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.

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.

From a geological standpoint, northern Pakistan is one of the most active and unstable areas in the world. As a consequence, many massive landslides have occurred in the area in historical times that have destroyed infrastructure, blocked the Hunza River, and damaged the Karakoram Highway repeatedly. However, despite the high frequency of large magnitude landslide events, and the consequent damages, the entire area is largely understudied, mainly due to the difficult logistics and the large distances involved. This work is aimed at applying the potential use of Interferometric Synthetic Aperture Radar (InSAR) for landslide identification and investigation for the Hunza-Nagar Region. Sentinel-1 images covering a period of more than two years (February 2017–August 2019) were used and processed by adopting the small baseline subset (SBAS) method. The obtained deformation rate measured along the line of sight (V_LOS) varies from −114 to 20 mm/year. The downslope velocity deformation rates (V_slope) range from 0 to −300 mm/year. The V_slope stability threshold for our study area was calculated to be −14 mm/year from the V_slope standard deviation. Four active landslides with V_slope exceeding 14 mm/year were recognizable and have been confirmed by field inspection. The identified landslides listed from the most active to least active are the Humarri, Mayoon, Khai, and Ghulmet landslides, respectively. V_LOS exceeding 114 mm/year was observed in the Humarri landslide, which posed a threat of damming a lake on the Hispar River and was also a risk to the Humarri Village located below the landslide. The maximum mean deformation detected in the Ghulmet, and Mayoon landslide was in the order of 30 mm/year and 20 mm/year, respectively. More importantly, it was found that in places, the slope deformation time series showed a patchy correlation with precipitation and seismic events in the area. This may indicate a complex, and possibly uncoupled, relationship between the two controlling agents promoting the deformation. However, the collective impact of the two factors is evident in the form of a continuously descending deformation curve and clearly indicates the ground distortion. The results indicate a potentially critical situation related to the high deformation rates measured at the Humarri landslide. On this specific slope, conditions leading to a possible catastrophic failure cannot be ruled out and should be a priority for the application of mitigation measures.

Debris flows are a major geological hazard in mountainous regions. For improving mitigation, it is important to study the spatial distribution and factors controlling debris flows. In the Bailong River Basin, central China, landslides and debris flows are very well developed due to the large differences in terrain, the complex geological environment, and concentrated rainfall. For analysis, 52 influencing factors, statistical, machine learning, remote sensing and GIS methods were used to analyze the spatial distribution and controlling factors of 652 debris flow catchments with different frequencies. The spatial distribution of these catchments was divided into three zones according to their differences in debris flow frequencies. A comprehensive analysis of the relationship between various factors and debris flows was made. Through parameter optimization and feature selection, the Extra Trees classifier performed the best, with an accuracy of 95.6%. The results show that lithology was the most important factor controlling debris flows in the study area (with a contribution of 26%), followed by landslide density and factors affecting slope stability (road density, fault density and peak ground acceleration, with a total contribution of 30%). The average annual frequency of daily rainfall > 20 mm was the most important triggering factor (with a contribution of 7%). Forest area and vegetation cover were also important controlling factors (with a total contribution of 9%), and they should be regarded as an important component of debris flow mitigation measures. The results are helpful to improve the understanding of factors influencing debris flows and provide a reference for the formulation of mitigation measures.

Debris flow risk comprehensively reflects the natural and social properties of debris flow disasters and is composed of the risk of the disaster-causing body and the vulnerability of the carrier. The Bailong River Basin (BRB) is a typical mountainous environment where regional debris flow disasters occur frequently, seriously threatening the lives of residents, infrastructure, and regional ecological security. However, there are few studies on the risk assessment of mountainous debris flow disasters in the BRB. By considering a complete catchment, based on remote sensing and GIS methods, we selected 17 influencing factors, such as area, average slope, lithology, NPP, average annual precipitation, landslide density, river density, fault density, etc. and applied a machine learning algorithm to establish a hazard assessment model. The analysis shows that the Extra Trees model is the most effective for debris flow hazard assessments, with an accuracy rate of 88%. Based on socio-economic data and debris flow disaster survey data, we established a vulnerability assessment model by applying the Contributing Weight Superposition method. We used the product of debris flow hazard and vulnerability to construct a debris flow risk assessment model. The catchments at a very high-risk were distributed mainly in the urban area of Wudu District and the northern part of Tanchang County, that is, areas with relatively dense economic activities and a high disaster frequency. These findings indicate that the assessment results provide scientific support for planning measures to prevent or reduce debris flow hazards. The proposed assessment methods can also be used to provide relevant guidance for a regional risk assessment of debris flows in the BRB and other regions.

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 (A_L) to volume (V_L) (V_L = 0.067 × A_L^1.351), 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.