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The great M ~ 8 1920 Haiyuan earthquake (HYEQ) was one of the largest and most deadly earthquakes in China in the last century, with ~ 234,000 deaths. The earthquake occurred within the Loess Plateau of northern China, where Quaternary loess deposits form a distinctive blanket across the landscape. Large regions of this loess cover experienced co-seismic landslides. Based on an analysis of the original disaster reports, field surveys, and satellite image interpretation, we have compiled the shaking effects of the earthquake, including the distribution of landslides, fatalities, and structural damage. Landslides triggered by the HYEQ (n > 7,000) are concentrated south of the Haiyuan fault, in a region that has both thick loess cover and long-term relief generated by the drainage network. This distribution is spatially separated from landslides triggered by other earthquakes. We find that in contrast to previous studies, the most important factor in the severe death toll of the HYEQ was the collapse of housing by ground shaking, including collapse of loess house-caves. Landslides were a secondary factor; although up to 32,000 deaths occurred in areas with intense landsliding. Based on the revised distribution pattern of landslides and damage (e.g., house collapses), we suggest that the isoseismal intensity IX line extends south of previous locations. We have also identified 126 dammed lakes created by co-seismic landslides, which form major modifications of this semi-arid landscape. The research methods in this paper, combining historical review, satellite image interpretation, and field validation of landslides, can be used as a reference for studies of other areas affected by historical earthquakes and co-seismic landslides, elsewhere in the Loess Plateau and beyond. [ABSTRACT FROM AUTHOR]

The focus of this paper is on two large sequential landslides which formed a dam and the resulting lake that occurred along the Jinsha River on October 11 and November 3, 2018. About 24 and 9 × 106 m3 of material failed and rushed into the river, respectively. Both landslides totally blocked the river and induced hazard chains. Four multi-temporal unmanned aerial vehicle (UAV)-based high-resolution images and digital elevation models (DEMs) before and after the landslides were obtained and merged. The initial deformation of the two landslides can be clearly captured by optical remote-sensing images and interferometric synthetic aperture radar (InSAR) analysis. The depth-integrated continuum method was adopted to analyze the dynamic process of both landslides, using the same basal friction angle and cohesion. The computational results matched well with field measurements and observations. It is shown that the evaluation of potentially landslide-prone areas by the depth-integrated continuum method is feasible and is able to provide significant information before a possible event. [ABSTRACT FROM AUTHOR]

Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events. [ABSTRACT FROM AUTHOR]

On April 20, 2013, a Mw 6.6 earthquake struck Lushan County of Sichuan Province, China, about 80 km southwest of the epicenter of the 12 May 2008 Wenchuan Mw 7.9 event. It triggered a large number of landslides in a broad area, causing causality and injuries and damages to roads, drains, dwellings, and infrastructures. Field investigations of coseismic landslides provide a basis for better understanding and illustrating the spatial distributions and hazards related to the Lushan earthquake-triggered landslides. Therefore, this paper presents results of field investigations of the Lushan earthquake-triggered landslides, which can be classified into two main categories of disrupted landslides and coherent landslides, and constitutes seven types. The disrupted landslides include five types: rock falls, rock slides, rock avalanches, soil falls, and soil slides; and the coherent landslides have two types: soil slumps and slow earth flows. All of the seven types of landslides can be observed in many field photos, aerial photographs, and/or satellite images. Among them, seven typical large-scale landslides, distributed in a northeast trending line consistent with the Shuangshi-Dachuan fault, which is presumably related with the earthquake, are described in detail. We prepared an original, emergency-based coseismic landslide inventory map based on visual interpretation of available post-earthquake aerial photographs and pre-earthquake satellite images, combined with results from emergency-based field investigations. The original inventory map registered 3884 landslides. Later, 11,761 more landslides were identified by much more careful and time-consuming visual interpretation based on the same aerial photographs and satellite images. Subsequently, all the 15,645 coseismic landslides were correlated with six common landslide impact factors, including elevation, slope angle, slope aspect, topographic position, stratum/lithology, and peak ground accumulation (PGA). These observational and compiled coseismic landslide data allow us to make subsequent compiling of a detailed and completed landslide inventory by visual interpretation of remote sensing images as well as further studies of landslides induced by the Lushan earthquake. [ABSTRACT FROM AUTHOR]

On 8 May 2022 and 12 April 2020, two large rock avalanches occurred in Zhijin County, China, in the villages of Baiyan and Baiwu, respectively. These long-runout rock avalanches had the same geological, hydrological, and topographical settings and similar triggering factors but different mobility, and this inspired a research interest in the failure and runout processes of large-volume mass movements. In this study, a detailed on-site field investigation was conducted with the aid of unmanned-aerial-vehicle-based photogrammetry and satellite imaging technologies. The results suggest that the two rock avalanches were, rather than a single factor, collaboratively triggered by multiple loading effects involving long-term underground coal-mining vibration, karstification, and cumulative precipitation. The cliff failure was dominated by the strength of the rock mass and development of the joints, while the runout was controlled by the dynamic performance of the soil on the potential sliding surface rather than the rock debris itself. The liquefaction performance of the sliding surface—consisting of coarse soils on an ancient eluvial slope in the transition area—indicates that the Baiyan avalanche had significantly longer runout and higher mobility than the Baiwu case. Although the two avalanches had different mobilities, they both briefly obeyed a physics-based statistical evaluation method—the energy line. [ABSTRACT FROM AUTHOR]

The infrastructure construction in tectonically active mountainous areas is faced with the threat of earthquake-induced rockslide-avalanches. We took the Woqian rockslide-avalanche induced by the Wenchuan earthquake near the Qinchuan active fault, Southwestern China as an instance to elucidate a unique initiation mechanism of the inward inclined bedding slope, which had a characteristic of twice destabilization in succession. The high-resolution satellite images, UAV aerial photography, field investigation, and numerical simulation were conducted to reveal the failure processes. The results suggested that (1) the long-term headward erosion of the gully runoff had locally exposed the unconformity contact plane within the strata, which evolved as a part of the sliding plane; (2) the seismic loading produced a rapid shear strain increment on the unconformity plane; (3) the brittle rupture occurred along the sliding plane at the bottom of the sliding source I developed at 6 s after the seismic wave arrival; (4) as a "resistance barrier", the sliding source I first collapsed and most of it accumulated at a short distance; (5) the sliding source II subsequently slid along the apparent dip of the bedding plane and then entrained the deposits of the source I into a catastrophic rock avalanche. The failure mechanism of such carbonaceous bedding slopes with similar geological conditions could provide an insight into the early risk recognition of potential rockslides and the safe site selection of infrastructures. [ABSTRACT FROM AUTHOR]

A large-scale obliquely inclined bedding rockslide, activated by a heavy rainstorm, occurred on July 8, 2020, at 7:05 (UTC + 8) in Shiban Village, Songtao Miao Autonomous County, Guizhou Province, China. The loss of life in this event was greatly reduced owing to the local warning system for rainstorm-induced geohazards. To understand the failure characteristics, triggering factors, the genetic mechanism of the landslide, the geomorphological features, geological characteristics, hydrological conditions, and rainfall characteristics were systematically studied by a synthetic approach including field investigations, satellite imagery, unmanned aerial vehicle (UAV) photography, laboratory tests, and rainfall data statistics. The results indicated that the interface between the soft and hard rock, the well-developed joints, and the free face in front of the slope constituted the boundaries of this landslide. The concave topography at the back and southern edge of the landslide, the bare ground, and the cataclastic structure of the rock mass provided favorable conditions for the collection or infiltration of rainwater. The concentrated rainstorm was the direct trigger for the landslide, which led to a rapid inflow and retention of rainfall in the landslide through favorable landform and geological conditions. The groundwater recharge that cannot be drained in time caused the mechanical deterioration of rock mass and induced a rapid increase in pore water pressure in the landslide. Moreover, the water level of the Ganlong River at the toe of the slope also rose rapidly, and the uplift pressure in front of the slope increased accordingly. Under the combined action of these adverse factors, the overall anti-sliding force of the slope was less than the sliding force, finally resulting in the landslide. Remarkably, the local warning system for rainstorm-induced geohazards successfully forecasted the landslide, but the shortcoming is that the forecast time in advance is short. Nevertheless, the prediction has significantly reduced human casualties and provided valuable experience for the prediction of this type of landslide. [ABSTRACT FROM AUTHOR]

Landslides are catastrophic natural hazards that often lead to loss of life, property damage, and economic disruption. Image-based landslide investigations are crucial for determining landslide susceptibility and risk. In practice, satellite images have been widely utilized for such investigations; however, they still require significant labor and time resources. In this study, we propose an image-based two-phase data-driven framework for detecting and segmenting landslide regions using satellite images. In phase I, an object detection algorithm, Faster-RCNN, is trained to detect the landslide location within the large-scale satellite images. The bounding boxes of each landslide location are proposed and visualized. In phase II, we crop the satellite images into small images using the location information of the bounding boxes. Next, we use a boundary detection algorithm to identify the boundary information of each detected loess landslide to strengthen the segmentation performance. Finally, we improve the architecture of the segmentation U-Net by integrating additional inception blocks with dilation to enhance the landslide segmentation performance. A total of 150 local loess landslide occurrences in northern China are selected as our case study to validate the effectiveness, efficiency, and universality of the proposed two-phase framework. Segmentation of loess landslides is considered a challenging task due to the intrinsic nature of vague boundary information. The proposed framework is compared with the conventional U-Net and other recent benchmarking landslide segmentation algorithms. Computational results indicate that the proposed framework produces more accurate segmentation of loess landslides compared with the other tested benchmarking algorithms. [ABSTRACT FROM AUTHOR]

Convolution neural network (CNN) is an effective and popular deep learning method which automatically learns complicated non-linear mapping from original inputs to given labels or ground truth through a series of convolutional layers. This study focuses on detecting landslides from high-resolution optical satellite images using CNN-based methods, providing opportunities for recognizing latent landslides and updating large-scale landslide inventory with high accuracy and time efficiency. Considering the variety of landslides and complicated backgrounds, attention mechanisms originated from the human visual system are developed for boosting the CNN to extract more distinctive feature representations of landslides from backgrounds. As deep learning needs a large number of labeled data to train a learning model, we manually prepared a landslide dataset which is located in the Bijie city, China. In the dataset, 770 landslides, including rock falls, rock slides, and a few debris slides, were interpreted by geologists from the satellite images and digital elevation model (DEM) data and further checked by fieldwork. The landslide data was separated into a training set that trains the attention boosted CNN model and a testing set that evaluates the performance of the model with a ratio of 2:1. The experimental results showed that the best F1-score of landslide detection reached 96.62%. The results also proved that the performance of our spatial-channel attention mechanism was fairly over other recent attention mechanisms. Additionally, the effectiveness of predicting new potential landslides with high efficiency based on our dataset is demonstrated. [ABSTRACT FROM AUTHOR]