT FROM AUTHOR]

Most of the coal mines in Southwest China are located in mountainous areas with high vegetation coverage, and most activities are carried out under the mountains. The deformation monitoring and mechanical behavior analysis of the mining area helps reveal the typical mountain deformation and failure mechanism caused by underground mining activities and reduce the risk of mountain collapse in the mining area. In this manuscript, a research method for mountain stability in mining areas is proposed, which combines InSAR deformation monitoring with numerical analysis. Based on the high-precision deformation information obtained by DS-InSAR and the landslide range, a three-dimensional explicit finite difference numerical analysis method was used to reconstruct the landslide model. According to the layout of the coal mining working face, the variation mechanism of overlying stratum stress and the mountain slip in the coal mining process is inverted, and the mechanism of mountain failure and instability in the mining area is analysed. Based on the sentinel data, the experiment performed time series monitoring and inversion analysis of the mountain collapse in Nayong, Guizhou, China. The results show that mining activities a certain distance from the mountain will affect mountain stability, and there are specific mechanisms. From 2015 to 2017, the stress redistribution of overlying strata above the goaf area resulted in dense longitudinal cracks in the landslide body due to coal mining. The mountain is in a continuous damage state, and the supporting force to prevent collapse continues to decrease, resulting in a gradual decrease in landslide stability. Both the time series DS-InSAR monitoring results and numerical simulation results verify the actual occurrence and development of the on-site subsidence. [ABSTRACT FROM AUTHOR]

The karst landscape is widespread in the southern region of China. As a result of underground mining activities, the original stress equilibrium is disrupted, causing the redistribution of stress in the overlying rock layer, inducing the longitudinal fracture of mining to expand and penetrate upwards, resulting in the rupture and destabilization of the karst cave roof, thus triggering a series of engineering problems such as karst cave collapse, landslide, the discontinuous deformation of the ground surface, and soil erosion. In order to study the evolutionary characteristics of buried rock fissures in shallow coal seam mining under the karst landform, taking the shallow coal seam with the typical karst cave development landform in Guizhou as the engineering background, based on the similarity simulation experiment and fractal theory, the evolution law of buried rock fissures and network fractal characteristics under the disturbance of the karst landform mining are analyzed. The research shows that the mining-induced fracture reaches the maximum development height of 61 m on the left side of the cave, and the two sides of the cave produce uncoordinated deformation. The separation fracture below the cave is relatively developed, and the overall distribution pattern of the cave rock fracture network presents a "ladder" shape. The correlation coefficient of the fractal dimension of the rock fractures under different advancing distances is more than 0.90, and the rock fracture network under the karst landform has high self-similarity. The variation of fractal dimension with the advancing degree of the working face can be divided into four stages. The first and second stages show an exponential growth trend, and the third and fourth stages show linear changes with slopes of 0.0007 and 0.0014, respectively. The fluctuation of the fractal dimension is small. The periodic weighting of the upper roof in the cave-affected zone is frequent, the fragmentation of the fractured rock mass becomes larger, and the fractures of the upper rock mass are relatively developed. The research results can provide a reference for the study on the evolution law of mining-induced rock fissures under similar karst landforms. [ABSTRACT FROM AUTHOR]

On July 10, 2020, a catastrophic landslide with a slope angle of only 8° occurred in the red-bed series strata in Qianxi, Guizhou, southwestern China, due to prolonged rainfall, with a volume of around 12 million m3. The landslide did not exhibit significant surface deformation (sliding distance 2.0–19.2 m) when it occurred, instead of starting a subsequent creep slide lasting several months (sliding distance 16.3–48.0 m), and the rupture surface at the slope toe remained absent, which may imply a unique mechanism. To investigate the causes, unmanned aerial vehicle (UAV) images, electrical resistivity tomography (ERT), drilling, and inclinometer monitoring have been employed to analyze the surface deformation and the internal structure of the landslide. Moreover, slake durability tests are conducted to help analyze its possible failure mechanism. Preliminary analysis shows that the rising water table induced by rainfall and softening-disintegration of red-bed rock at the slip zone caused this landslide. Based on the borehole data, ERT data, and the deformation characteristics of the houses, it is speculated that there are two sliding surfaces located at different depths. The overall gentle slope structure and the local water-insulated limestone layer largely account for the bulging at the landslide toe and the formation of two sliding surfaces, respectively. The results provide a reference for understanding the similar low-angle landslides in red-bed series strata. [ABSTRACT FROM AUTHOR]

The Pusa landslide, in Guizhou, China, occurred on 28 August 2017, caused 26 deaths with 9 missing. However, few studies about the pre-event surface deformation are provided because of the complex landslide formation and failure mechanism. To retrieve the precursory signal of this landslide, we recovered pre-event deformation with multi-sensor synthetic aperture radar (SAR) imagery. First, we delineated the boundary and source area of the Pusa landslide based on the coherence and SAR intensity maps. Second, we detected the line-of-sight (LOS) deformation rate and time series before the Pusa landslide with ALOS/PALSAR-2 and Sentinel-1A/B SAR imagery data, where we found that the onset of the deformation is four months before landslide event. Finally, we conceptualized the failure mechanism of the Pusa landslide as the joint effects of rainfall and mining activity. This research provides new insights into the failure mechanism and early warning of rock avalanches. [ABSTRACT FROM AUTHOR]