Failure mechanism and movement process of three loess landslides due to freeze-thaw cycle in the Fangtai village, Yongjing County, Chinese Loess Plateau

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Formative Period Tracing and Driving Factors Analysis of the Lashagou Landslide Group in Jishishan County, China

Fan,

Zhang,

Niu

et al. 2024

“…In addition, based on the previously mentioned results, we believe that there is a notable contribution from the attenuation of soil strength caused by changes in soil properties during Heitai irrigation. At present, the loss of material via spring water, changes in pore solution concentration, and the freeze-thaw cycle may be the main causes of changes in soil properties [31,34,35,37,72]. It is notable that the current source of groundwater recharge has recently weakened.…”

Section: Potential For Future Landslides On the Heitai Terracementioning

confidence: 99%

“…In fact, approximately 28% of the landslides that occurred in the early phases of the Heitai were shallow landslides unrelated to groundwater [16], and such landslides still exist today. In addition, wind-deposited loess, which has a sub-stable structure [14,27,28], is extremely water-sensitive [27,29], leading to soil strength attenuation caused by changes in soil properties after long periods of intense soil-water interaction [27,[30][31][32][33][34][35][36][37], which in turn contribute significantly to landslides [37]. Therefore, understanding the key triggering factors for slope failure in the context of irrigation engineering, combined with attempts to develop a classification scheme for loessrelated landslides and a summary of damage patterns for Yellow River terrace landslides, is timely.…”

Section: Introductionmentioning

confidence: 99%

Effects of Irrigation Projects on the Classification of Yellow River Terrace Landslides and their Failure Modes: A Case Study of Heitai Terrace

Zhang,

Zeng,

Zhao

et al. 2023

The study of the classification and failure modes of Yellow River terrace landslides under the influence of irrigation projects is of key importance to alleviate the paradox between the rapid evolution of terrace landscapes caused by landslides and the survival of local residents. However, such studies remain controversial, despite it being widely recognized that a rise in groundwater level caused by irrigation is a key factor associated with landslide failure modes. In this paper, we take the Heitai terrace as a case study. Using aerial images and field investigations, we classify landslides in the Heitai loess layer into type A landslides (not related to groundwater) and type B1 and B2 landslides (related to groundwater). We analyze the failure modes and disaster-causing characteristics of each type of landslide, and our results indicate that the attenuation in soil strength is a key factor common to both type A and type B landslides, based on which type A landslides with small volume and short sliding distance are able to block the previous spring discharge, causing a rise in localized groundwater, which further contributes to type B landslides; the location of previous type B1 landslides with a large volume and long sliding distance and type A landslides may be more susceptible to type B2 landslides with a small volume and short sliding distance, where there are low confining pressures during the lower soil shear process. Therefore, we believe that the inevitable interaction effects between the failure modes of landslides during landslide evolution, which govern the geomorphological evolution of the Heitai terrace, are unavoidable. Combining these data with numerical analyses, we further demonstrate that a rise in groundwater level and discontinuous attenuation of soil strength caused by changes in soil properties during irrigation together control terrace landslides and their failure modes. From the results of interferometric synthetic aperture radar time-series monitoring of Yellow River terrace activity with and without irrigation projects, and electrical resistivity tomography groundwater detection, we conclude that in the future, Heitai terrace will continue to experience a high intensity of landslide activity, and conditions for the most catastrophic type of landslide (type B1) will remain, including the high localized groundwater caused by previous landslides, and the discontinuous attenuation of soil strength caused by the deterioration in soil properties. In this context, we believe that slope-cutting engineering will be one of the most economical means to achieve future landslide-type transformation on the Heitai terrace; this will mitigate the process of geomorphological evolution and improve the human living environment.

“…Landslides are one of the most devastating natural disasters, causing huge economic losses and thousands of deaths every year around the world [1][2][3]. Affected by destructive earthquakes [4][5][6], rapid snowmelts [7][8][9], intense rainfalls [10][11][12], and human activities [13][14][15][16], landslides may tend to occur in susceptible areas, posing a great threat to local residents [17]. Rapidly obtaining the scopes of active potential landslides is helpful in order to take effective measures to save people's lives and property before disaster and provide useful information regarding landslide risk management for governments [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Liang,

Zhang,

et al. 2023

Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management.