Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.9
Journals
Forests
Special Issues
Landslides in Forests around the World: Causes and Mitigation
share announcement

Landslides in Forests around the World: Causes and Mitigation

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Natural Hazards and Risk Management".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 32298

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Haijia Wen

E-Mail Website
Guest Editor
National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, Chongqing, China
Interests: natural disasters; engineering risk; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Weile Li

E-Mail Website
Guest Editor
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Interests: landslide detection; landslide monitoring; landslide prediction; landslide risk assessment; remote sensing; InSAR
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chong Xu

E-Mail Website
Guest Editor
Geological Hazards Research Center, National Institute of Natural Hazards, Ministry of Emergency Management, Beijing, China
Interests: earthquake-triggered landslides; rainfall-triggered landslides; active faults; hazard and risk mapping; landslide inventory
Special Issues, Collections and Topics in MDPI journals
Dr. Hiromu Daimaru

E-Mail Website
Guest Editor
Tama Forest Science Garden, Forestry and Forest Products Research Institute, Tokyo, Japan
Interests: landslide risk evaluation and mapping; geomorphology; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Landslides are one of the most pervasive natural hazards and usually result in enormous human casualties and property losses. With population growth, the expansion of infrastructure, and increased agricultural activity in forest areas, the significance of landslides is set to increase in the future. The climate-change-related impacts, including an increased frequency of extreme rainfall events and heightened risk of forest dieback and wildfires, are likely to result in compound effects on the landslide incidence. Forests and landslides have strong inter-influences. However, there is a lack of precise understanding of the role of forests in relation to landslides. Landslide disaster prevention and mitigation are urgent needs and challenging tasks in the practice of landslides in forests. In the last decade, great progresses have been made, particularly on the new methods based on optical remote sensing, InSAR, LiDAR, and so on in association with artificial intelligence methods for landslide detection, monitoring, early warning, and risk assessment. The rapid advancement in this active field has shed light on effective and on-time responses to potential landslide preventions and mitigations in the forest regions. 

Aim and Scope: This Special Issue aims to collect the latest developments and applications of both basic and applied research on forest landslides, with particular attention to the causes of and solutions to landslides. Research can focus, but is not limited to, climate change, extreme rainfall, earthquakes, and human-activity-induced landslides in forests. Research also can pay attention to the occurrence mechanism, susceptibility, hazard, and risk of landslides. The application research of artificial intelligence methods such as machine learning/deep learning, and remote sensing technologies such as optical remote sensing, LiDAR, and InSAR are particularly welcome. 

History: The main contents of landslide research are the occurrence mechanism, on-site detection and monitoring, susceptibility and hazard mapping, early warning, and risk assessment. Researchers have developed a variety of model tests, statistical and numerical simulation methods, and monitoring technologies. 

Cutting-edge Research: In recent decades, earth observations in association with deep learning in artificial intelligence (AI) have drawn more and more attention, and great progress has been made, particularly on the new methods based on optical remote sensing, InSAR, LiDAR, and so on in association with convolutional neural networks (CNNs) for landslide detection, hazard and risk assessment, monitoring, and early warning. 

What kind of papers we are soliciting: Innovative methods and original applications (including but not limited to landslide mechanisms, inventorying, prediction, recognition, early warning systems, susceptibility mapping, risk management, spatial modeling, and mitigations) would be appropriate.

Dr. Haijia Wen
Dr. Weile Li
Prof. Dr. Chong Xu
Dr. Hiromu Daimaru
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • landslides in forests
  • landslide inventory
  • remote sensing
  • spatial-temporal prediction
  • machine learning
  • deep learning
  • physics-based and data-driven hybrid modeling
  • susceptibility and hazard mapping
  • risk assessment and management
  • monitoring and early warning

Related Special Issue

Published Papers (15 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Other

4 pages, 636 KiB  
Editorial
Landslides in Forests around the World: Causes and Mitigation
by Haijia Wen, Weile Li, Chong Xu and Hiromu Daimaru
Forests 2023, 14(3), 629; https://doi.org/10.3390/f14030629 - 20 Mar 2023
Cited by 2 | Viewed by 1225
Abstract
Landslides are a common natural disaster in forested mountainous regions [...] Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)

Research

Jump to: Editorial, Other

18 pages, 7367 KiB  
Article
Changes in Slope Stability over the Growth and Decay of Japanese Cedar Tree Roots
by Yasuhiko Okada, Fei Cai and Ushio Kurokawa
Forests 2023, 14(2), 256; https://doi.org/10.3390/f14020256 - 29 Jan 2023
Cited by 2 | Viewed by 2241
Abstract
In Japan, repeated driftwood landslide disasters have become a major issue; thus, studies are required to better understand forest function to implement appropriate forest management and prevent such disasters. We investigated the effect of Japanese cedar tree roots on shallow landslide initiation. To [...] Read more.
In Japan, repeated driftwood landslide disasters have become a major issue; thus, studies are required to better understand forest function to implement appropriate forest management and prevent such disasters. We investigated the effect of Japanese cedar tree roots on shallow landslide initiation. To incorporate the effect of roots on the two side-flanks of the shallow landslide, we propose a new slope-stability analysis method in which the sliding block is simplified as a three-prism model. The root reinforcement was approximated by the sum of the root pullout forces over a unit area, incorporating changes in the root strength with the growth of the trees after planting and the decay of the stumps after cutting. The reinforced root strength after the stump-cutting decreased linearly with time, with no strength remaining at 9 years. In contrast, the reinforced root strength of the new plants increased according to a logistic curve with time; thus, the root strength increased only slightly up to 9 years after planting, and the minimum total reinforced root strength was observed at this time. The safety factor of the slopes in a forest basin in Ibaraki Prefecture was calculated using the proposed three-prism method at intervals of 5 years on a 1-metre-resolution digital elevation model. The number of unstable grids peaked at 10 years, and a higher risk of slope instability was observed at 5–15 years. Therefore, implementing forest operations for lowering slope instability during this period should be important to prevent landslide disasters. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

7 pages, 2251 KiB  
Communication
Nucleation Process of the 2017 Nuugaatsiaq, Greenland Landslide
by Zhenwei Guo, Xinrong Hou, Dawei Gao and Jianxin Liu
Forests 2023, 14(1), 2; https://doi.org/10.3390/f14010002 - 20 Dec 2022
Cited by 1 | Viewed by 1179
Abstract
Seismic precursors prior to the failure of rocks are essential for probing the nucleation process and mitigating hazards. However, such precursory events before large landslides are rarely reported possibly due to the lack of near-source observations. The 2017 Nuugaatsiaq, Greenland landslide that was [...] Read more.
Seismic precursors prior to the failure of rocks are essential for probing the nucleation process and mitigating hazards. However, such precursory events before large landslides are rarely reported possibly due to the lack of near-source observations. The 2017 Nuugaatsiaq, Greenland landslide that was preceded by an abundance of small earthquakes and captured by a local seismic station is a notable exception and offers us a valuable opportunity to investigate how a large landslide initiated. Prior work suggests that accelerated creeping plays an important role during the landslide nucleation process. However, by analyzing the temporal evolution of the waveform similarities, waveform amplitudes, and inter-event times of the seismic precursors, we find that the Nuugaatsiaq landslide was very likely triggered by a series of accelerated and migratory small earthquakes approaching the nucleation area of the upcoming landslide, thus providing important insights into the failure initiation of massive landslides. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

19 pages, 12034 KiB  
Article
Research on a Regional Landslide Early-Warning Model Based on Machine Learning—A Case Study of Fujian Province, China
by Yanhui Liu, Junbao Huang, Ruihua Xiao, Shiwei Ma and Pinggen Zhou
Forests 2022, 13(12), 2182; https://doi.org/10.3390/f13122182 - 19 Dec 2022
Cited by 2 | Viewed by 2059
Abstract
China’s landslide disasters are serious, and regional landslide disaster early-warning is one of the important means of disaster prevention and mitigation. The traditional regional landslide disaster early-warning model, however, is limited by the complex landslide induction mechanism, limited data accumulation, and insufficient big [...] Read more.
China’s landslide disasters are serious, and regional landslide disaster early-warning is one of the important means of disaster prevention and mitigation. The traditional regional landslide disaster early-warning model, however, is limited by the complex landslide induction mechanism, limited data accumulation, and insufficient big data analysis methods, and has problems such as limited early-warning accuracy and insufficient refinement. In this paper, a machine learning method was introduced into the field of regional landslide disaster warning. From the model construction process of training sample-set construction, sample learning and training, model parameter optimization, model preservation, warning output, and so on, a method for constructing a regional landslide early-warning model based on machine learning was systematically proposed. In the sample learning and training, 80% of the training sample-set was used as the training set, and 20% was used as the test set for five-fold cross validation. The Bayesian Optimization algorithm was used to optimize the model parameters, and the accuracy, ROC curve, and AUC value were used to verify the model accuracy and model generalization ability. With China’s Fujian province as an example, based on nine years of geological and meteorological data (2010–2018), geological environment factors, factors of hazard-affected bodies and historical disaster situations, and rainfall-induced factors in four categories, a total of 26 indicators were used as input characteristic parameters. Six machine learning algorithms were adopted to improve model training; the results showed that the Random Forest algorithm performed the best, giving an accuracy of 92.3%, and was the model with the best generalization ability (AUC was 0.955). The second best was the Artificial Neural Network model, with an accuracy of 0.937 and an AUC of 0.935. Next were the Nearest Neighbor model, the Logistic Regression model, and the Support Vector Machine; the poorest results were from the Decision Tree model. Finally, the typical rainfall-type landslide disaster process in Fujian Province was selected as an example to verify the Random Forest algorithm model. The results showed that compared with the early-warning results of the original explicit statistical model, the hit rate of the new model was 6 times, or equal to that of the original model, and the landslide density in the early-warning area of the new model was 1.6–1.7 times that of the original model. Preliminary verification showed that the new model based on the Random Forest method has obvious advantages, a higher hit rate and a smaller warning area, and can achieve more accurate warnings. The follow-up will continue to track the new landslide disaster situation in the study area and carry out model verification and correction. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

32 pages, 10370 KiB  
Article
A Comparative Study of Shallow Machine Learning Models and Deep Learning Models for Landslide Susceptibility Assessment Based on Imbalanced Data
by Shiluo Xu, Yingxu Song and Xiulan Hao
Forests 2022, 13(11), 1908; https://doi.org/10.3390/f13111908 - 14 Nov 2022
Cited by 5 | Viewed by 1892
Abstract
A landslide is a type of geological disaster that poses a threat to human lives and property. Landslide susceptibility assessment (LSA) is a crucial tool for landslide prevention. This paper’s primary objective is to compare the performances of conventional shallow machine learning methods [...] Read more.
A landslide is a type of geological disaster that poses a threat to human lives and property. Landslide susceptibility assessment (LSA) is a crucial tool for landslide prevention. This paper’s primary objective is to compare the performances of conventional shallow machine learning methods and deep learning methods in LSA based on imbalanced data to evaluate the applicability of the two types of LSA models when class-weighted strategies are applied. In this article, logistic regression (LR), random forest (RF), deep fully connected neural network (DFCNN), and long short-term memory (LSTM) neural networks were employed for modeling in the Zigui-Badong area of the Three Gorges Reservoir area, China. Eighteen landslide influence factors were introduced to compare the performance of four models under a class balanced strategy versus a class imbalanced strategy. The Spearman rank correlation coefficient (SRCC) was applied for factor correlation analysis. The results reveal that the elevation and distance to rivers play a dominant role in LSA tasks. It was observed that DFCNN (AUC = 0.87, F1-score = 0.60) and LSTM (AUC = 0.89, F1-score = 0.61) significantly outperformed LR (AUC = 0.89, F1-score = 0.50) and RF (AUC = 0.88, F1-score = 0.50) under the class imbalanced strategy. The RF model achieved comparable outcomes (AUC = 0.90, F1-score = 0.61) to deep learning models under the class balanced strategy and ran at a faster training speed (up to 63 times faster than deep learning models). The LR model performance was inferior to that of the other three models under the balanced strategy. Meanwhile, the deep learning models and the shallow machine learning models showed significant differences in susceptibility spatial patterns. This paper’s findings will aid researchers in selecting appropriate LSA models. It is also valuable for land management policy making and disaster prevention and mitigation. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

0 pages, 3806 KiB  
Article
Landslide Susceptibility-Oriented Suitability Evaluation of Construction Land in Mountainous Areas
by Linzhi Li, Xingyu Chen, Jialan Zhang, Deliang Sun and Rui Liu
Forests 2022, 13(10), 1621; https://doi.org/10.3390/f13101621 - 03 Oct 2022
Cited by 2 | Viewed by 1518
Abstract
The aim of the present study was to assess the suitability of mountainous areas for construction land on the basis of landslide susceptibility, to obtain the spatial distribution pattern of said suitability and to improve the existing theories and methods used to ascertain [...] Read more.
The aim of the present study was to assess the suitability of mountainous areas for construction land on the basis of landslide susceptibility, to obtain the spatial distribution pattern of said suitability and to improve the existing theories and methods used to ascertain said suitability. Taking Hechuan District in Chongqing as the research area and using data relating to 754 historical landslide sites from 2000 to 2016, we selected 22 factors that influence landslides. The factors were classified into five types, namely topography and geomorphology, geological structure, meteorology and hydrology, environmental conditions and human activities. A landslide susceptibility model was constructed using the random forest algorithm, and safety factors of construction land suitability were established according to the results of landslide susceptibility, with the suitability of land for construction in mountainous areas assessed by combining the key factors (natural, social and ecological factors). The weights of the factors were determined through the use of expert approaches to classify the suitability of land for construction in the research area into five levels: prohibited, unsuitable, basically suitable, more suitable and most suitable. The results of the study show that: (1) The average accuracy of the tenfold cross-validation training set data of landslides reached 0.978; the accuracy of the test set reached 0.913; the accuracy of the confusion matrix reached 97.2%; and the area under curve (AUC) values of the training set, test set and all samples were 0.999, 0.756 and 0.989, respectively. Historical landslide events were found to be mostly concentrated in highly susceptible areas, and the landslide risk level in Hechuan District was mostly low or very low (accounting for 76.26% of the study area), although there was also a small proportion with either a high or very high risk level (9.25%). The high landslide susceptibility areas are primarily concentrated in the southern and southeastern ridge, in the valley and near water systems, with landslides occurring less frequently in the gentle hilly basin. (2) The suitability of land for construction in mountainous areas was strongly influenced by landslide susceptibility, distance from roads and distance from built-up areas; among such parameters, rainfall, elevation and lithology significantly influenced landslides in the region. (3) The land suitable for construction in the study area was highly distributed, mainly in urban areas where the three rivers meet and around small towns, with a spatial distribution pattern of high in the middle and low on both sides. Furthermore, the suitability of land for construction in Hechuan District was found to be primarily at the most suitable and more suitable levels (accounting for 84.66% of the study area), although a small proportion qualified for either the prohibited or unsuitable level (accounting for 15.72%). The present study can be extended and applied to similar mountainous areas. The landslide susceptibility map and construction land suitability map can support the spatial planning of mountainous towns, and the assessment results can assist with the development direction of mountainous towns, the layout of construction land and the siting of major infrastructure. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

18 pages, 6021 KiB  
Article
A Case Study on the Energy Capacity of a Flexible Rockfall Barrier in Resisting Landslide Debris
by Lei Zhao, Lijun Zhang, Zhixiang Yu, Xin Qi, Hu Xu and Yifan Zhang
Forests 2022, 13(9), 1384; https://doi.org/10.3390/f13091384 - 30 Aug 2022
Cited by 1 | Viewed by 1423
Abstract
Landslides frequently occur in forest areas with a steep hillside, especially when severely disturbed by human activities. After sustained heavy rainfall, a landslide occurred near the Tianwan tunnel entrance of the Chongqing-Huaihua railway in China. Fortunately, the landslide debris was successfully intercepted by [...] Read more.
Landslides frequently occur in forest areas with a steep hillside, especially when severely disturbed by human activities. After sustained heavy rainfall, a landslide occurred near the Tianwan tunnel entrance of the Chongqing-Huaihua railway in China. Fortunately, the landslide debris was successfully intercepted by a flexible barrier originally installed to stop rockfalls, which is, to date, the first publicly reported case of landslide debris having been successfully intercepted by a flexible barrier without any damage, in mainland of China. A field investigation was first conducted, and then a back analysis of the landslide mobility and the interaction between the landslide and the flexible barrier was carried out. The back analysis showed that the impact energy was three-times larger than the rated energy capacity of the flexible barrier. It also showed that the elongation of the brake rings and the deflection of the flexible barrier from the numerical simulation was comparable to that from the field measurements. The fact that these brake rings were not elongated to their limit indicated that the capacity of the flexible barrier still had a surplus. Finally, to investigate the maximum energy capacity of a flexible rockfall barrier in resisting landslide debris, parametric analyses of a flexible barrier impacted by landslide debris with different impact energies and velocities were carried out using a coupled ALE-FEM modeling technique. The results showed that the flexible barrier dissipated less than 40% of the total energy of the landslide debris. With an increase of impact energy, the energy dissipation ratio of the flexible barrier decreased linearly. The maximum energy capacity of a flexible rockfall barrier in resisting landslide debris is four-times that of resisting a rockfall. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

15 pages, 13047 KiB  
Article
Automatic Remote Sensing Identification of Co-Seismic Landslides Using Deep Learning Methods
by Dongdong Pang, Gang Liu, Jing He, Weile Li and Rao Fu
Forests 2022, 13(8), 1213; https://doi.org/10.3390/f13081213 - 01 Aug 2022
Cited by 7 | Viewed by 1652
Abstract
Rapid and accurate extraction of landslide areas triggered by earthquakes has far-reaching significance for geological disaster risk assessment and emergency rescue. At present, visual interpretation and field survey are still the most-commonly used methods for landslide identification, but these methods are often time-consuming [...] Read more.
Rapid and accurate extraction of landslide areas triggered by earthquakes has far-reaching significance for geological disaster risk assessment and emergency rescue. At present, visual interpretation and field survey are still the most-commonly used methods for landslide identification, but these methods are often time-consuming and costly. For this reason, this paper tackles the problem of co-seismic landslide identification and the fact that there is little sample information in existing studies on landslide. A landslide sample dataset with 4000 tags was produced. With the YOLOv3 algorithm as the core, a convolutional neural network model with landslide characteristics was established to automatically recognize co-seismic landslides in satellite remote sensing images. By comparing it with the graphical interpretation results of remote sensing images, we found that the remote sensing for landslide recognition model constructed in this paper demonstrated high recognition accuracy and fast speed. The F1 value was 0.93, indicating that the constructed model was stable. The research results can provide reference for emergency rescue and disaster investigation of the same co-seismic landslide disaster. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

27 pages, 9698 KiB  
Article
Analysis of the Influence of Forests on Landslides in the Bijie Area of Guizhou
by Yu Zhang, Chaoyong Shen, Shaoqi Zhou and Xuling Luo
Forests 2022, 13(7), 1136; https://doi.org/10.3390/f13071136 - 19 Jul 2022
Cited by 13 | Viewed by 1935
Abstract
Forests are an important part of the ecological environment, and changes in forests not only affect the ecological environment of the region but are also an important factor causing landslide disasters. In order to correctly evaluate the impact of forest cover on landslide [...] Read more.
Forests are an important part of the ecological environment, and changes in forests not only affect the ecological environment of the region but are also an important factor causing landslide disasters. In order to correctly evaluate the impact of forest cover on landslide susceptibility, in this paper, we build an evaluation model for the contribution of forests to the landslide susceptibility of different grades based on survey data for forest land change in Bijie City and landslide susceptibility data, and discuss the effects of forest land type, origin, age group, and dominant tree species on landslide susceptibility. We find that forests play a certain role in regulating landslide susceptibility: compared with woodland, the landslide protection ability of shrubland is stronger. Furthermore, natural forests have a greater inhibitory effect on landslides than artificial forests, and compared with young forest, mature forest and over-mature forest, middle-aged forest and near-mature forest have stronger landslide protection abilities. In addition, the dominant tree species in different regions have different impacts on landslides. Coniferous forests such as Chinese fir and Cryptomeria fortunei in Qixingguan and Dafang County have a low ability to prevent landslides. Moreover, the soft broad tree species found in Qianxi County, Zhijin County, Nayong County and Jinsha County are likely to cause landslides and deserve further research attention. Additionally, a greater focus should be placed on the landslide protection of walnut economic forests in Hezhang County and Weining County. Simultaneously, greater attention should be paid to the Cyclobalanopsis glauca tree species in Weining County because the area where this tree species is located is prone to landslides. Aiming at addressing the landslide susceptibility existing in different forests, we propose forest management strategies for the ecological prevention and control of landslides in Bijie City, which can be used as a reference for landslide susceptibility prevention and control. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

10 pages, 2038 KiB  
Article
Effect of Particle Form and Surface Friction on Macroscopic Shear Flow Friction in Particle Flow System
by Yu Huang, Yi’an Wang and Suran Wang
Forests 2022, 13(7), 1107; https://doi.org/10.3390/f13071107 - 14 Jul 2022
Cited by 3 | Viewed by 1355
Abstract
The damage caused by landslide disasters is very significant. Among them, landslides after forest fires have been widely concerned by scholars in recent years due to their particular physical and chemical properties. This large-scale shear flow of particulate matter has similarities to fluid [...] Read more.
The damage caused by landslide disasters is very significant. Among them, landslides after forest fires have been widely concerned by scholars in recent years due to their particular physical and chemical properties. This large-scale shear flow of particulate matter has similarities to fluid systems. However, due to the discontinuity of the particle system, its flow process has significant random characteristics. To investigate the random properties of particle systems, this study conducted a series of ring shear tests on four particle systems. The effects of the particle shape, normal stress, and shear velocity on the systems’ shear rheological features were investigated using experimental data. The particle form has an important effect on the macroscopic properties of the system. In a spherical particle system, the macroscopic friction fluctuation is determined by the friction of the particle surface and the system’s normal stress. The shear velocity has a minor effect on this characteristic. Three elements simultaneously influence the macroscopic friction fluctuation of a breccia particle system: the particle surface friction, system normal stress, and shear velocity. The origins of macroscopic frictional fluctuations in particle systems with various shapes are fundamentally distinct. This study contributes to a better understanding of the causes of particle system fluctuations, and establishes the theoretical foundation for the future development of disaster prevention technology. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

14 pages, 2857 KiB  
Article
Study on the Shear Strength of Root-Soil Composite and Root Reinforcement Mechanism
by Pengcheng Li, Xuepei Xiao, Lizhou Wu, Xu Li, Hong Zhang and Jianting Zhou
Forests 2022, 13(6), 898; https://doi.org/10.3390/f13060898 - 09 Jun 2022
Cited by 8 | Viewed by 2553
Abstract
This study investigates the effects of root distributions and stress paths on the shear strength of root-soil composites using a consolidated-undrained (CU) triaxial test. On the basis of the limit equilibrium, two root reinforcement coefficients (n and m) are proposed for [...] Read more.
This study investigates the effects of root distributions and stress paths on the shear strength of root-soil composites using a consolidated-undrained (CU) triaxial test. On the basis of the limit equilibrium, two root reinforcement coefficients (n and m) are proposed for characterizing the effects of shear strength parameters on the principal stress considering different root distribution angles and root diameters. Then, n and m are introduced into the conventional limit equilibrium equation to develop a new limit equilibrium equation for root-soil composites. The results demonstrate that the root distribution angles (α) and root diameters (d) affect the shear strength of the root-soil composites. Under a consolidated-undrained condition, the effective cohesion (crs′) of the rooted soil is high and decreases in the order of 90°, 0°, 30° and 60°. For the same root distribution angle, crs′ increases with the increasing root diameter. Meanwhile, the effective internal friction angle (φrs′) changes slightly. The failure principal stress of the root-soil composites is positively correlated with n and m. Furthermore, the deformation of the samples indicates that the run-through rate of α = 90° and α = 0° are both 0. Meanwhile, the lateral deformation rate declines from 17.0% for α = 60° to 10.9% for α = 90°. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

18 pages, 18604 KiB  
Article
Mapping the Long-Term Evolution of the Post-Event Deformation of the Guang’an Village Landslide, Chongqing, China Using Multibaseline InSAR Techniques
by Kui Zhang, Faming Gong, Li Li, Alex Hay-Man Ng and Pengfei Liu
Forests 2022, 13(6), 887; https://doi.org/10.3390/f13060887 - 07 Jun 2022
Cited by 3 | Viewed by 1724
Abstract
On 21 October 2017, days of heavy rainfall triggered a landslide in Guang’an Village, Wuxi County, Chongqing, China. According to the field investigation after the incident, there is still a massive accumulation body, which could possibly reactivate the landslide. In this study, to [...] Read more.
On 21 October 2017, days of heavy rainfall triggered a landslide in Guang’an Village, Wuxi County, Chongqing, China. According to the field investigation after the incident, there is still a massive accumulation body, which could possibly reactivate the landslide. In this study, to explore the long-term evolution of the deformation after the initial Guang’an Village Landslide, a time-series InSAR technique (TS-InSAR) was applied to the 128 ascending Sentinel-1A datasets spanning from October 2017 to March 2022. A new approach is proposed to enhance the conventional TS-InSAR method by integrating LiDAR data into the TS-InSAR process chain. The spatial–temporal evolution of post-event deformation over the Guang’an Village Landslide is analyzed based on the time-series results. It is found that the post-event deformation can be divided into three main stages: the post-failure stage, the post-failure and reactivation stage, and the reactivation stage. It is also suggested that, although the study area is currently under the reactivation stage, there are two active deformation zones that may become the origin of a secondary landslide triggered by heavy rainfall in the future. Moreover, the nearby Yaodunzi landslide might also play an important role in the generation and reactivation of a secondary Guang’an Village Landslide. Therefore, continuous monitoring for post-event deformation of the Guang’an Village Landslide is important for early warning of a secondary landslide in the near future. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

20 pages, 8443 KiB  
Article
A Hybrid Landslide Warning Model Coupling Susceptibility Zoning and Precipitation
by Deliang Sun, Qingyu Gu, Haijia Wen, Shuxian Shi, Changlin Mi and Fengtai Zhang
Forests 2022, 13(6), 827; https://doi.org/10.3390/f13060827 - 25 May 2022
Cited by 25 | Viewed by 2412
Abstract
Landslides are one of the most severe and common geological hazards in the world. The purpose of this research is to establish a coupled landslide warning model based on random forest susceptibility zoning and precipitation. The 1520 landslide events in Fengjie County, Chongqing, [...] Read more.
Landslides are one of the most severe and common geological hazards in the world. The purpose of this research is to establish a coupled landslide warning model based on random forest susceptibility zoning and precipitation. The 1520 landslide events in Fengjie County, Chongqing, China, before 2016 are taken as research cases. We adapt the random forest model to build a landslide susceptibility model. The antecedent effective precipitation model, based on the fractal relationship, is used to calculate the antecedent effective precipitation in the 10 days before the landslide event. Based on different susceptibility zones, the effective precipitation corresponding to different cumulative frequencies is counted as the threshold, and the threshold is adjusted according to the fitted curve. Finally, according to the daily precipitation, the rain warning levels in susceptibility zones are further adjusted, and the final prewarning model of the susceptibility zoning and precipitation coupling is obtained. The results show that the random forest model has good prediction ability for landslide susceptibility zoning, and the precipitation warning model that couples landslide susceptibility, antecedent effective precipitation, and the daily precipitation threshold has high early warning ability. At the same time, it was found that the precipitation warning model coupled with antecedent effective precipitation and the daily precipitation threshold has more accurate precipitation warning ability than the precipitation warning model coupled with the antecedent effective precipitation only; the coupling of the two can complement each other to better characterize the occurrence of landslides triggered by rainfall. The proposed coupled landslide early warning model based on random forest susceptibility and rainfall inducing factors can provide scientific guidance for landslide early warning and prediction, and improve the manageability of landslide risk. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

16 pages, 30785 KiB  
Article
A Data-Driven Model for Spatial Shallow Landslide Probability of Occurrence Due to a Typhoon in Ningguo City, Anhui Province, China
by Yulong Cui, Jiale Jin, Qiangbing Huang, Kang Yuan and Chong Xu
Forests 2022, 13(5), 732; https://doi.org/10.3390/f13050732 - 08 May 2022
Cited by 7 | Viewed by 2017
Abstract
From 9 to 11 August 2019, the southeast coastal areas of China were hit by Typhoon Lekima, which caused a large number of shallow landslides. The typhoon resulted in a maximum rainfall of 402 mm during 3 days in Ningguo City. In this [...] Read more.
From 9 to 11 August 2019, the southeast coastal areas of China were hit by Typhoon Lekima, which caused a large number of shallow landslides. The typhoon resulted in a maximum rainfall of 402 mm during 3 days in Ningguo City. In this study, satellite images were acquired before and after the rainfall and visual interpretation was used to identify 414 shallow landslides in Ningguo City, and a complete database of shallow landslides caused by the typhoon-induced rainfall in Ningguo City was created. Nine landslide-influencing factors were selected—elevation, slope, aspect, strata, distance to faults, distance to rivers, distance to roads, normalized vegetation difference index, and rainfall—and the relationships between the rainfall-induced landslide distribution and the influencing factors were analyzed. The Bayesian probability method was combined with a logistic regression model to establish a landslide probability map for the study area. The real probabilities of landslide occurrence in the study area under five different rainfall conditions were calculated, and probability maps of landslide occurrence were drawn. The results of this study provide a reference for disaster prevention and reduction of typhoon rainstorm landslides in the southeast coastal areas of China and a future basis for decision making by the Ningguo government departments before a typhoon rainstorm occurs. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

Other

Jump to: Editorial, Research

20 pages, 3170 KiB  
Technical Note
Landslide Susceptibility Research Combining Qualitative Analysis and Quantitative Evaluation: A Case Study of Yunyang County in Chongqing, China
by Wengang Zhang, Songlin Liu, Luqi Wang, Pijush Samui, Marcin Chwała and Yuwei He
Forests 2022, 13(7), 1055; https://doi.org/10.3390/f13071055 - 04 Jul 2022
Cited by 15 | Viewed by 2232
Abstract
Machine learning-based methods are commonly used for landslide susceptibility mapping. Most of the recent publications focused on quantitative analysis, i.e., improving data processing methods, comparing and perfecting the data-driven model itself, but rarely taking the qualitative aspects of the local landslide occurrences into [...] Read more.
Machine learning-based methods are commonly used for landslide susceptibility mapping. Most of the recent publications focused on quantitative analysis, i.e., improving data processing methods, comparing and perfecting the data-driven model itself, but rarely taking the qualitative aspects of the local landslide occurrences into consideration and the further analysis of the key features was always lacking. This study aims to combine qualitative and quantitative analysis and examine its effect on mapping accuracy; based on the feature importance ranks and the related literature, the key features for identifying landslide/non-landslide points of different sub-zones were further analyzed. Before modeling, the study area Yunyang County, Chongqing City, China, was manually divided into four sub-zones based on the information from geological hazards exploration in Chongqing, including the mechanism of landslide formation and sliding failure and geomorphic unit characteristics. Upon the qualitative analysis basis, five grid searches tuned random forest models (one for the whole region and four for the sub-zones independently) were established by 1654 data points and 20 conditioning features. Compared with the conventional data-driven method, the integrated quantitative evaluation based on the qualitative analysis results showed higher reliability, which not only improved the mapping accuracy but also increased the AUC values of all four sub-models, which were 8.8%, 2.3%, 1.9% and 9.1% higher than that of the parent model. Moreover, the quantitative evaluation based on the qualitative analysis revealed the key factors affecting local landslide formation. Therefore, qualitative analysis is recommended in future landslide susceptibility modeling with the additional combination of data-driven methods. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-15
Back to TopTop