Peihua Xu

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred, however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital elevation model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The LSTM (Long Short-Term Memory) method is then adopted to predict the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the deformation of Tuanjiecun potential landslide will increase to -238.57mm. This paper provides a new approach for disaster prevention and mitigation by determining a potential landslide using composite remote sensing and predicting the development of the potential landslide on indirect contact monitoring.

Landslides in high-order position areas pose a serious threat to residents located below the area. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred, however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset InSAR) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3)and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital elevation model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The LSTM (Long Short-Term Memory) method is then adopted to predicted the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the Tuanjiecun potential landslide will be relatively stable in the next five years although the largest deformation increased to -238.57mm. The analyses presented here aims to identify the current stage and future development of this potential landslide in terms of the underlying mechanism and transformation, so as to provide effective guidance for disaster prevention and mitigation across the globe.

Landslides and collapses are common geological hazards in mountainous areas, posing significant threats to the lives and property of residents. Therefore, early identification of disasters is of great significance for disaster prevention. In this study, we used Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to process C-band Sentinel-1A images to monitor the surface deformation from Songpinggou to Feihong in Maoxian County, Sichuan Province. Visibility analysis was used to remove the influence of geometric distortion on the SAR images and retain deformation information in the visible area. Hot spot and kernel density analyses were performed on the deformation data, and 18 deformation clusters were obtained. Velocity and slope data were integrated, and 26 disaster areas were interpreted from the 18 deformation clusters, including 20 potential landslides and 6 potential collapses. A detailed field investigation indicated that potential landslides No. 6 and No. 8 had developed cracks and were severely damaged, with a high probability of occurrence. Potential collapse No. 22 had developed fissures, exposing a dangerous rock mass and posing significant threats to the lives and property of residents. This study shows that the proposed method that combines visibility analysis, InSAR deformation rates, and spatial analysis can quickly and accurately identify potential geological disasters and provide guidance for local disaster prevention and mitigation.

Driven by the pull of gravity, mass-wasting comprises all of the sedimentary processes related to remobilization of sediments deposited on slopes, including creep, sliding, slumping, flow, and fall. It is vital to conduct mass-wasting susceptibility mapping, with the aim of providing decision makers with management advice. The current study presents two individual data mining methods—the frequency ratio (FR) and information value model (IVM) methods—to map mass-wasting susceptibility in four catchments in Miyun County, Beijing, China. To achieve this goal, nine influence factors and a mass-wasting inventory map were used and produced, respectively. In this study, 71 mass-wasting locations were investigated in the field. Of these hazard locations, 70% of them were randomly selected to build the model, and the remaining 30% of the hazard locations were used for validation. Finally, a receiver operating characteristic (ROC) curve was used to assess the mass-wasting susceptibility maps produced by the above-mentioned models. Results show that the FR had a higher concordance and spatial differentiation, with respective values of 0.902 (area under the success rate) and 0.883 (area under the prediction rate), while the IVM had lower values of 0.865 (area under the success rate) and 0.855 (area under the prediction rate). Both proposed methodologies are useful for general planning and evaluation purposes, and they are shown to be reasonable models. Slopes of 6–21° were the most common thresholds that controlled occurrence of mass-wasting. Farmland terraces were mainly composed of gravel, mud, and clay, which are more prone to mass-wasting. Mass-wasting susceptibility mapping is feasible and potentially highly valuable. It could provide useful information in support of environmental health policies.

The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.