Landslides最新文献

{"title":"Study on the spatial distribution pattern of correlation between surface deformation and reservoir water level in the Three Gorges Reservoir area","authors":"Hongwei Sang, Dan Zhang, Chengcheng Zhang, Chuanjie Xi, Ke Fang, Bin Shi, Ling Chang","doi":"10.1007/s10346-024-02356-z","DOIUrl":"https://doi.org/10.1007/s10346-024-02356-z","url":null,"abstract":"<p>The deformation characteristics of landslides in the Three Gorges Reservoir area have been proven to be closely related to periodic reservoir water level fluctuations and seasonal rainfall. However, most past studies have focused on the deformation characteristics of a single landslide that occurred on the reservoir bank and the influence pattern of reservoir water level. The spatial influence characteristics of reservoir water level need further analysis. To do so, we used multi-temporal InSAR monitoring technology with the Sentinel-1A images acquired between 2017 and 2019 to generate hillslope deformation time series data at the front edge of the Three Gorges Reservoir area. By conducting Pearson correlation coefficient analysis on the deformation and reservoir water level, it was found that in space, the slope units closer to the reservoir have a more significant response to the negative correlation with the reservoir water level: Within 2 km of the reservoir, the response relationship is the best, gradually weaken in turn, and is almost marginal at and beyond about 7 km. Meanwhile, the analysis between seasonal rainfall and relative deformation also reflects that the reservoir water level is the main controlling factor of hillslope deformation in the reservoir area. In short, the reservoir water level parameter does not have spatial attributes that are reflected in space using the Pearson correlation coefficient, supplementing a new reference index for stability analysis of landslides in the reservoir area, which is of great value for risk analysis in the reservoir area.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"50 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data","authors":"Nirdesh Sharma, Manabendra Saharia","doi":"10.1007/s10346-024-02360-3","DOIUrl":"https://doi.org/10.1007/s10346-024-02360-3","url":null,"abstract":"<p>Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-resolution imagery, which are both costly and time-consuming. While existing machine learning-based automated mapping methods exist, they have limited transferability due to low availability of training data and the inability to handle out-of-distribution scenarios. This study introduces ML-CASCADE, a user-friendly open-source tool designed for real-time landslide mapping. It is a semi-automated tool that requires the user to create landslide and non-landslide samples using pre- and post-landslide Sentinel-2 imagery to train a machine learning model. The model training features include Sentinel-2 data, terrain data, vegetation indices, and bare soil index. ML-CASCADE is developed as an easy-to-use application on top of Google Earth Engine and supports both pixel and object-based classification methods. We validate the landslide extent developed using ML-CASCADE with independent expert-developed inventories. ML-CASCADE is not only able to identify the landslide extent accurately but can also map a complex cluster of landslides within 5 min and a simple landslide within 2 min. Due to its ease of use, speed, and accuracy, ML-CASCADE will serve as a critical operational asset for landslide risk management.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"77 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 15-year history of repeated ice-rock avalanches from a single source area in the Qinghai-Tibet Plateau","authors":"Qiankuan Wang, Bin Li, Aiguo Xing, Yiwei Liu, Yu Zhuang","doi":"10.1007/s10346-024-02355-0","DOIUrl":"https://doi.org/10.1007/s10346-024-02355-0","url":null,"abstract":"<p>Four repeated ice-rock avalanches occurred in the Amney Machen Mountains between 2004 and 2019, exhibiting both spatial and temporal characteristics of a recurring disaster chain. These events serve as notable examples of large-scale ice-rock avalanche chain disasters in the Qinghai-Tibet Plateau. Integrating multi-source data including remote sensing imaging, meteorological records, and glacier field observations, the geological and climatic characteristics of recurring ice-rock avalanches were characterized. A comprehensive study of the dynamic glacial changes in the region using multiple approaches, including offset-tracking and support vector machine classification, reveals the underlying triggering mechanisms and spatio-temporal evolution of the ice-rock avalanches. To investigate the possible impact of seismic events on the occurrence of repeated ice-rock avalanches, we performed a time-series analysis of the avalanche-prone mass to assess disaster risk. The results suggest that ice-rock avalanches in Amney Machen are caused by long-term climate warming, short-term meteorological fluctuations, glacier retreats, and patterns of ablation rather than earthquakes. Regional warming has culminated in glacier melt and de-buttressing, while freeze–thaw cycles have caused the propagation of stress crevasses and the deterioration of the ice-rock masses. Meltwater and rainfall introduce external driving forces to the ice-rock system, acting to lubricate and soften the bedrock and accelerate glacier sliding. The glacier’s ablation pattern, characterized by significant thinning at its lower part and slight thinning or even localized thickening at the top, has further heightened the hazard of glacial disasters. Our findings reveal the characteristics, triggering mechanisms, and spatio-temporal evolution of typical ice-rock avalanches, providing insights for monitoring and preventing glacial disasters throughout the Qinghai-Tibet Plateau.\u0000</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"4 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of Storm Alex on the Vievola catchment: a quantitative analysis of sediment volume and morphological changes in the Roya River tributaries","authors":"Raphaël Kerverdo, Sara Lafuerza, Christian Gorini, Alain Rabaute, Didier Granjeon, Rémy Deschamps, Eric Fouache, Mina Jafari, Pierre-Yves Lagrée","doi":"10.1007/s10346-024-02361-2","DOIUrl":"https://doi.org/10.1007/s10346-024-02361-2","url":null,"abstract":"<p>This study investigates the sediment dynamics resulting from the extreme Storm Alex in October 2020 in the Roya Valley and its tributaries in the Alpes-Maritimes region, France. The storm, triggered by a low-pressure system, led to unprecedented rainfall, causing extensive flooding and erosion in the region. Despite limited pre-flood data, the study employs aerial and satellite imagery, digital elevation models, and field surveys to quantify sediment mobilization and its effects on the Viévola alluvial fan in the Roya Valley. The Roya Valley’s complex geomorphology, characterized by steep gradients, gullies, and torrential streams, played a significant role in sediment transport. The study reveals that the Dente and Rabay torrents were major sediment contributors, with gullies in these areas producing substantial erosion. Bank erosion in the Dente valley was particularly prominent, attributed to geological factors and glacial deposits. The analysis, relying on topographical comparisons and digital data, assesses sediment volumes eroded and deposited during the event. Despite challenges in data quality, the study offers valuable insights into sediment dynamics during extreme hydro-sedimentary events. The Viévola catchment area is a focal point, emphasizing the importance of scree and fluvio-glacial deposits as primary sources of sediment. The findings emphasize the need for improved pre-event data and monitoring in mountainous regions susceptible to extreme events. The study’s methodology, despite limitations, contributes to a better understanding of geomorphic responses to extreme events. Expanding similar studies to cover a wider range of catchment areas and incorporating field data offers potential for enhanced hazard assessment and management strategies. The research underscores the critical role of sediment transport in shaping landscapes and impacting human infrastructure during extreme flood events.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"26 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating landslide data balancing for susceptibility mapping using generative and machine learning models","authors":"Yuhang Jiang, Wei Wang, Lifang Zou, Yajun Cao, Wei-Chau Xie","doi":"10.1007/s10346-024-02352-3","DOIUrl":"https://doi.org/10.1007/s10346-024-02352-3","url":null,"abstract":"<p>With the development and application of machine learning, significant advances have been made in landslide susceptibility mapping. However, due to challenges in actual field landslide investigations, current landslide susceptibility mapping is usually characterized by insufficient landslide samples (positive samples) and low reliability of non-landslide samples (negative samples). Considering Lianghe County in Yunnan Province, China, as an example, this paper aims to research the effectiveness of three oversampling models in generating positive samples for landslides: Conditional Tabular Generative Adversarial Networks (CTGAN), Generative Adversarial Networks (GAN), and the traditional Synthetic Minority Oversampling Technique (SMOTE) algorithms. Additionally, three machine learning methods, including 1D Convolutional Neural Network-Long Short-Term Memory Neural Network (CNN-LSTM), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) classifiers, are used for landslide susceptibility assessment. We also devise a non-landslide data (negative samples) screening method utilizing a self-trained support vector machine within a semi-supervised framework. The results show that by training on the dataset after negative sample screening, the AUC values for the 1D-CNN-LSTM, RF, and GBDT models have shown significant improvement, increasing from (0.778, 0.869, 0.849) to (0.837, 0.936, 0.877). Compared with the original training set, the prediction accuracy of the three machine learning models is improved after training on the augmented data by CTGAN, GAN, and SMOTE models. The RF model, augmented with 200 positive samples generated by CTGAN, achieves the highest prediction accuracy in the study (AUC = 0.962). The 1D CNN-LSTM model achieves its highest prediction accuracy (AUC = 0.953) when augmented with 200 positive samples from GAN. Similarly, the GBDT model reaches its highest prediction accuracy (AUC = 0.928) when augmented with 200 positive samples created by SMOTE. In addition, the spatial distribution of data indicates that the data generated by the generative adversarial model exhibits higher diversity, which can be used for landslide susceptibility assessment.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"79 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal patterns of non-seismic fatal landslides in China from 2010 to 2022","authors":"Zhuoyang Li, Meihuan Yang, Haijun Qiu, Tao Wang, Mohib Ullah, Dongdong Yang, Tianqing Wang","doi":"10.1007/s10346-024-02362-1","DOIUrl":"https://doi.org/10.1007/s10346-024-02362-1","url":null,"abstract":"<p>Landslides represent a major global natural disaster, often leading to severe consequences, including substantial loss of life and property. However, research on the spatiotemporal distribution characteristics of fatal landslide events across different climate regions and their association with precipitation remains limited. In this study, we compiled a database of non-seismic fatal landslides in China from 2010 to 2022 to examine their spatiotemporal distribution and relationship with precipitation. From 2010 to 2022, China experienced a total of 710 fatal landslide events, causing 5158 fatalities. The data revealed a declining trend in both the number of fatal landslides and associated fatalities, with the number of fatal landslides demonstrating a recurring cycle of 3–4 years marked by continuous decreases within each cycle. The initial year of a new cycle witnessed a significant increase in the number of fatal landslides, suggesting a periodic occurrence, which is related to El Niño. The central subtropical humid region recorded the highest number of fatal landslide events, attributed to its highest annual precipitation. The trend in fatal landslides closely corresponded with variations in precipitation, increasing in spring and summer and decreasing in autumn and winter. The cumulative frequency distributions of fatal landslides and fatalities followed a power-law distribution, with a sharp decline observed when exceeding a certain value, indicating a deflection effect. Despite the low population density, the plateau climate region has the highest risk of life loss among all climate regions. Understanding the spatial distribution of non-seismic fatal landslides can significantly aid in formulating more effective disaster prevention and mitigation policies.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"11 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: The Fuyang shallow landslides triggered by an extreme rainstorm on 22 July 2023 in Zhejiang, China","authors":"Qing Lü, Junyu Wu, Zhenghua Liu, Zhongxuan Liao, Zihao Deng","doi":"10.1007/s10346-024-02343-4","DOIUrl":"https://doi.org/10.1007/s10346-024-02343-4","url":null,"abstract":"","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"43 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shear constitutive model for various shear behaviors of landslide slip zone soil","authors":"Zongxing Zou, Yinfeng Luo, Yu Tao, Jinge Wang, Haojie Duan","doi":"10.1007/s10346-024-02345-2","DOIUrl":"https://doi.org/10.1007/s10346-024-02345-2","url":null,"abstract":"<p>Soil constitutive models are widely investigated and applied in soil mechanical behaviors simulation; however, the damage evolution process of soil with various shear deformation behaviors was rarely studied. This study introduces a novel shear constitutive model for slip zone soil considering its damage evolution process. Firstly, an innovative method for determining the shear stiffness is proposed to assess the damage degree of slip zone soil during shear deformation. Further, a damage evolution model based on the log-logistic function is derived to characterize the damage evolution process of slip zone soil, and a new shear constitutive model based on the damage evolution process is subsequently proposed. Both the damage evolution model and the shear constitutive model are verified by the ring shear test data of the slip zone soil from the Outang landslide in the Three Gorges Reservoir area of China. Compared to the traditional peak-solving constitutive model based on the Weibull distribution, the proposed shear constitutive model has the distinct advantage of describing not only the brittle (strain softening) mechanical behavior but also the ductile and plastic hardening mechanical behavior of soil. In summary, this method offers a rapid determination of the damage evolution process and the shear behavior constitutive relationship of slip zone soil in landslides.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"30 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From mixed to hybrid facies volcanic debris avalanche at Colima Volcano: sedimentology and numerical modeling as evidence of transport and emplacement mechanisms","authors":"Matteo Roverato, Lucia Capra","doi":"10.1007/s10346-024-02335-4","DOIUrl":"https://doi.org/10.1007/s10346-024-02335-4","url":null,"abstract":"<p>Numerous partial collapses of Colima Volcano have occurred in its history, accompanied by the emplacement of volcanic debris avalanche deposits (VDADs). The collapse that generated the Tonila VDAD (T-VDAD; ~ 1 km³; ~ 15Ka cal. BP) occurred during “wet” paleoclimatic conditions in a high humidity environment, and water within the volcanic edifice, which played a significant role in the volcano’s instability and avalanche transport. This study aims to provide new data on the processes involved in the transport and emplacement mechanisms of debris avalanches based on a detailed granulometric and microtextural characterization and numerical modeling. In general, T-VDAD exhibited massive dynamic behavior during its transport, without segregation process, although some variation of the grains-size occurs from proximal to distal reaches from the source. At microscopic level, evidence suggests particle–particle interactions of rapid, high-energy, high velocity collisional nature, promoting comminution, which increases the fines content with distance. The general high content of fine material into the T-VDAD, combined with a significant water content within the mass before the collapse, due to partial edifice saturation, may have contributed to enhance its mobility. The T-VDAD mobility is here tested with the Titan2d numerical model; results show important paleo-topography implications and that the Coulomb frictional model with basal friction angles similar to previously tested cases best fits the areal propagation of the T-VDAD, confirming that, despite the fluid content that enhanced downslope transformation, the flow still behaved as a homogeneous and incompressible continuum with energy dissipation concentrated within its base.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"27 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early warning of landslides based on statistical analysis of landslide motion characteristics and AI Earth Cloud InSAR processing system: a case study of the Zhenxiong landslide in Yunnan Province, China","authors":"Bingquan Li, Yongsheng Li, Ruiqing Niu, Tengfei Xue, Huizhi Duan","doi":"10.1007/s10346-024-02350-5","DOIUrl":"https://doi.org/10.1007/s10346-024-02350-5","url":null,"abstract":"<p>Landslides, as a common natural disaster, pose a significant threat to human society and the natural environment, including loss of life, economic damage, and environmental destruction. Effective landslide early warning is key to reducing these negative impacts. However, current warning methods face two major challenges: one is the reliance on static threshold judgments, which not only easily leads to false and missed alarms but also cannot adapt to complex and changing natural conditions. The second is the lack of ground data support in areas with complex terrain, which greatly limits the application range and accuracy of traditional warning methods. To overcome these challenges, this study designed an efficient processing system for Interferometric Synthetic Aperture Radar (InSAR) based on the (Artificial Intelligence) AI Earth Cloud platform, integrated with the Comprehensive Standardized Deformation Index (CSDI) approach, to provide an early warning analysis for the Zhenxiong landslide in Yunnan Province, China on January 22, 2024. Utilizing the cloud platform for rapid generation of deformation rates and selection of characteristic deformation points to reflect landslide trends, and applying the CSDI method for time-displacement curve analysis, enabled a fast and accurate landslide early warning. The research results show that the method proposed in this study can effectively warn of landslide events, significantly improving the accuracy and practicality of the warning. By combining InSAR technology with the CSDI model, this study not only addresses the challenges faced by traditional methods but also provides new insights and solutions in the field of landslide early warning, demonstrating the great potential of technological innovation in natural disaster management.\u0000</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"2 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}