Topic Editors

National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, Chongqing, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China

Landslides and Natural Resources

Abstract submission deadline
25 August 2024
Manuscript submission deadline
25 October 2024
Viewed by
8730

Topic Information

Dear Colleagues,

Landslides are natural hazards that pose significant threats to human lives, infrastructure, and the environment. They are also closely related to the availability and management of natural resources, such as water, soil, minerals, and energy. With the impact of global climate change and the intensification of human activities, landslides and natural resources and their interactions represent great challenges. Understanding the causes, mechanisms, impacts, and mitigation of landslides, though state-of-the-art methodology and technology including AI and remote sensing, is essential for natural resource utilization, sustainable development, and disaster risk reduction. This Topic aims to provide a platform for researchers and practitioners to share their latest findings and insights on landslides and natural resources. It will cover a wide range of topics, including but not limited to the following:

  • Landslide inventory, mapping, monitoring, and modeling;
  • Landslide susceptibility, hazard, and risk assessment;
  • Landslide-triggering factors and processes;
  • Effects of landslides on water resources and hydrological systems;
  • Impacts of landslides on soil quality and erosion;
  • Interactions of landslides with mining activities and mineral resources;
  • Influences of landslides on energy production, transportation, and consumption;
  • Mitigation measures and technologies;
  • Landslide and land use planning;
  • Machine learning for landslide mapping and prediction;
  • Remote sensing for landslide investigation.

We look forward to receiving your contributions.

Prof. Dr. Haijia Wen
Dr. Weile Li
Prof. Dr. Chong Xu
Topic Editors

Keywords

  • landslides
  • natural resources
  • water
  • soil
  • minerals
  • energy
  • hazard
  • risk
  • mitigation
  • AI
  • remote sensing

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Geosciences
geosciences
2.7 5.2 2011 23.6 Days CHF 1800 Submit
Land
land
3.9 3.7 2012 14.8 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Water
water
3.4 5.5 2009 16.5 Days CHF 2600 Submit

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Published Papers (8 papers)

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21 pages, 7551 KiB  
Article
Experimental Study on the Clogging Performance of Waste Slag
by Shibo Li, Jinduo Chen, Jianquan Ma, Hao Li, Hao Guo, Yongqiang Qiu, Fuli Han and Yashu Ji
Water 2024, 16(10), 1390; https://doi.org/10.3390/w16101390 - 13 May 2024
Viewed by 292
Abstract
The fine particles around a mining area are easy to transport under the climatic and hydrological actions such as rainfall, that causes the change in the permeability of accumulated slag and increases the hazard probability of slag debris flow. In this study, eight [...] Read more.
The fine particles around a mining area are easy to transport under the climatic and hydrological actions such as rainfall, that causes the change in the permeability of accumulated slag and increases the hazard probability of slag debris flow. In this study, eight experiments were designed to discuss the influence of fine particle migration on the permeability characteristics and clogging of slag accumulation in different graded particles and different dry densities. The results of experiments with coarse slags of five different particle sizes show that the ratio ranging from four to six in the coarse slag size and fine size caused a significant clogging phenomenon. It is confirmed that the shape of the particles is one of the factors affecting the clogging of coarse soil besides the coarse and fine particle size, and the clogging assessment criterion for slag and the corresponding clogging patterns based on the slag shape characteristics are given. And through three kinds of different dry density-graded slag, three clogging experiments were completed to verify the clogging standard and clogging particle size. The experimental results show that the clogging particle size obtained by the clogging criteria can effectively reduce the permeability of slag accumulation, and it is considered that the equivalent particle size and particle shape characteristics are the main factors affecting the clogging performance of accumulation, while the dry density of deposits has no significant influence on it. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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19 pages, 34034 KiB  
Article
Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network
by Qiong Wu, Daqing Ge, Junchuan Yu, Ling Zhang, Yanni Ma, Yangyang Chen, Xiangxing Wan, Yu Wang and Li Zhang
Remote Sens. 2024, 16(6), 1090; https://doi.org/10.3390/rs16061090 - 20 Mar 2024
Viewed by 686
Abstract
Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR [...] Read more.
Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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35 pages, 10408 KiB  
Article
Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method
by Houlu Li, Bill X. Hu, Bo Lin, Sihong Zhu, Fanqi Meng and Yufei Li
Water 2024, 16(5), 709; https://doi.org/10.3390/w16050709 - 28 Feb 2024
Viewed by 715
Abstract
The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, [...] Read more.
The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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23 pages, 37124 KiB  
Article
Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China
by Yunfeng Shan, Zhou Xu, Shengsen Zhou, Huiyan Lu, Wenlong Yu, Zhigang Li, Xiong Cao, Pengfei Li and Weile Li
Remote Sens. 2024, 16(1), 99; https://doi.org/10.3390/rs16010099 - 26 Dec 2023
Cited by 1 | Viewed by 1145
Abstract
Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring [...] Read more.
Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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21 pages, 21234 KiB  
Article
Analysis of the Spatial Distribution and Deformation Types of Active Landslides in the Upper Jinsha River, China, Using Integrated Remote Sensing Technologies
by Shengsen Zhou, Baolin Chen, Huiyan Lu, Yunfeng Shan, Zhigang Li, Pengfei Li, Xiong Cao and Weile Li
Remote Sens. 2024, 16(1), 100; https://doi.org/10.3390/rs16010100 - 26 Dec 2023
Cited by 1 | Viewed by 719
Abstract
The Upper Jinsha River (UJSR) has great water resource potential, but large-scale active landslides hinder water resource development and utilization. It is necessary to understand the spatial distribution and deformation trend of active landslides in the UJSR. In areas of high elevations, steep [...] Read more.
The Upper Jinsha River (UJSR) has great water resource potential, but large-scale active landslides hinder water resource development and utilization. It is necessary to understand the spatial distribution and deformation trend of active landslides in the UJSR. In areas of high elevations, steep terrain or otherwise inaccessible to humans, extensive landslide studies remain challenging using traditional geological surveys and monitoring equipment. Stacking interferometry synthetic aperture radar (stacking-InSAR) technology, optical satellite images and unmanned aerial vehicle (UAV) photography are applied to landslide identification. Small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) was used to obtain time-series deformation curves of samples to reveal the deformation types of active landslides. A total of 246 active landslides were identified within the study area, of which 207 were concentrated in three zones (zones I, II and III). Among the 31 landslides chosen as research samples, six were linear-type landslides, three were upward concave-type landslides, 10 were downward concave-type landslides, and 12 were step-type landslides based on the curve morphology. The results can aid in monitoring and early-warning systems for active landslides within the UJSR and provide insights for future studies on active landslides within the basin. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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25 pages, 19930 KiB  
Article
Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition
by Bin Pan and Xianjian Shi
Remote Sens. 2023, 15(23), 5619; https://doi.org/10.3390/rs15235619 - 4 Dec 2023
Viewed by 1107
Abstract
Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines [...] Read more.
Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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19 pages, 12955 KiB  
Article
Using Electrical Resistivity Tomography Method to Determine the Inner 3D Geometry and the Main Runoff Directions of the Large Active Landslide of Pie de Cuesta in the Vítor Valley (Peru)
by Yasmine Huayllazo, Rosmery Infa, Jorge Soto, Krover Lazarte, Joseph Huanca, Yovana Alvarez and Teresa Teixidó
Geosciences 2023, 13(11), 342; https://doi.org/10.3390/geosciences13110342 - 9 Nov 2023
Cited by 2 | Viewed by 1681
Abstract
Pie de Cuesta is a large landslide with a planar area of 1 km2 located in the Vítor district, in the Arequipa department (Peru), and constitutes an active phenomenon. It belongs to the rotational/translational type, which concerns cases that are very susceptible [...] Read more.
Pie de Cuesta is a large landslide with a planar area of 1 km2 located in the Vítor district, in the Arequipa department (Peru), and constitutes an active phenomenon. It belongs to the rotational/translational type, which concerns cases that are very susceptible to reactivation because any change in the water content or removal of the lower part can lead to a new instability. In this context, a previous geological study has been decisive in recognizing the lithologies present and understanding their behavior when they are saturated. But it is also necessary to know the inner “landslide geometry” in order to gusset a geotechnical diagnosis. The present study shows how the deep electrical profiles (ERT, electrical resistivity tomography method), supported by two Vp seismic refraction tomography lines (SVP), have been used to create a 3D cognitive model that would allow the identification of the inner landslide structure: the 3D rupture surface, the volume of the sliding mass infiltration sectors or fractures, and the preferred runoff directions. Moreover, on large landsides, placing the geophysical profiles is a crucial aspect because it greatly depends on the accessibility of the area and the availability of the physical space required. In our case, we need to extend profiles up to 1100 m long in order to obtain data at greater depths since this landslide is approximately 200 m tall. Based on the geophysical results and geologic information, the 3D final model of the inner structure of this landslide is presented. Additionally, the main runoff water directions and the volume of 90.5 Hm3 of the sliding mass are also estimated. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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25 pages, 11675 KiB  
Article
Updated Global Navigation Satellite System Observations and Attention-Based Convolutional Neural Network–Long Short-Term Memory Network Deep Learning Algorithms to Predict Landslide Spatiotemporal Displacement
by Beibei Yang, Zizheng Guo, Luqi Wang, Jun He, Bingqi Xia and Sayedehtahereh Vakily
Remote Sens. 2023, 15(20), 4971; https://doi.org/10.3390/rs15204971 - 15 Oct 2023
Cited by 2 | Viewed by 999
Abstract
Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various [...] Read more.
Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various regions within landslides. The present study proposes a landslide spatiotemporal displacement forecasting model by introducing attention-based deep learning algorithms based on spatiotemporal analysis. The Maximal Information Coefficient (MIC) approach is employed to quantify the spatial and temporal correlations within the daily data of Global Navigation Satellite System (GNSS) observations. Based on the quantitative spatiotemporal analysis, the proposed prediction model combines a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture spatial and temporal dependencies individually. Spatial–temporal attention mechanisms are implemented to optimize the model. Additionally, we develop a single-point prediction model using LSTM and a multiple-point prediction model using the CNN-LSTM without an attention mechanism to compare the forecasting capabilities of the attention-based CNN-LSTM model. The Outang landslide in the Three Gorges Reservoir Area (TGRA), characterized by a large and active landslide equipped with an advanced monitoring system, is taken as a studied case. The temporal MIC results shed light on the response times of monitored daily displacement to external factors, showing a lagging duration of between 10 and 50 days. The spatial MIC results indicate mutual influence among different locations within the landslide, particularly in the case of nearby sites experiencing significant deformation. The attention-based CNN-LSTM model demonstrates an impressive predictive performance across six monitoring stations within the Outang landslide area. Notably, it achieves a remarkable maximum coefficient of determination (R2) value of 0.9989, accompanied by minimum values for root mean squared error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE), specifically, 1.18 mm, 0.99 mm, and 0.33%, respectively. The proposed model excels in predicting displacements at all six monitoring points, whereas other models demonstrate strong performance at specific individual stations but lack consistent performance across all stations. This study, involving quantitative deformation characteristics analysis and spatiotemporal displacement prediction, holds promising potential for a more profound understanding of landslide evolution and a significant contribution to reducing landslide risk. Full article
(This article belongs to the Topic Landslides and Natural Resources)
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