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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]

Rapid detection of landslides using remote sensing images plays a key role in hazard assessment and mitigation. Many deep convolutional neural network-based models have been proposed for this purpose; however, for small-scale landslide detection, excessive convolution and pooling process may cause potential texture information loss, which can lead to misclassification of landslide target. In this paper, we present a novel UNet model for the automatic detection of landslides, wherein the reversed image pyramid features (RIPFs) are adapted to mitigate the information loss caused by a succession of convolution and pooling. The proposed RIPF-Unet model is trained and validated using the open-source landslides dataset of the Bijie area, Guizhou Province, China, wherein the precision of the proposed model is observed to increase by 3.5% and 4.0%, compared to the conventional UNet and UNet + + model, respectively. The proposed RIPF-Unet model is further applied to the case of the Longtoushan region after the 2014 Ms.6.5 Ludian earthquake. Results show that the proposed model achieves a 96.63% accuracy for detecting landslides using remote sensing images. And the RIPF-Unet model is also advanced in its compact parameter size; notably, it is 31% lighter compared to the UNet + + model. [ABSTRACT FROM AUTHOR]

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A large catastrophic landslide occurred on July 23, 2019, after a continuous heavy rainfall at Jichang (N 26°15′22.99″, E 104°40′6.33″), Shuicheng county, Guizhou province, China. This landslide had a maximum runout distance of 1295 m, a maximum falling height of 477 m between the highest point of the source area and the leading edge, and a maximum speed of 45.5 m/s. The 1.4 million cubic meters of debris instantly buried twenty-one buildings, destroyed six buildings, and resulted in 42 fatalities and 9 people missing. This paper analyzes the extent and mechanisms of initiation and runout distance of the landslide. The landslide occurred in the eluvial and deluvial accumulation of basalt, and its initiation mechanisms were different from the bedrock landslides that are typical of the interbedded soft and hard rocks in Guizhou and basalt landslides in Southwest China. The prolonged heavy rainfall, lush vegetation, and road construction played the roles in landslide initiation. Subsequently, the landslide can be divided into three sliding regions caused by the valley on both sides and the stepped terrain in the middle, and the valley topography enhanced the disaster intensity and reach. [ABSTRACT FROM AUTHOR]

The Zongling landslide (Nayong, Guizhou, China) is dominated by a unique karst landscape area with many landslide masses. In this paper, an improved Interferometric Point Target Analysis (IPTA) method is proposed to identify and monitor the Zongling landslide. In this method, the Anderson-Darling test is applied to distributed scatterer (DS) selection, and DS and persistent scatterer (PS) are combined to improve the density of measurement points in vegetation area. Moreover, this method is also characterized by the appropriate combination of differential interferograms produced by a small baseline subsets and the employment of the phase triangulation algorithm to estimate the optimal phase. Combining 105 scenes of C-band Sentinel-1A ascending and descending data acquired during 2014–2018, the method is applied to retrieve time series displacement for the large-scale landslide in Zongling Town. Finally, three accelerating landslides are identified from our result, which is consistent with ALOS PALSAR differential interferometry synthetic aperture radar (DInSAR) results and field investigation. The influencing factors and deformation mechanism of the Zongling landslide are also analysed. Our monitoring results will help the local government to conduct regular inspections and strengthen disaster prevention in mountain areas. [ABSTRACT FROM AUTHOR]

This paper mainly explores the deformation instability mechanism and influencing factors of landslide in a town called Fa'er, in southwestern China's Guizhou Province. Drawing on the theories of engineering geological mechanics, the exploration was carried out through the analysis of geological data, field investigation, and drone survey. The results show that: The mining of underground coal seams led to multiple tensile cracks on the trailing edge; the fragmented rock and soil mass, as well as the free face on the front edge create a favorable landform for initiating the landslide; the landslide is mainly induced by the heavy rainfall for several consecutive days; under the continuous heavy rainfall, the creep shear zone was penetrated by cracks, and the landslide was pulled by gravity to slide; the deformation mode shifted from creeping-tensile cracking to plastic flow-tensile cracking. [ABSTRACT FROM AUTHOR]

Large rocky landslides induced by underground mining are the primary type of geological disaster in the karst mountains of southwestern China. In this study, by integrating field surface surveys, interferometric synthetic aperture radar (InSAR) monitoring, and discrete element simulations, we analyzed the deformation evolution of steep cliffs with karst structural planes under mining disturbance. Based on the stress transfer, deformation response, and instability precursors, a landslide instability model was developed. The results revealed that the instability boundaries and the instability mode were controlled by the large structural plane at the top of the mountain. Underground mining accelerated the uneven subsidence of the overlying strata, which led to expansion and penetration of the karst fissure structural planes and fracturing of the rock base, which are the main reason for the instability of the mountain. Underground mining caused internal stress fluctuation of the overlying strata, and the mountain experienced four stages during this process, i.e., development of karst fissure structural planes, fissures expansion and rock base fracturing, further fissures expansion and rock base crashing, and fissures penetration and shear failure. The mining-induced failure of mountain with large karst structural planes can be described as tensile shear-fracturing crashing failure. The InSAR monitoring results revealed that accelerated deformation occurred in the source area starting 20 days before the Baiyan landslide. In addition, 25 potential deformation landslides were identified in Zhijin County, Guizhou Province. Hence, InSAR technology can be used for early-stage detection and warning of similar landslides. [ABSTRACT FROM AUTHOR]

The extraction of underground minerals in hilly regions is highly susceptible to landslides, which requires the application of InSAR techniques to monitor the surface displacement. However, repeated mining for multiple coal seams can cause a large displacement beyond the detectable gradient of the traditional InSAR technique, making it difficult to explore the relationship between landslides and subsurface excavations in both temporal and spatial domains. In this study, the Tengqing landslide in Shuicheng, Guizhou, China, was chosen as the study area. Firstly, the large-gradient surface displacement in the line of sight was obtained by the fusion of SAR offset tracking and interferometric phase. Subsequently, a multi-segment logistic model was proposed to simulate the temporal effect induced by repeated mining activities. Next, a simplified probability integral method (SPIM) was utilized to invert the geometry of the mining tunnel and separate the displacement of the mining subsidence and landslide. Finally, the subsurface mining parameters and in situ investigation were carried out to assess the impact of mining and precipitation on the kinematic process of Tengqing landslides. Results showed that the repeated mining activities in Tengqing can not only cause land subsidence and rock avalanches at the top of the mountain, but also accelerate the landslide displacement. The technical approach presented in this study can provide new insights for monitoring and modeling the effects of repeated mining-induced landslides in mountainous areas. [ABSTRACT FROM AUTHOR]

A gently dipping bedding landslide occurred on January 3, 2022, in Jinhaihu District, Bijie City, Guizhou Province, in a slope with a weak interbedding of relatively soft rock layers having a loose structure and poor mechanical strength. Fourteen persons were killed, 3 others were injured, and the site's engineering infrastructure was also buried as a consequence of this landslide. Because the construction workers did not have sufficient time to avert the landslide, the rock mass slid toward the free face within a short period of time, resulting in tragedy. The field investigation revealed that after three levels of the slope were excavated, the weak interlayer was exposed to the surface, which provided the free face for the rock mass to slide down along the weak interlayer. According to the XRD results, the main mineral composition of the weak interlayer is illite, accounting for 56.5% of the total composition, indicating that the weak interlayer has strong water absorption and expansibility; the surface water penetrated along the joints and cracks of the upper rock mass to the weak interlayer, which reduced the strength of the weak interlayer and finally caused instability failure. The evolution of this landslide was revealed by numerical simulation using PFC2D. The research results are of great significance to the understanding of this kind of slope and can provide reference value for similar projects in the future. [ABSTRACT FROM AUTHOR]

In the context of circular high steep slopes within a 500-m aperture spherical radio telescope (FAST) site in Pingtang, Guizhou Province in southwest China, a pertinent small-area karst landslide risk assessment is carried out based on geographic information system and fuzzy analytical hierarchy process. Results show the following: (a) The AUC value of hazard assessment is 0.826, which means a good adaptability and a relatively higher accuracy, and the AUC value of risk assessment is 0.803, which means the accuracy of landslide risk assessment is within acceptable level. (b) The slopes of FAST area are generally at the medium or low-hazard level, with high-hazard region accounting for only 3.17% of the total area. High-hazard regions are primarily found in two locations, one is located in nearby the Guangmingding slope where the dangerous rock masses were basically cleared away, the other is located in nearby feed tower 5H where potentially unstable rock masses were reinforced to improve the stability of slope. (c) High-vulnerability area of FAST accounts for approximately 43.62%. As a direct result, the high-risk area of FAST site accounts for 34.36% based on the overlap of vulnerability and hazard. The high risk is concentrated in the surrounding areas such as the feed tower, support pillar, and telescope mirror, and the high-risk area overlaps most of the fault fracture zone in the study area. Our findings provide meaningful references for landslide prevention and monitoring of FAST areas and are instructive in theory and practice. [ABSTRACT FROM AUTHOR]

Prediction on landslide displacement plays an important role in landslide early warning. Many models have been proposed for this purpose. However, the accuracy of the prediction results by these models often varies under different conditions. Rational evaluation and comprehensive consideration of these results still remain a scientific challenge. A new comprehensive combination model is proposed to predict the landslides displacement. The elementary displacement prediction is made by the support vector machine model, the exponential smoothing model, and the gray model (GM)(1,1). The results of the models are comprehensively evaluated by combining the results and introducing the accuracy matrix. The optimal weight in the evaluation work is obtained. A rational prediction result can be attained based on the so-called combination model. The proposed method has been tested by the application of Qinglong landslides in Guizhou Province, China. The comparison between the prediction results and in situ measurement shows that the prediction precision of the proposed model is satisfactory. The root-mean-square error (RMSE) of the combination model can be reduced to 1.4316 (monitoring site JCK2), 1.2623 (monitoring site JCK4), 2.3758 (monitoring site JCK6), 2.2704 (monitoring site JCK8), 1.4247 (monitoring site JCK11), and 0.9449 (monitoring site JCK12), which is much lower than the RMSE of the individual models. [ABSTRACT FROM AUTHOR]

In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people's lives. The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote sensing data is problematic. Object-oriented categorization is utilized in this research to extract landside data from high-resolution GF-1 and Sentinel-2 data. Data preprocessing begins with orthophoto correction, image matching, and data fusion, followed by band enhancement, which comprises band synthesis, principal component analysis, and filtering, and finally landside extraction using an object-oriented technique. The impact of geology, lithology, rainfall, and human activities on the occurrence of landslides in the study area is explored utilizing DEM data, visualization tools, remote sensing interpretation map, and other associated data. The studies are conducted in Shuicheng County, Guizhou Province, China, with a segmentation scale of 25 pixels and 14 classification feature parameters. Following that, the landslide mass is extracted and categorization findings of nearby characteristics are acquired. Finally, the destructiveness of the landslide is determined by comparing the results of object-oriented classification before and after the landslide. With a Kappa coefficient of 0.76 and a landslide extraction accuracy of 79.8%, the overall classification accuracy is 87%. Combined with the geological structure, rock lithology, spatial location, landslide occurrence process, elevation of the study area, precipitation and the impact of human activities, the causes of the landslide are discussed and analyzed. The early warning of other unknown landslides can be obtained by analyzing the features of the aforementioned components. [ABSTRACT FROM AUTHOR]

This short communication reports on a large landslide with a movement of 2 million m3 of soil and rock that occurred on 23 July, 2019 in the village of Pingdi, located in the county of Shuicheng, Guizhou Province, China. This landslide resulted in 42 deaths and 9 missing people. This report describes the preliminary investigation, rescue effort, and possible cause. The total rainfall in the 6 days prior to the landslide was 189.1 mm, which may be held responsible as the major cause. Some recommendations are proposed to reduce human casualties and property losses. [ABSTRACT FROM AUTHOR]