Landslides and Natural Resources

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Geosciences geosciences	2.4	5.3	2011	23.5 Days	CHF 1800
Land land	3.2	4.9	2012	16.9 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Water water	3.0	5.8	2009	17.5 Days	CHF 2600

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Published Papers (17 papers)

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21 pages, 25173 KiB  

Open AccessArticle

Effects of Freeze–Thaw and Dry–Wet Cycles on the Collapsibility of the Ili Loess with Variable Initial Moisture Contents

by Lilong Cheng, Zizhao Zhang, Chenxin Liu, Yongliang Zhang, Qianli Lv, Yanyang Zhang, Kai Chen, Guangming Shi and Junpeng Huang

Land 2024, 13(11), 1931; https://doi.org/10.3390/land13111931 - 16 Nov 2024

Cited by 1 | Viewed by 920

Abstract

Exposed to seasonal climate changes, the loess in the Ili region of Xinjiang, which has variable engineering properties, frequently undergoes freezing–thawing (F-T) and wetting–drying (W-D) cycles. In the present research, a series of uniaxial compression tests were conducted to investigate the collapsibility characteristics [...] Read more.

Exposed to seasonal climate changes, the loess in the Ili region of Xinjiang, which has variable engineering properties, frequently undergoes freezing–thawing (F-T) and wetting–drying (W-D) cycles. In the present research, a series of uniaxial compression tests were conducted to investigate the collapsibility characteristics of the representative loess slope in the Ili region. In parallel, scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR) tests were conducted. The test results obtained from the research indicated that both F-T cycles and W-D cycles exacerbate the deterioration of the loess, with the most severe effects observed after 6–10 cycles. Under the combined physical cycles, the microstructure of the loess progressively evolves from the relatively aggregated state to the dispersed one. Meanwhile, the porosity of the loess exhibited an initial increase with the number of W-D cycles, followed by an obvious decrease. Note that the pattern of the loess experiences fluctuation, which was achieved at the given point with the increased number of F-T cycles. It is suggested that the variability in loess wetting collapse is attributed to the irreversible alteration in the microstructure attributed to the combined cycles. The main reasons for the occurrence of loess collapse are the frost heaving force and the swelling–shrinking action. The impacts of W-D and F-T cycles on the loess obtained from this research can make a contribution to the in-depth understanding about loess collapse in the Ili valley. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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26 pages, 284813 KiB  

Open AccessArticle

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

by Bo Deng, Qiang Xu, Xiujun Dong, Weile Li, Mingtang Wu, Yuanzhen Ju and Qiulin He

Remote Sens. 2024, 16(21), 4075; https://doi.org/10.3390/rs16214075 - 31 Oct 2024

Cited by 1 | Viewed by 1430

Abstract

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently [...] Read more.

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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24 pages, 22919 KiB  

Open AccessArticle

Critical State Analysis for Iron Ore Tailings with a Fine-Grained Interlayer: Effects of Layering Thickness and Dip Angle

by Xu Ji, Qiang Xu, Kaiyi Ren, Lanting Wei and Wensong Wang

Water 2024, 16(20), 2958; https://doi.org/10.3390/w16202958 - 17 Oct 2024

Viewed by 1085

Abstract

The formation of layering during the sedimentation process of tailings makes it of great significance to investigate tailings and to analyze their susceptibility to flow liquefaction. In this study, homogeneous iron ore tailings (IOTs) specimens were reconstituted with pure coarser grains and pure [...] Read more.

The formation of layering during the sedimentation process of tailings makes it of great significance to investigate tailings and to analyze their susceptibility to flow liquefaction. In this study, homogeneous iron ore tailings (IOTs) specimens were reconstituted with pure coarser grains and pure finer grains sampled from a typical tailings storage facility. Additionally, an improved sample preparation method was developed to create heterogeneous IOTs samples containing a fine-grained interlayer with different thicknesses and dip angles using the above two materials. A series of standard drained and undrained triaxial compression tests were conducted to investigate the effects of the presence of a layered structure and its geometry on the stress–strain responses, and the properties of the IOTs under the critical state soil mechanics framework, which has been widely adopted in the analysis of liquefaction in mine tailings. The results showed that for the two homogeneous specimens, unique critical state lines (CSLs) can be identified, but they have different degrees of curvature in the e-ln p′ plane, causing a decrease in the susceptibility to liquefaction with increasing fines content. With increasing fine-grained interlayer thickness (FGLT) within 0–40 mm, the critical state friction angle (φ_cs) decreased steadily, while the CSLs in the e-ln p′ plane translated upward. This may be because the morphology of the microstructure within the fine-grained interlayer restricted the compression of the intergranular pores. With increasing fine-grained interlayer dip angle (FGLA) within the range 0–30°, φ_cs decreased until a discontinuity occurred at a dip angle of 15°, while the CSLs in the e-ln p′ plane rotated clockwise through a pivot point. Different FGLAs could change the contact area between the different layers and the axial distribution of the fine-grained interlayer and thus may further contribute to the rotation of the CSLs. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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14 pages, 6462 KiB  

Open AccessArticle

The Variation in Boulder Bars Triggered by the 2018 Sedongpu Natural Dam Failure in the Yarlung Tsangpo River

by Xiangang Jiang, Xinlin Xie, Zhehao Guo, Anders Wörman, Xingrong Liu, Weiming Liu and Yiqin Xie

Land 2024, 13(9), 1517; https://doi.org/10.3390/land13091517 - 19 Sep 2024

Viewed by 995

Abstract

Natural dams are formed most often in narrow, steep valleys in high mountains. The outburst floods triggered by natural dam failures result in the topography and landforms successively being altered. Boulder bars are common natural structures that are selected here to quantitatively evaluate [...] Read more.

Natural dams are formed most often in narrow, steep valleys in high mountains. The outburst floods triggered by natural dam failures result in the topography and landforms successively being altered. Boulder bars are common natural structures that are selected here to quantitatively evaluate the impact of outburst floods on the topographical and landform variations in downstream channels. In this study, we selected the Sedongpu natural dam on the Yarlung Tsangpo River formed as a result of a landslide in 2018 as an example, and studied the geomorphological changes in a river reach located 173 km downstream of the Sedongpu natural dam. The sizes and shapes of the boulder bars in this area were statistically analyzed. The results show that there are three shape types of boulder bars in this area, i.e., sickle, bamboo leaf and oval. Furthermore, it found that the relationship between the lengths and widths of boulder bars is similar before and after outburst floods, as is the relationship between perimeters and lengths of boulder bars, which means these relationships are not affected by outburst floods. And the perimeters of boulder bars are almost twice their lengths. In addition, the relationship between the areas and lengths of boulder bars follows a power function. The most important finding is that the riverine morphological features conserved self-similarity due the influence of the outburst flood erosion triggered by a natural dam failure. This finding adds to the previous observations since dam failures introduce sudden and dominating impacts on river systems. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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22 pages, 75910 KiB  

Open AccessArticle

Identification and Deformation Characteristics of Active Landslides at Large Hydropower Stations at the Early Impoundment Stage: A Case Study of the Lianghekou Reservoir Area in Sichuan Province, Southwest China

by Xueqing Li, Weile Li, Zhanglei Wu, Qiang Xu, Da Zheng, Xiujun Dong, Huiyan Lu, Yunfeng Shan, Shengsen Zhou, Wenlong Yu and Xincheng Wang

Remote Sens. 2024, 16(17), 3175; https://doi.org/10.3390/rs16173175 - 28 Aug 2024

Cited by 3 | Viewed by 984

Abstract

Reservoir impoundment imposes a significant triggering effect on bank landslides. Studying the early identification of landslides and their stability concerning reservoir water levels and rainfall is vital for guaranteeing the safety of residents and infrastructure in reservoir regions. This study proposed a method [...] Read more.

Reservoir impoundment imposes a significant triggering effect on bank landslides. Studying the early identification of landslides and their stability concerning reservoir water levels and rainfall is vital for guaranteeing the safety of residents and infrastructure in reservoir regions. This study proposed a method for establishing a dynamic inventory of active landslides at large hydropower stations using integrated remote sensing techniques, demonstrated at Lianghekou Reservoir. We employed interferometric stacking synthetic aperture radar (stacking-InSAR) technology, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology, and optical satellite images to identify and catalogue active landslides. Moreover, we conducted field investigations to examine the deformation characteristics of landslides. Finally, Pearson’s correlation analysis was employed to evaluate the response between deformation values, reservoir water levels, and rainfall. The results revealed 75 active landslides, including 12 long-term active landslides before impoundment and 63 new landslides after impoundment, which were primarily concentrated in the Waduo and Yazho–Zatou regions. The correlation coefficient between landslide deformation values and the reservoir level was high (0.93), while the correlation coefficient with rainfall was low (0.57). The results of this research offer a crucial foundation for preventing and mitigating landslides in reservoir areas. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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30 pages, 27101 KiB  

Open AccessArticle

A Novel Dataset Replenishment Strategy Integrating Time-Series InSAR for Refined Landslide Susceptibility Mapping in Karst Regions

by Yajie Yang, Xianglong Ma, Wenrong Ding, Haijia Wen and Deliang Sun

Water 2024, 16(17), 2414; https://doi.org/10.3390/w16172414 - 27 Aug 2024

Cited by 1 | Viewed by 1342

Abstract

The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sample expansion strategy, achieving greater precision and [...] Read more.

The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sample expansion strategy, achieving greater precision and reliability in the landslide susceptibility model. An integrated interpretative framework for landslide susceptibility assessment is developed using the XGBoost-SHAP-PDP algorithm to deeply investigate the key contributing factors of landslides in karst areas. Firstly, 17 conditioning factors (e.g., surface deformation rate, land surface temperature, slope, lithology, and NDVI) were introduced based on field surveys, satellite imagery, and literature reviews, to construct a landslide susceptibility conditioning factor system in line with karst geomorphology characteristics. Secondly, a sample expansion strategy combining the frequency ratio (FR) with SBAS-InSAR interpretation results was proposed to optimize the landslide susceptibility assessment dataset. The XGBoost algorithm was then utilized to build the assessment model. Finally, the SHAP and PDP algorithms were applied to interpret the model, examining the primary contributing factors and their influence on landslides in karst areas from both global and single-factor perspectives. Results showed a significant improvement in model accuracy after sample expansion, with AUC values of 0.9579 and 0.9790 for the training and testing sets, respectively. The top three important factors were distance from mining sites, lithology, and NDVI, while land surface temperature, soil erosion modulus, and surface deformation rate also significantly contributed to landslide susceptibility. In summary, this paper provides an in-depth discussion of the effectiveness of LSM in predicting landslide occurrence in complex terrain environments. The reliability and accuracy of the landslide susceptibility assessment model were significantly improved by optimizing the sample dataset within the karst landscape region. In addition, the research results not only provide an essential reference for landslide prevention and control in the karst region of Southwest China and regional central engineering construction planning but also provide a scientific basis for the prevention and control of geologic hazards globally, showing a wide range of application prospects and practical significance. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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25 pages, 19447 KiB  

Open AccessArticle

Risk Mapping of Geological Hazards in Plateau Mountainous Areas Based on Multisource Remote Sensing Data Extraction and Machine Learning (Fuyuan, China)

by Shaohan Zhang, Shucheng Tan, Yongqi Sun, Duanyu Ding and Wei Yang

Land 2024, 13(9), 1361; https://doi.org/10.3390/land13091361 - 26 Aug 2024

Cited by 1 | Viewed by 1252

Abstract

Selecting the most effective prediction model and correctly identifying the main disaster-driving factors in a specific region are the keys to addressing the challenges of geological hazards. Fuyuan County is a typical plateau mountainous town, and slope geological hazards occur frequently. Therefore, it [...] Read more.

Selecting the most effective prediction model and correctly identifying the main disaster-driving factors in a specific region are the keys to addressing the challenges of geological hazards. Fuyuan County is a typical plateau mountainous town, and slope geological hazards occur frequently. Therefore, it is highly important to study the spatial distribution characteristics of hazards in this area, explore machine learning models that can be highly matched with the geological environment of the study area, and improve the accuracy and reliability of the slope geological hazard risk zoning map (SGHRZM). This paper proposes a hazard mapping research method based on multisource remote sensing data extraction and machine learning. In this study, we visualize the risk level of geological hazards in the study area according to 10 pathogenic factors. Moreover, the accuracy of the disaster point list was verified on the spot. The results show that the coupling model can maximize the respective advantages of the models used and has highest mapping accuracy, and the area under the curve (AUC) is 0.923. The random forest (RF) model was the leader in terms of which single model performed best, with an AUC of 0.909. The grid search algorithm (GSA) is an efficient parameter optimization technique that can be used as a preferred method to improve the accuracy of a model. The list of disaster points extracted from remote sensing images is highly reliable. The high-precision coupling model and the single model have good adaptability in the study area. The research results can provide not only scientific references for local government departments to carry out disaster management work but also technical support for relevant research in surrounding mountainous towns. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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28 pages, 28454 KiB  

Open AccessArticle

Landslide Distribution and Development Characteristics in the Beiluo River Basin

by Fan Liu, Yahong Deng, Tianyu Zhang, Faqiao Qian, Nan Yang, Hongquan Teng, Wei Shi and Xue Han

Land 2024, 13(7), 1038; https://doi.org/10.3390/land13071038 - 10 Jul 2024

Cited by 1 | Viewed by 1436

Abstract

The Beiluo River Basin, situated in the central region of the Loess Plateau, frequently experiences landslide geological disasters, posing a severe threat to local lives and property. Thus, establishing a detailed database of historical landslides and analyzing and revealing their development characteristics are [...] Read more.

The Beiluo River Basin, situated in the central region of the Loess Plateau, frequently experiences landslide geological disasters, posing a severe threat to local lives and property. Thus, establishing a detailed database of historical landslides and analyzing and revealing their development characteristics are of paramount importance for providing a foundation for geological hazard risk assessment. First, in this study, landslides in the Beiluo River Basin are interpreted using Google Earth and ZY-3 high-resolution satellite imagery. Combined with a historical landslide inventory and field investigations, a landslide database for the Beiluo River Basin is compiled, containing a total of 1781 landslides. Based on this, the geometric and spatial characteristics of the landslides are analyzed, and the relationships between the different types of landslides and landslide scale, stream order, and geomorphological types are further explored. The results show that 50.05% of the landslides have a slope aspect between 225° and 360°, 68.78% have a slope gradient of 16–25°, and 38.97% are primarily linear in profile morphology. Areas with a high landslide density within a 10 km radius are mainly concentrated in the loess ridge and hillock landform region between Wuqi and Zhidan Counties and in the loess tableland region between Fu and Luochuan Counties, with a significant clustering effect observed in the Fu County area. Loess–bedrock interface landslides are relatively numerous in the northern loess ridge and hillock landform region due to riverbed incision and the smaller thickness of loess in this area. Intra-loess landslides are primarily found in the southern loess tableland region due to headward erosion and the greater thickness of loess in this area. Loess–clay interface landslides, influenced by riverbed incision and the limited exposure of red clay, are mainly distributed in the northern part of the southern loess tableland region and on both sides of the Beiluo River Valley in Ganquan County. These results will aid in further understanding the development and spatial distribution of landslides in the Beiluo River Basin and provide crucial support for subsequent landslide susceptibility mapping and geological hazard assessment in the region. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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23 pages, 50566 KiB  

Open AccessArticle

Integrated Remote Sensing Investigation of Suspected Landslides: A Case Study of the Genie Slope on the Tibetan Plateau, China

by Wenlong Yu, Weile Li, Zhanglei Wu, Huiyan Lu, Zhengxuan Xu, Dong Wang, Xiujun Dong and Pengfei Li

Remote Sens. 2024, 16(13), 2412; https://doi.org/10.3390/rs16132412 - 1 Jul 2024

Cited by 1 | Viewed by 1405

Abstract

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the [...] Read more.

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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20 pages, 7551 KiB  

Open AccessArticle

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 1162

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  

Open AccessArticle

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

Cited by 3 | Viewed by 1933

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  

Open AccessArticle

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

Cited by 2 | Viewed by 1348

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  

Open AccessArticle

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 9 | Viewed by 2756

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  

Open AccessArticle

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 3 | Viewed by 1441

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  

Open AccessArticle

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

Cited by 5 | Viewed by 2587

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  

Open AccessArticle

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 4 | Viewed by 3024

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  

Open AccessArticle

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 9 | Viewed by 2025

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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