Explore the vast range of titles available.

Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, there has been limited analysis of actual landslide activity processes in real-time. The SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) method can fully consider the current landslide susceptibility situation and, thus, it can be used to optimize the results of LSM. In this study, we compared the results of LSM using logistic regression and Random Forest models along the KKH. Both approaches produced a classification in terms of very low, low, moderate, high, and very high landslide susceptibility. The evaluation results of the two models revealed a high susceptibility of land sliding in the Gaizi Valley and the Tashkurgan Valley. The Receiver Operating Characteristic (ROC) curve and historical landslide verification points were used to compare the evaluation accuracy of the two models. The Area under Curve (AUC) value of the Random Forest model was 0.981, and 98.79% of the historical landslide points in the verification points fell within the range of high and very high landslide susceptibility degrees. The Random Forest evaluation results were found to be superior to those of the logistic regression and they were combined with the SBAS-InSAR results to conduct a new LSM. The results showed an increase in the landslide susceptibility degree for 2808 cells. We conclude that this optimized landslide susceptibility mapping can provide valuable decision support for disaster prevention and it also provides theoretical guidance for the maintenance and normal operation of KKH.

With climate change, the Qinghai–Tibet Highway (QTH) is facing increasingly severe risks of natural hazards, posing a significant threat to its normal operation. However, the types, distribution, and future risks of hazards along the QTH are still unclear. In this study, we established an inventory of multi-hazards along the QTH by remote sensing interpretation and field validation, including landslides, debris flows, thaw slumps, and thermokarst lakes. The QTH was segmented into three sections based on hazard distribution and environmental factors. Susceptibility modelling was performed for each hazard within each section using machine learning models, followed by further evaluation of hazard susceptibility under future climate change scenarios. The results show that, at present, approximately 15.50% of the area along the QTH exhibits high susceptibility to multi-hazards, with this proportion projected to increase to 20.85% and 23.32% under the representative concentration pathways (RCP) 4.5 and RCP 8.5 distant future scenarios, respectively. Variations in hazard-prone environments dominate the spatial heterogeneity of multi-hazard distribution. Gravity hazards demonstrate limited sensitivity to climate change, whereas thermal hazards exhibit a more pronounced response. Our geomorphology-based segmented assessment framework effectively enhances evaluation accuracy and model interpretability. The results can provide critical insights for the operation, maintenance, and hazard risk management of the QTH.

The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.

Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.

Lanzhou New District is the first and largest national-level new district in the Loess Plateau region of China. Large-scale land creation and rapid utilization of the land surface for construction has induced various magnitudes of land subsidence in the region, which is posing an increasing threat to the built environment and quality of life. In this study, the spatial and temporal evolution of surface subsidence in Lanzhou New District was assessed using Persistent Scatterer Interferometric Synthetic Aperture radar (PSInSAR) to process the ENVISAT SAR images from 2003–2010, and the Small Baseline Subset (SBAS) InSAR to process the Sentinel-1A images from 2015–2016. We found that the land subsidence exhibits distinct spatiotemporal patterns in the study region. The spatial pattern of land subsidence has evidently extended from the major urban zone to the land creation region. Significant subsidence of 0–55 mm/year was detected between 2015 and 2016 in the land creation and urbanization area where either zero or minor subsidence of 0–17.2 mm/year was recorded between 2003 and 2010. The change in the spatiotemporal pattern appears to be dominated mainly by the spatial heterogeneity of land creation and urban expansion. The spatial associations of subsidence suggest a clear geological control, in terms of the presence of compressible sedimentary deposits; however, subsidence and groundwater fluctuations are weakly correlated. We infer that the processes of land creation and rapid urban construction are responsible for determining subsidence over the region, and the local geological conditions, including lithology and the thickness of the compressible layer, control the magnitude of the subsidence process. However, anthropogenic activities, especially related to land creation, have more significant impacts on the detected subsidence than other factors. In addition, the higher collapsibility and compressibility of the loess deposits in the land creation region may be the underlying mechanism of macro-subsidence in Lanzhou New District. Our results provide a useful reference for land creation, urban planning and subsidence mitigation in the Loess Plateau region, where the large-scale process of bulldozing mountains and valley infilling to create level areas for city construction is either underway or forthcoming.

Rainfall-induced landslides are a significant hazard in many areas of loess-covered terrain in Northwest China. To investigate the response of a loess landslide to rainfall, a series of artificial rainfall experiments were conducted on a natural loess slope, located in the Bailong River Basin, in southern Gansu Province. The slope was instrumented to measure surface runoff, pore water pressure, soil water content, earth pressure, displacement, and rainfall. The hydrological response was also characterized by time-lapse electrical resistivity tomography. The results show that most of the rainfall infiltrated into the loess landslide, and that the pore water pressure and water content responded rapidly to simulated rainfall events. This indicates that rainfall infiltration on the loess landslide was significantly affected by preferential flow through fissures and macropores. Different patterns of pore water pressure and water content variations were determined by the antecedent soil moisture conditions, and by the balance between water recharge and drainage in the corresponding sections. We observed three stages of changing pore water pressure and displacement within the loess landslide during the artificial rainfall events: Increases in pore water pressure initiated movement on the slope, acceleration in movement resulting in a rapid decrease in pore water pressure, and attainment of a steady state. We infer that a negative pore water pressure feedback process may have occurred in response to shear-induced dilation of material as the slope movement accelerated. The process of shear dilatant strengthening may explain the phenomenon of semi-continuous movement of the loess landslide. Shear dilatant strengthening, caused by intermittent or continuous rainfall over long periods, can occur without triggering rapid slope failure.

The eastern margin of the Tibetan Plateau is one of the regions with the most severe landslide disasters on a global scale. With the intensification of seismic activity around the Tibetan Plateau and the increase in extreme rainfall events, the prevention of landslide disasters in the region is facing serious challenges. This article selects the Bailong River Basin located in this region as the research area, and the historical landslide data obtained from high-precision remote sensing image interpretation combined with field validation are used as the sample library. Using machine learning algorithms and data-driven landslide susceptibility assessment as the methods, 17 commonly used models and 17 important factors affecting the development of landslides are selected to carry out the susceptibility assessment. The results show that the BaggingClassifier model shows advantageous applicability in the region, and the landslide susceptibility distribution map of the Bailong River Basin was generated using this model. The results show that the road and population density are both high in very high and high susceptible areas, indicating that there is still a significant potential landslide risk in the basin. The quantitative evaluation of the main influencing factors emphasizes that distance to a road is the most important factor. However, due to the widespread utilization of ancient landslides by local residents for settlement and agricultural cultivation over hundreds of years, the vast majority of landslides are likely to have occurred prior to human settlement. Therefore, the importance of this factor may be overestimated, and the evaluation of the factors still needs to be dynamically examined in conjunction with the development history of the region. The five factors of NDVI, altitude, faults, average annual rainfall, and rivers have a secondary impact on landslide susceptibility. The research results have important significance for the susceptibility assessment of landslides in the complex environment of human–land interaction and for the construction of landslide disaster monitoring and early warning systems in the Bailong River Basin.

At 6pm on July 19, 2019, the Yahuokou earthflow (Zhouqu County, Gansu Province, China) was reactivated following a period of intense rainfall. A volume of approximately 3.9 × 106 m3 travelled approximately 1100 m in 27 days, blocking part of the Min River. Multi-temporal InSAR monitoring and interpretation of high-resolution optical images were used to map different phase of mass movements (including rock fall, topple, slide, and flowslide) to characterize the pre-failure deformation of the earthflow. Analysis of displacement patterns indicates that initiation of the earthflow is closely correlated with road construction, intense and long-lasting rainfall (443 mm from April 1, to July 19, 2019), and seismic activity. The potential to forecast the time of failure based on pre-failure deformation patterns observed by InSAR monitoring is demonstrated. Three types of pre-failure deformation are distinguished: steady creep, fluctuating creep, and accelerating creep. The monitoring process enabled identification and characterization of three key stages of the earthflow (initiation, transportation, and accumulation/deposition) providing important lessons learned for improvement of landslide early warning and hazard assessment in similar geological and geomorphological settings.

The Hunza Valley, in the northwestern Karakoram Mountains, North Pakistan, is a typical region with many towns and villages, and a dense population and is prone to landslides. The present study completed landslide identification, updating a comprehensive landslide inventory and analysis. First, the ground surface deformation was detected in the Hunza Valley by SBAS-InSAR from ascending and descending datasets, respectively. Then, the locations and boundaries were interpreted and delineated, and a comprehensive inventory of 118 landslides, including the 53 most recent InSAR identified active landslides and 65 landslides cited from the literature, was completed. This study firstly named all 118 landslides, considering the demand for globally intensive research and hazard mitigation. Finally, the deformation, spatial–topographic development, and distribution characteristics in the Hunza Valley scale and three large significant landslides were analyzed. Information on 72 reported landslides was used to construct an empirical power law relationship linking landslide area (AL) to volume (VL) (VL = 0.067 × AL1.351), and this formula predicted the volume of 118 landslides in this study. We discovered that the landslides from the literature, which were interpreted from optical images, had lower levels of velocity, area, elevation, and height. The SBAS-InSAR-detected active landslide was characterized by higher velocity, larger area, higher elevation, larger slope gradient, larger NDVI (normalized difference vegetation index), and greater height. The melting glacier water and rainfall infiltration from cracks on the landslide’s upper part may promote the action of a push from gravity on the upper part. Simultaneously, the coupling of actions from river erosion and active tectonics could have an impact on the stability of the slope toe. The up-to-date comprehensive identification and understanding of the characteristics and mechanism of landslide development in this study provide a reference for the next step in landslide disaster prevention and risk assessment.

The Heifangtai terrace, in Northwest China, is a typical area where loess landslides have been induced by agricultural irrigation, and many of the landslides are prone to reactivation. However, the spatiotemporal evolution and hydrological-triggering mechanisms of loess landslide reactivation are not well understood. In this research, multiple remote sensing (SBAS-InSAR, TLS, and optical remote sensing), integrated with time-lapse ERT (tl-ERT) imaging, was used to monitor the post-failure evolution of the Luojiapo landslide in Heifangtai during the period of May 2015 to Nov. 2020. Pronounced temporal and spatial differences in the deformation and hydrological evolution of landslides after sliding were observed. The largest displacement rates occurred in the landslide source area, and the lateral extension of the landslide source area caused by spatial differences in reactivation is an important feature of landslide evolution. In the landslide area, the groundwater table (GWT) decreased at first ascribed to the spring hole caused by the exposure of the GWT after sliding and then increased due to the subsequent continuous irrigation, and the lag time of the GWT response to irrigation decreased significantly. Spatial differences in GWT evolution are one of the main causes of spatial differences in landslide reactivation, and reactivation was more likely to occur where the GWT fluctuated at a high level. The GWT also fell with local reactivation. Our findings highlight the potential for obtaining internal and external spatiotemporal information of loess landslide evolution using multiple remote sensing integrated with tl-ERT. Our results also help to understand the reactivation process of irrigated loess landslides and provide a reference for the monitoring and early warning of such landslides.