Landslide Detection and Monitoring Using Multisource Remote Sensing Data

The China–Nepal Transportation Corridor is vital to the country’s efforts to build a land trade route in South Asia and promote the Ring-Himalayan Economic Cooperation Belt. Due to the complex geological structure and topographical environment of the Qinghai–Tibet Plateau, coupled with the impact of climate change, the frequent occurrence of geological disasters has increased the operational difficulty of the China–Nepal Highway and the construction difficulty of the China–Nepal Railway. However, to date, there has been no systematic study of the spatial distribution of landslides along the entire route within the area, the factors influencing landslides at different scales, or the causes of landslides under different topographic backgrounds. There is an even greater lack of research on areas threatened by potential landslides. This study comprehensively applies remote sensing, mathematical statistics, and machine learning methods to map landslides along the China–Nepal transportation corridor, explore the influencing factors and causes of different types of landslides, and investigate the distribution characteristics of potential landslides. A total of 609 historic landslides have been interpreted in the study area and were found to be distributed along faults and locally concentrated. The strata from which landslides develop are relatively weak and are mainly distributed within 2 km of a fault with a slope between 20° and 30°. The direction of slope for the majority of landslides is south to south-west, and their elevation is between 4000 and 5000 m. In addition, we discovered a power law relationship between landslide area and volume (V_L = 2.722 × A_L^1.134) and determined that there were 47 super-large landslides, 213 large landslides, and 349 small and medium-sized landslides in the area, respectively. Slope is the most significant influencing factor for the development of landslides in the area. Apart from slope, faults and strata significantly influence the development of large and medium-small landslides, respectively. We have identified 223 potential landslides in the region, 15 of which directly threaten major transport routes, mainly in the Renbu Gorge section of the China–Nepal Highway and the proposed China–Nepal Railway section from Peikucuo to Gyirong County. In addition, we also discussed the causes of landslides within three geomorphic units in the region. First, the combined effects of faulting, elevation, and relatively weak strata contribute to the development of super-large and large landslides in the Gyirong basin and gorge. Second, the relatively weak strata and the cumulative damaging effects of earthquakes promote the development of small and medium-sized landslides in the Xainza-Dinggye rift basin. Third, under the combined effect of the hanging wall effect of thrust faults and the relatively weak material composition, landslides of various types have developed in the Nagarzê mountain. It is worth noting that potential landslides have developed in all three geomorphic units mentioned above. This study provides data and theory to assist in the accurate mitigation and control of landslide hazards in the corridor. Full article

To reduce the significant losses caused by slope failures and landslides, it is of great significance to detect and predict these disasters scientifically. This study focused on Huangdao District of Qingdao City in Shandong Province, using the improved Faster R-CNN network to detect slope failures and landslides. This study introduced a multi-scale feature enhancement module into the Faster R-CNN model. The module enhances the network’s perception of different scales of slope failures and landslides by deeply fusing high-resolution weak semantic features with low-resolution strong semantic features. Our experiments show that the improved Faster R-CNN model outperformed the traditional version, and that ResNet50 performed better than VGG16 with an AP value of 90.68%, F1 value of 0.94, recall value of 90.68%, and precision value of 98.17%. While the targets predicted by VGG16 were more dispersed and the false detection rate was higher than that of ResNet50, VGG16 was shown to have an advantage in predicting small-scale slope failures and landslides. The trained Faster R-CNN network model detected geological hazards of slope failure and landslide in Huangdao District, missing only two landslides, thereby demonstrating high detection accuracy. This method can provide an effective technical means for slope failures and landslides target detection and has practical implications. Full article

Fluctuations in reservoir water levels exert a strong triggering effect on landslides along reservoir banks, constituting a long-term concern in the safe operation of hydroelectric projects and in the prevention and management of geological disasters. While existing research has investigated the impact of periodic water level changes on the deformation of reservoir bank landslides, observation and detection of such deformation are challenging, with noticeable gaps in understanding how these deformations respond to water level changes during the water impoundment period. To address this, our study targets the Baihetan Reservoir, leveraging 567 ascending and descending LiCSAR data and LiCSBAS (the small-baseline subset within LiCSAR) technology to construct a time series of ground deformations in the study area from 2019 to 2023. The TLCC (Time Lag Cross Correlation) model was employed to examine the time-lag response pattern of reservoir bank landslide deformations to reservoir water level changes during the impoundment period. Our findings indicate a clear time-lag response in reservoir bank landslide deformations to water level changes during the impoundment process. The rise in water levels emerged as a primary factor influencing the instability of reservoir bank landslides. During the half-year impoundment period of the Baihetan Reservoir, a time lag of 5–7 days was observed between landslide deformations and increases in water levels, with landslides on the eastern and western banks exhibiting differing time-lag response patterns. Our study illuminates the time-lag effect between water level changes during reservoir impoundment and reservoir bank landslide deformation monitoring. By proposing a quantitative analysis methodology utilizing LiCSBAS technology and the TLCC model, our findings can inform decision-making in the field of disaster prevention and reduction in reservoir engineering. Full article

The Arctic zone of West Siberia (Yamal and Gydan peninsulas) is an area with continuous permafrost and tabular ground ice close to the surface, active thermodenudation, and related landforms: retrogressive thaw slumps (RTS); in Russian referred to as thermocirques (TC). The dimensions of most TCs have not been determined so far. We use Sentinel 2 imagery to measure each TC area ranging from 0.55 to 38 ha with a median of 2.5 ha. Around 95% of TCs have an area of less than 10 ha. The largest areas are gained due to the merging of several neighboring TCs. The ArcticDEM is used to determine TC edge elevation and slope angle. In general, the Median TC of the Yamal peninsula has an area of 1.8 ha, an elevation of the edge of 17.7 m, and a slope angle of 2.5°. The Median TC of the Gydan peninsula has an area of 2.6 ha, elevation of the edge of 29.4 m, and slope angle of 3°. TCs of the Gydan peninsula occupy higher positions and slightly steeper slopes compared to TCs of the Yamal peninsula. The ranges of the median and the largest TC areas are consistent with the reported RTS dimensions in North America. Full article

This paper proposes a method to reconstruct and visualize landslide events based on pre- and post-disaster remote sensing data. The proposed method establishes the dynamic equations of the landslide evolution process and calibrates the model parameters based on pre- and post-disaster remote sensing data. Based on the calibrated dynamic equations, we reconstruct and simulate the historical landslide process and visualize the landslide evolution. The experimental results show that our method could dynamically and realistically reconstruct and visualize the landslide evolution process. Moreover, the landslide process simulation can also detect the maximum depth, maximum sliding speed, maximum momentum, and other indicators during the evolution process, and the visualization results can be used for subsequent hazard assessment, engineering implementation, and other applications. Full article

This paper investigates the possibility to detect the short-time evolution of the slow-moving Vomice earth flow, located in the northeastern sector of the Calabria region (South Italy), by combining the information obtained from two different drone flights, carried out in February 2019 and June 2022, with field surveys. The obtained results consisted of delimiting all landslide bodies constituting the Vomice earth flow, detecting landslide types and the state of activity, as well as identifying spatial and volumetric changes. The obtained results showed that depletion and transition zones of the Vomice earth flow are active, while the accumulation zone appears prevalently dormant. Particularly, in the analyzed period, the depletion zone was characterized by local collapses of the main scarps where several slides evolving in earth flows caused more than 20 m of retrogressive fail upslope. The maximum elevation changes observed in these zones were about ±5 m. The volume of the material mobilized by mass movements was about 114.2 × 10³ m³, whereas the volume of the accumulated material was approximately 92.7 × 10³ m³. The transition zone was affected by several slow earthflows that re-mobilized the displaced material located in the middle portion of the landslide and reached the accumulation zone. Overall, the results of this study demonstrated the practicality and feasibility of using UAV tools for detecting the short-time evolution of a large landslide. Full article

The Jiaju ancient landslide is a giant landslide located upstream of the Dadu River, eastern Tibetan Plateau, with a volume of approx. 7.04 × 10⁸ m³. The Jiaju ancient landslide is complex and comprises five secondary sliding bodies, e.g., the Jiaju landslide (H01), Niexiaping landslide (H02), Xiaobawang landslide (H03), Niela landslide (H04), and Mt.-peak landslide (H05). Affected by regional neotectonic movement, heavy rainfall, river erosion, and lithology, the secondary sliding bodies of the Jiaju ancient landslide are undergoing significantly different creep-sliding deformation, which will cause great damage to villages, roads, and rivers around the sliding bodies. Combined with the SBAS-InSAR method, Sentinel-1A data from June 2018 to August 2021, remote sensing and field surveys, this study obtained the Jiaju ancient landslide deformation characteristics and deformation rate in the line-of-sight direction (V_LOS), slope (V_Slope), and vertical (V_Vertical). It is concluded that the maximum deformation rate of the Jiaju ancient landslide is significant. The maximum of V_LOS, V_Slope, and V_Vertical are −179 mm/a, −211 mm/a, and −67 mm/a, respectively. The Niela landslide (H04), Jiaju landslide (H01), and Mt.-peak landslide (H05) are very large and suffer strong deformation. Among these, the Niela landslide (H04) is in the accelerative deformation stage and at the Warn warning level, and the Jiaju landslide (H01) is in the creep deformation and attention warning level, especially heavy rainfall, which will accelerate landslide deformation and trigger reactivation. Because the geological structure is very complex for the Jiaju ancient landslide and strong neotectonic movement, under heavy rainfall, the secondary landslide creep-sliding rate of the Jiaju ancient landslide is easily accelerated and finally slides in part or as a whole, resulting in river blocking. It is suggested to strengthen the landslide deformation monitoring of the Niela landslide and Jiaju landslide and provide disaster mitigation and prevention support to the government and residents along the Dadu River watershed. Full article

The Himalayan state of Sikkim is prone to some of the world’s largest landslides, which have caused catastrophic damage to lives, properties, and infrastructures in the region. The settlements along the steep valley sides are particularly subject to frequent rainfall-triggered landslide events during the monsoon season. The region has also experienced smaller rock slope failures (RSF) after the 2011 Sikkim earthquake. The surface displacement field is a critical observable for determining landslide depth and constraining failure mechanisms to develop effective mitigation techniques that minimise landslide damage. In the present study, the persistent scatterers InSAR (PSI) method is employed to process the series of Sentinel 1-A/B synthetic aperture radar (SAR) images acquired between 2015 and 2021 along ascending and descending orbits for the selected areas in Gangtok, Sikkim, to detect potentially active, landslide-prone areas. InSAR-derived ground surface displacements and their spatio-temporal evolutions are combined with field investigations to better understand the state of activity and landslide risk assessment. Field investigations confirm the ongoing ground surface displacements revealed by the InSAR results. Some urban areas have been completely abandoned due to the structural damage to residential housing, schools, and office buildings caused by displacement. This paper relates the geotechnical investigations carried out on the ground to the data obtained through interferometric synthetic aperture radar (InSAR), focusing on the triggering mechanisms. A strong correlation between seasonal rainfall and landslide acceleration, as well as predisposing geological-structural setting, suggest a causative mechanism of the landslides. Full article