Characteristics Of Landslides Research Articles

Responses to Landslides and Landslide Mapping on the Blue Ridge Escarpment, Polk County, North Carolina, USA

ABSTRACT Landslides occur in Polk County, North Carolina, primarily along the Columbus Promontory of Blue Ridge Escarpment (BRE), which has 400 m of topographic relief and slopes typically >20°. Bedrock is characterized as late Proterozoic to early Paleozoic metamorphic rocks within Paleozoic thrust sheets. On May 18, 2018, ∼200 mm of rainfall over a 3- to 4-hour period triggered numerous debris flows and slides along the BRE, causing one fatality and severe damage to homes and roads. The State Emergency Operations Center tasked the North Carolina Geological Survey to assess slope stability ahead of search and rescue operations and assess damage along the North Pacolet River valley. The loss of life and destruction from the 2018 storm and ongoing threats to infrastructure prompted us to map landslides throughout Polk County in 2019–2021 to fully document the 2018 landslides and place them in the context of past and ongoing landsliding. We mapped 920 varied types of landslides and attribute 241 to the 2018 storm, making it one of the largest events in North Carolina since 2004 with respect to landslide numbers and spatial frequency. The highest concentrations of landslide features in Polk County are along the slopes of the BRE, especially the Pacolet River and Green River valleys. These rivers exploit post-orogenic brittle fractures to form linear reentrants where the May 2018 and other landslides are concentrated. This article describes our landslide response and mapping efforts and relates our findings to the geomorphic and geologic framework and to past landslide events in the region.

Landslide susceptibility mapping: an integrated approach using knowledge-based numerical rating scheme, remote sensing, and multiple overlay analysis

Landslides are natural calamities that happen all over the world. Every year, landslides claim the lives of thousands of people and cause significant damage worldwide. The rapid and unpredictable expansion of many cities has a significant impact on the physical environment, with rapidly urbanizing regions of uneven topography where environmental circumstances make the construction of structures and infrastructure impossible or susceptible to instability. Our work proposes the integration of remote sensing, a knowledge-based numerical rating scheme, and multiple overlay analysis methodologies for landslide susceptibility mapping. We evaluate binary overlay, weighted overlay, and fuzzy overlay to map landslide vulnerability in the Ambegaon taluka of Pune district, Maharashtra. The input information used for the prediction and valuation of the landslide susceptibility map (LSM) includes an inventory of 39 active landslide points and 10 potential causality features of landslides: rainfall, slope, aspect, curvature, elevation, Euclidean distance to streamline, normalized difference vegetation index, topographic wetness index, Euclidean distance to roadline, and lithology. The area of consideration was categorized into five main susceptibility groups based on the computed landslide susceptibility scale, ranging from extremely low to severe. The LSM created using the fuzzy gamma operator (k = 0.95) has an overall forecast and prediction accuracy of 89.74%. The optimum value of the gamma operator (k) for this research is 0.95. The LSM was verified by correlating the frequency of landslides on different hazard classes. Implementation of this LSM at the regional level offers a foundation for expanding the approach to other geographies.

Characteristics of landslides triggered by the 2013 ML6.5 Nantou, Taiwan, earthquake

Earthquake-triggered landslides are common disasters of active mountain belts. Due to the lack of earthquake-triggered landslide inventory in Taiwan, it is not intuitive to observe spatial relationships and discover unique patterns between landslides and essential triggers. We examined strong earthquake events in Taiwan after the 1999 Mw7.6 Chi-Chi earthquake and targeted the 2013 ML6.5 Nantou earthquake to create the landslide inventory. We adopted two Landsat-8 satellite images before and after the event to detect landslides, and incorporated a 20-m DEM and rock type data of Taiwan to represent key factors triggering earthquake-induced landslides such as peak ground acceleration (PGA), lithology, slope roughness, slope, and aspect. Based on the analysis of the density of landslides, there are strong correlations between the landslide occurrence and seismic and geomorphic factors. Furthermore, we noticed that the landslide aspects have a systematic tendency towards the northeast, which is not correlated with the dip directions and wave propagation directions. Instead, we found that the northeastward landslide aspect is more associated with the westward–southwestward surface movement at the landslides. We found that the included angles between the landslide aspects and the displacement directions for all the landslides are ~ 100°–180°. The relationship indicated that the coseismic deformation of the Nantou earthquake may play a role in the landslide distribution.Graphical

Kinematic Behavior Analysis of the Wadi Landslide From Time-Series Sentinel-1 Data

The analysis of kinematic characteristics and triggering factors of the potentially unstable slopes is of great significance for the slope protection and landslide reinforcing. Located in Mao Country, Sichuan Province in China, Wadi village is a typical mountainous area where geological disasters frequently occur due to the complex geological environment, intense tectonic activities, and concentrated seasonal rainfall. However, the slope stability and potential risk over this area are not fully evaluated yet. In this article, we investigate the unstable slope in Wadi village using time-series synthetic aperture radar interferometry (InSAR) technique. The deformation history retrieved from 132 C-band descending Sentinel-1 images between October 2014 and September 2019 demonstrates that the Wadi landslide presents a slow sliding trend with the averaged line-of-sight velocity of −70 mm/year and has a distinct seasonal velocity at the head and toe area. We further decompose the predominant and periodic velocities by a kinematic model to analyses spatial and temporal characteristics of Wadi landslide. The results reveal that the Wadi landslide is a hybrid type of push and pull with two source areas in the head and toe of the slope, respectively. The periodic velocity variations are highly correlated with seasonal rainfall. Our study also demonstrates the importance of correcting InSAR unwrapping error for interpreting the landslide kinematics and other small-scale geomorphological processes.

Failure characteristics of the reservoir landslide and surge calculations

Failure characteristics of the reservoir landslide and surge calculations

Landslide detection of optical remote sensing image based on attention and u-net

With the improvement of remote sensing image technology, researchers pay more and more attention to detecting landslides in optical remote sensing images. In this paper, the landslide is detected by semantic segmentation model based on deep learning, U-shaped network is used to enhance the extraction ability of landslide features, and the model pays more attention to landslide area through attention mechanism, so as to make the model detect landslide more accurately. Through experiments on the Bijie Landslide Dataset, the values of OA and mIoU in this model are increased by 1% and 16% respectively. The boundary of landslide is more straightforward and more accurate.

Reg-SA–UNet++: A Lightweight Landslide Detection Network Based on Single-Temporal Images Captured Postlandslide

Landslide detection based on remote sensing images is an effective method for rapidly and accurately detecting landslide regions, which can aid in disaster prevention and mitigation. Landslide detection methods based on semantic segmentation can be used to delineate the scope of landslides while detecting their location. Most existing models use multi-temporal or geological data to improve accuracy. However, the large amount of data introduces additional parameters, consuming significant computing resources. Therefore, this study proposes Reg-SA–UNet++, a model for landslide detection, which uses a single-temporal image captured post-landslide. Reg-SA–UNet++ is based on UNet++ with the following modifications: deep supervised pruning is removed for fewer model parameters and increased detection accuracy; RegNet is employed to replace the convolutional blocks in the encoding process to reduce the number of parameters and improve feature acquisition and attention modules are added at the connection of the convolutional blocks of each layer to strengthen the model's attention to landslide features. The overall accuracy and F1 score of the Reg-SA–UNet++ model for the constructed landslide dataset (93.37% and 92.41%, respectively) and landslide mapping (97.09% and 96.10%, respectively) verify the effectiveness of the proposed model in detecting landslides from remote sensing images.

Inventory and Distribution Characteristics of Large-Scale Landslides in Baoji City, Shaanxi Province, China

Inventories of historical landslides play an important role in the assessment of natural hazards. In this study, we used high-resolution satellite imagery from Google Earth to interpret large landslides in Baoji city, Shaanxi Province on the southwestern edge of the Loess Plateau. Then, a comprehensive and detailed map of the landslide distribution in this area was prepared in conjunction with the historical literature, which includes 3440 landslides. On this basis, eight variables, including elevation, slope, aspect, slope position, distance to the fault, land cover, lithology and distance to the stream were selected to examine their influence on the landslides in the study area. Landslide number density (LND) and landslide area percentage (LAP) were used as evaluation indicators to analyze the spatial distribution characteristics of the landslides. The results show that most of the landslides are situated at elevations from 500 to 1400 m. The LND and LAP reach their peaks at slopes of 10–20°. Slopes facing WNW and NW directions, and middle and lower slopes are more prone to sliding with higher LND and LAP. LND and LAP show a decreasing trend as the distance to the fault or stream increases, followed by a slow rise. Landslides occur primarily in the areas covered by crops. Regarding lithology, the regions covered by the Quaternary loess and Cretaceous gravels are the main areas where landslides occur. The results would be helpful for further understanding the developmental characteristics and spatial distribution of landslides on the Loess Plateau, and also provide a support to subsequent landslide susceptibility mapping in this region.

Modelling discontinuity control on the development of Hell\u2019s Mouth landslide

This paper focuses on numerical modelling and back analysis of the Hell’s Mouth landslide to provide improved understanding of the evolution of a section of the north coast of Cornwall, UK. Discontinuity control is highlighted through the formation of a ‘zawn’ or inlet, the occurrence of two successive landslides and evidence of ongoing instability through opening of tension cracks behind the cliff top. Several integrated remote sensing (RS) techniques have been utilised for data acquisition to characterise the slope geometry, landslide features and tension crack extent and development. In view of the structural control on the rock slope failures, a 3D distinct element method (DEM) code incorporating a discrete fracture network and rigid blocks has been adopted for the stability analysis. The onset and opening of tension cracks behind the modelled slope failure zones has also been studied by analysing the displacements of two adjoining landslide blocks, between which, a joint-related tension crack developed. In addition, a sensitivity analysis has been undertaken to provide further insight into the influence of key discontinuity parameters (i.e. dip, dip direction, persistence and friction angle) on the stability of this section of the coastline. Numerical modelling and field observations indicate that block removal and preferential erosion along a fault resulted in the formation of the inlet. The development of the inlet provides daylighting conditions for discontinuities exposed on the inlet slope wall, triggering the initial landslide which occurred on 23rd September 2011. Numerical modelling, and evidence from a video of the initial landslide, suggests that the cliff instability is characterised by a combination of planar sliding, wedge sliding and toppling modes of failure controlled by the discrete fracture network geometry.

Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network

Considering the complexity of landslide hazards, their manual investigation lacks efficiency and is time-consuming, especially in high-altitude plateau areas. Therefore, extracting landslide information using remote sensing technology has great advantages. In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. Based on the idea of feature-driven classification, the landslide extraction model of a fully convolutional spectral–topographic fusion network (FSTF-Net) based on a deep convolutional neural network of multi-source data fusion is proposed, which takes into account the topographic factor (slope and aspect) and the normalized difference vegetation index (NDVI) as multi-source data input by which to train the model. In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction of the landslide and surrounding ground-object information. With Mangkam County in the southeast of the Qinghai–Tibet Plateau China as the study area, the proposed method was evaluated based on the high-precision digital elevation model (DEM) generated from stereoscopic images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). Results show that our method had a landslide detection precision of 0.85 and an overall classification accuracy of 0.89. Compared with the latest DeepLab_v3+, our model increases the landslide detection precision by 5%. Thus, the proposed FSTF-Net model has high reliability and robustness.

Application of InSAR satellite method for mapping of active landslides in Bulgaria – opportunities and perspectives

Abstract. Landslides are geological phenomena that are spread on Bulgarian territory mainly along the northern Black Sea coast and on the right banks of the Danube in the western part of the country. Mitigation of the negative effects of these destructive geological phenomena is the compilation of inventory maps of their distribution and registers with the main characteristics of the individual landslides. Conventional methods for making such maps are time-consuming and resource-intensive. Modern satellite, air and ground-based remote sensing technologies facilitate the production of landslide maps, reducing the time and resources required to compile and systematically update them. In this paper, we demonstrate the applicability of Differential Sentinel-1A satellite SAR interferometry (DInSAR) to assess the movement activity and use the information for further updating the national landslide inventories in Bulgaria. We perform several analyses based on multi-temporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides. The use of new opportunities for free access to satellite images, which can be applied in conjunction with other methods, greatly facilitates the processes of inventory, mapping and study of landslides.

Retraction Note to: Distribution characteristics of mountain landslides based on heterogeneous network and optimization of human resource management in agricultural enterprises

Retraction Note to: Distribution characteristics of mountain landslides based on heterogeneous network and optimization of human resource management in agricultural enterprises

Detecting Long-Term Deformation of a Loess Landslide from the Phase and Amplitude of Satellite SAR Images: A Retrospective Analysis for the Closure of a Tunnel Event

Information about the long-term spatiotemporal evolution of landslides can improve the understanding of landslides. However, since landslide deformation characteristics differ it is difficult to monitor the entire movement of a landslide using a single method. The Interferometric Synthetic Aperture Radar (InSAR) and pixel offset tracking (POT) method can complement each other when monitoring deformation at different landslide stages. Therefore, the InSAR and improved POT method were adapted to study the pre- and post-failure surface deformation characteristics of the Gaojiawan landslide to deepen understanding of the long-term spatiotemporal evolution characteristics of landslides. The results show that the deformation displacement gradient of the Gaojiawan landslide exhibited rapid movement that exceeded the measurable limit of InSAR during the first disaster. Moreover, the Gaojiawan landslide has experienced long-term creep, and while studying the post-second landslide’s failure stability, the acceleration trend was identified via time series analysis, which can be used as a precursor signal for landslide disaster warning. Our study aims to provide scientific reference for local governments to help prevent and mitigate geological disasters in this region.

Retraction Note to: Sensitivity of mountain landslide based on Markov model and aerobic training management

In this paper, we will use MATLAB to create a Markov model, determine the input and output membership functions of the study area, and import the data of the study area into the model. The ROC method is used to prove the accuracy of the sensitivity distribution map of mountain landslide disaster and the feasibility of the model in the sensitivity assessment of mountain landslide. It also shows the reliability and applicability of the selected factors. Based on GIS technology, the influencing factors of sensitivity coefficient of landslides in mountainous areas can be extracted. According to the spatial distribution characteristics of landslides, six influencing factors of landslides and their spatial distribution relationships are analyzed: height, slope, geological lithology, terrain curvature, terrain humidity index, and runoff intensity index. Training management combined with aerobic exercise is helpful to improve people’s body shape. The effect of “resistance training + aerobic exercise” training method adopted by experimental group 1 on body shape is the most significant. The arm circumference of the male experimental group increased, while that of the female experimental group decreased. In the training management, different combination methods of resistance training and aerobic exercise have no significant effect on the improvement of the muscle strength of the subjects after the experiment. By analyzing and comparing the changes of cardiopulmonary function indexes of the subjects before and after training, this paper expounds the training effect of blood flow restriction under different pressures combined with low-intensity aerobic exercise on cardiopulmonary function. In this paper, through the study of the Markov model and mountain landslide sensitivity, it is applied to aerobic training management, which promotes the better improvement of people’s body shape.

Landslide Characteristics and Evolution: What We Can Learn from Three Adjacent Landslides

Landslide processes are a consequence of the interactions between their triggers and the surrounding environment. Understanding the differences in landslide movement processes and characteristics can provide new insights for landslide prevention and mitigation. Three adjacent landslides characterized by different movement processes were triggered from August to September in 2018 in Hualong County, China. A combination of surface and subsurface characteristics illustrated that Xiongwa (XW) landslides 1 and 2 have deformed several times and exhibit significant heterogeneity, whereas the Xiashitang (XST) landslide is a typical retrogressive landslide, and its material has moved downslope along a shear surface. Time-series Interferometric Synthetic Aperture Radar (InSAR) and Differential InSAR (DInSAR) techniques were used to detect the displacement processes of these three landslides. The pre-failure displacement signals of a slow-moving landslide (the XST landslide) can be clearly revealed by using time-series InSAR. However, these sudden landslides, which are a typical catastrophic natural hazard across the globe, are easily ignored by time-series InSAR. We confirmed that effective antecedent precipitation played an important role in the three landslides’ occurrence. The deformation of an existing landslide itself can also trigger new adjacent landslides in this study. These findings indicate that landslide early warnings are still a challenge since landslide processes and mechanisms are complicated. We need to learn to live with natural disasters, and more relevant detection and field investigations should be conducted for landslide risk mitigation.

GIS-Based Landslide Disaster Risk Areas and Ground Movements Mapping to Support Disaster Mitigation Activities (Case Study: Tasikmalaya Regency)

Tasikmalaya Regency is an area that is prone to landslides and ground movements. The availability of comprehensive and accurate information in controlling land use for regional development in areas prone to landslides and ground movements is very important for casualty prevention and other losses such as physical, social and economic. This information must be disseminated to the public as an early warning system to support disaster mitigation efforts. Identification of the characteristics of landslide and ground movement prone areas requires the mapping of risk areas to mitigate disasters. This can be done using Geographic Information System (GIS). This mapping activity was carried out using the method of collecting digital data from five vulnerability controlling factors of landslides and ground movements, namely rainfall, rock type, soil type, land cover and slope. The analysis is continued by weighting the factors that influence landslides and ground motion and then overlaying the five controlling factor maps (with their respective weight values) to produce a landslides and ground movement vulnerability level map which is then inputted into WebGIS. This map can then be used by local governments and the public as an information medium to support disaster mitigation activities.

Environmental Displacement and Mental Well-Being in Banjarnegara, Indonesia.

Background:Residential moves (displacement) owing to climate- and weather-related disasters may significantly impact mental health. Despite the growing risk from climate change, health impacts of environmental-mobility remain understudied.Objectives:We assessed the effects of displacement on the association between landslides and changes in perceived mental well-being in Banjarnegara, Indonesia. We also investigated whether sociodemographics (age, sex, level of education, household-level income, or employment in agriculture) and landslide characteristics (number and severity of landslides) were associated with differing odds of relocation after experiencing landslides.Methods:In this cross-sectional study, we surveyed 420 individuals who experienced landslides between 2014 and 2018 to assess perceived changes in mental well-being, comparing after landslide exposure to before landslide exposure. We used a novel six-item measure that was created in collaboration with the local community to compare perceived changes between those who were displaced by landslides and those who were not displaced, using logistic and multinomial regressions adjusted for sociodemographic characteristics. We then assessed whether the odds of displacement differed based on sociodemographic characteristics and landslide exposure characteristics, using logistic regressions.Results:Those who were displaced were more likely than those who were not displaced to report perceived increases in economic stability [; 95% confidence interval (CI): 1.45, 6.46], optimism (; 95% CI: 1.87, 8.61), safety (; 95% CI: 1.44, 5.10), religiosity (; 95% CI: 1.03, 3.65), and closeness with community (; 95% CI: 1.10, 3.33) after landslides compared with before their first landslide during the study period. More frequent landslide exposures were associated with reduced odds of relocation, but more severe landslides were associated with increased odds of relocation.Discussion:These findings suggest that landslides affect the mental well-being not only of those who are displaced but also of those who are left behind. Further, this work supports the need for community-based participatory research to fully capture the health impacts of environmental mobility. https://doi.org/10.1289/EHP9391

Characteristics of Landslide and Rainfall Areas in Majalengka Regency, West Java Province

Majalengka Regency is one of the districts with a high landslide hazard in West Java Province. They are mostly affected by heavy rainfall or prolonged rain. This study aims to classify landslide events in 2018-2019 based on physical factors consisting of slopes, soil types, lithology, land use, and vegetation density using the K-Means Clustering analysis method. To analyze the characteristics of rainfall that triggered landslides in 2018-2019 using the Thiessen polygon method. The results showed that the clustering of landslide events in 2018-2019 in Majalengka Regency was formed five clusters with the highest rainfall on the D-Day average in cluster 5, which is 49 mm/day. The highest average cumulative rainfall 3 days before the landslide events was in cluster 4, which is 80 mm/day. The highest average cumulative rainfall 5 days before the landslide events was in cluster 3 is 112 mm/day. The highest average cumulative rainfall 10 days before the landslide events was in cluster 1, which is 174 mm/day.

AI-Landslide: Software for acquiring hidden insights from global landslide data using Artificial Intelligence

<h2>Abstract</h2> AI-Landslide is a decision support software for a city planner, disaster recovery strategist or landslide researcher. The software can be deployed in a number of platforms including mobile phones (Android or iOS), tablets or desktop environments. AI-Landslide uses AI based algorithms like automated regression analysis, decomposition analysis and anomaly detection to find out hidden insights on landslide features. The user of AI-landslide does not need to have prior knowledge on AI algorithms, since the right algorithms are executed in the background and the hidden insights on the landslide data are presented in a natural language. AI-Landslide is available at https://github.com/DrSufi/GlobalLandslide.

Retraction Note to: Monitoring and early warning of loess landslide based on distributed environment and effectiveness calculation of physical training

Loess landslide has become a major geological hazard due to its high distribution, high occurrence frequency, and high loss. More than 1800 landslides occur every year in China, causing an average of more than 400 deaths every year, and the direct economic loss is as high as 4.6 billion yuan. This is a serious threat to the lives and property safety of the people in the disaster area, and caused huge losses to the country. China’s “one belt, one road” like a cooperative project has been like a raging fire in recent years, and the new mode of economic development and regional cooperation has been opened. However, one belt, one road, is seriously threatened by landslides. From 1900 to 2015 one belt, one road, along the more than 330 landslides, caused more than 20 thousand deaths. Therefore, this document collects the basic x-di loess data, uses ArcGIS software and distributed environment network, analyzes the spatial and temporal distribution characteristics of landslides, obtains the factors affecting landslides, and develops the loess landslide monitoring and early warning system. Then this paper studies the sports training, as an important part of college physical education curriculum, should carry out the corresponding reform to play a positive role and effectiveness of sports training. Some colleges and universities lack the scientific management of sports teams in the process of sports training. The overall quality and teaching ability of teachers are not high. We must cultivate students’ interest and further strengthen the reform of sports teaching. It is necessary to strengthen the psychological training of teachers and students, improve the training methods and standard training, in order to improve the effect of sports training. In this paper, through the study of loess landslide geological disasters, the monitoring, and early warning system is developed, which is applied to sports training to make sports training more effective.