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Landslides in Forests around the World: Causes and Mitigation—2nd Edition
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Landslides in Forests around the World: Causes and Mitigation—2nd Edition

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 9049

Special Issue Editors

Prof. Dr. Haijia Wen

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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
Prof. Dr. Chong Xu

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Guest Editor
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: landslides; geological hazards; remote sensing; geology
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. 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,

Background: 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. Impacts related to climate change, 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 incidence of landslides. Forests and landslides have strong inter-influences. However, there is a lack of a precise understanding of the role of forests in relation to landslides. Landslide disaster prevention and mitigation are urgently needed but represent challenging tasks in terms of landslide practices in forests. In the last decade, great progress has been made, particularly in 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 timely responses to potential landslide prevention and mitigation in 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 being paid to causes and solutions. Potential topics include, but are not limited to, climate change, extreme rainfall, earthquakes, and human-activity-induced landslides in forests. Submissions may also be focused on the mechanism of occurrence, susceptibility, hazard, and risk of landslides. The application 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: Previously, landslide research has focused on the mechanism of occurrence, 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 to address such topics. 

Cutting-edge research: In recent decades, Earth observations, in association with deep learning in artificial intelligence (AI), have drawn increasing attention. Great progress has been made in 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) are welcome. 

Prof. Dr. Haijia Wen
Prof. Dr. Chong Xu
Dr. Weile Li
Prof. 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 (6 papers)

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Research

16 pages, 6542 KiB  
Article
Analysis of Water Migration and Spoil Slope Stability under the Coupled Effects of Rainfall and Root Reinforcement Based on the Unsaturated Soil Theory
by Huanran Song, Jiankun Huang, Zhiwei Zhang, Qunou Jiang, Lanhua Liu, Caisong He and Yang Zhou
Forests 2024, 15(4), 640; https://doi.org/10.3390/f15040640 - 31 Mar 2024
Viewed by 666
Abstract
Root reinforcement is an effective slope protection measure due to root water absorption and soil suction. However, the coupled effect of rainfall and root reinforcement remains unclear, resulting in a challenge to evaluate slope stability in complex environments. This paper regards the root–soil [...] Read more.
Root reinforcement is an effective slope protection measure due to root water absorption and soil suction. However, the coupled effect of rainfall and root reinforcement remains unclear, resulting in a challenge to evaluate slope stability in complex environments. This paper regards the root–soil composite as a natural fiber composite and quantifies its reinforcement effect using direct shear tests. The unsaturated soil seepage–stress theory was employed to simulate the effect of rainfall on water migration and the stability of spoil, overburden, and vegetated slopes. Field measurements and pore water pressure tests verified the simulation results. Furthermore, the influences of the slope angle, rainfall parameters, and vegetation cover thickness on slope stability were analyzed. The results showed the following: (1) The root reinforcement enhanced the soil’s ability to resist shear deformation, substantially improving soil shear strength. The cohesion of the root–soil composite (crs = 33.25 kPa) was 177% higher than that of the engineering spoil (ces = 12 kPa) and 32.21% higher than that of the overburden soil (cos = 25.15 kPa). (2) The overburden and vegetated slopes had lower permeability coefficients and a higher shear strength than the spoil slope, and the effect was more pronounced for the latter, resulting in lower landslide risks. The water migration trend of the vegetated slope was characterized by substantial runoff and a low sediment yield. The safety factors of the spoil slope, overburden slope, and vegetated slope were 1.741, 1.763, and 1.784 before rainfall and 1.687, 1.720, and 1.763 after rainfall, respectively, indicating a significantly higher safety factor of the vegetated slope after rainfall. (3) The slope angle significantly affected slope stability, with lower safety factors observed for higher rainfall intensities and durations. Under these conditions, the slope angle should be less than 30°, and the soil thickness should be 0.5 m for herbaceous vegetation and shrubs and 1.0 m for trees. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation—2nd Edition)
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16 pages, 6829 KiB  
Article
Stability of Ficus virens-Reinforced Slopes Considering Mechanical and/or Hydrological Effects
by Changbing Qin, Rui Wang, Wenkang Chen, Yusha Shi, Haixiu Sun, Jianjun Tang and Luqi Wang
Forests 2024, 15(1), 133; https://doi.org/10.3390/f15010133 - 08 Jan 2024
Cited by 1 | Viewed by 939
Abstract
Vegetation reinforcement for slopes has been recognized as an environment-friendly measure and has been widely adopted in engineering practice. However, the stability analysis of vegetation reinforcement for slopes has mainly been discussed for an infinite slope and common grass and scrub plant species. [...] Read more.
Vegetation reinforcement for slopes has been recognized as an environment-friendly measure and has been widely adopted in engineering practice. However, the stability analysis of vegetation reinforcement for slopes has mainly been discussed for an infinite slope and common grass and scrub plant species. This study proposes a procedure for analyzing the stability of a finite slope reinforced with Ficus virens under transpiration and rainfall conditions. A simplified empirical model for characterizing root cohesion and triaxial testing is utilized to quantify the mechanical effect of roots on rooted soil shear strength. A numerical modeling technique with COMSOL Multiphysics is used to investigate the hydrological effect of roots. The combination of these two effects forms an expression for the unsaturated shear strength of rooted soils. The stability of a vegetated soil slope is then investigated in terms of safety factors and failure mechanisms, with/without considering rainfall. The results show that the stability solutions without consideration of the roots’ mechanical and/or hydrological effects are overly conservative. The hydrological contribution to slope stability could also be partially preserved under short-term rainfall, and as rainfall continues, the hydrological effect is weakened, while the mechanical reinforcement is assumed to be unchanged. In the meantime, the hydrological contribution to slope stability is susceptible to atmospheric conditions, which indicates a favorable effect on water uptake and an adverse consequence for water infiltration. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation—2nd Edition)
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20 pages, 5314 KiB  
Article
Assessment and Mechanism Analysis of Forest Protection against Rockfall in a Large Rock Avalanche Area
by Kanglei Song, Haiqing Yang, Dan Liang, Lichuan Chen, Lili Qu and Chiwei Chen
Forests 2023, 14(10), 1982; https://doi.org/10.3390/f14101982 - 30 Sep 2023
Viewed by 976
Abstract
Trees in forests can obstruct falling rocks and serve as a natural barrier to reduce the velocity of falling rocks. Recently, there has been growing interest in utilizing forests to safeguard against potential rockfall. Nevertheless, there is a dearth of research regarding the [...] Read more.
Trees in forests can obstruct falling rocks and serve as a natural barrier to reduce the velocity of falling rocks. Recently, there has been growing interest in utilizing forests to safeguard against potential rockfall. Nevertheless, there is a dearth of research regarding the impact of rock size and forest structure on forest preservation against rockfall. This study takes the Jiweishan rock avalanche that occurred in China in June 2009 as an example to discuss the protection mechanism of forests against rockfall in rock avalanche disasters. Three sizes of rockfalls from the Jiweishan rock avalanche were simulated and analyzed with and without forests using Rockyfor3D software. The findings indicate that forests can mitigate the energy impact of falling rocks. Especially in the debris flow area of rock avalanches, the protective effect of trees on small-sized falling rocks is most obvious, reducing the runout distance and damage range of the debris flow. Moreover, the protective effect of forest structures on rockfall risk was explored. It was found that broad-leaved forests had the best protection against falling rocks, followed by coniferous broad-leaved mixed forests, and coniferous forests had the worst protective effect. Furthermore, increasing forest planting density and tree diameter at breast height (DBH) can result in better protection against rockfall. Thus, rational planning of forest species and planting density in areas of rockfall can effectively reduce the threat of rockfall risk. The research ideas in this study can provide a basis for evaluating the mitigation of rockfall hazards by forests and provide a reference for constructing and planning protective forests in rockfall and rock avalanche hazard areas. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation—2nd Edition)
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20 pages, 18457 KiB  
Article
Analysis of Soil–Water Characteristics and Stability Evolution of Rainfall-Induced Landslide: A Case of the Siwan Village Landslide
by Haijia Wen, Jiafeng Xiao, Xiongfeng Wang, Xuekun Xiang and Xinzhi Zhou
Forests 2023, 14(4), 808; https://doi.org/10.3390/f14040808 - 14 Apr 2023
Cited by 4 | Viewed by 1998
Abstract
This paper aimed to study the soil–water characteristics and stability evolution law of rainfall-induced landslide. Taking the two landslide events in Siwan village as an example, the formation conditions of the disaster and landslide characteristics were analyzed. Additionally, the deformation characteristics and destruction [...] Read more.
This paper aimed to study the soil–water characteristics and stability evolution law of rainfall-induced landslide. Taking the two landslide events in Siwan village as an example, the formation conditions of the disaster and landslide characteristics were analyzed. Additionally, the deformation characteristics and destruction mechanisms of landslides were discussed in-depth. The soil–water characteristics and hydraulic conductivity of the landslides were analyzed based on TRIM experiment results. Geo-Studio numerical software was further used for typical sections to analyze the stability of the evolution of the landslide events under rainfall conditions. The results showed that (1) The soil–water characteristic curve (SWCC) inversely varies with water content volume, and the sliding body has lower saturated water content and matrix suction than the sliding zone. The hydraulic conductivity function (HCF) increases with water content volume, and the sliding body has higher hydraulic conductivity (0.43 m/d) than the sliding zone (0.03 m/d). (2) Rainfall is the primary cause of landslides, and there is a hysteretic effect. Heavy rainfall will inevitably accelerate the formation of landslides in the analysis of the deformation characteristics and destruction mechanisms of rainfall-induced landslides. (3) Compared with the engineering analogy of the Fredlund and Xing (FX) model, the Van Genuchten–Mualem (VGM) model of the soil–water characteristics test based on the TRIM experimental system can better reflect the actual field situation. The numerical simulation method based on the TRIM experiments of the soil–water characteristics test is scientifically sound and reliable for the stability evolution of overburden rainfall-induced landslides. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation—2nd Edition)
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21 pages, 5960 KiB  
Article
Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China
by Zian Lin, Qiuguang Chen, Weiping Lu, Yuanfa Ji, Weibin Liang and Xiyan Sun
Forests 2023, 14(3), 499; https://doi.org/10.3390/f14030499 - 02 Mar 2023
Viewed by 1362
Abstract
Landslide susceptibility mapping is the basis of regional landslide risk assessment and prevention. In recent years, deep learning models have been applied in landslide susceptibility mapping, but some problems remain, such as gradient disappearance, explosion, and degradation. Additionally, the potential nonlinear temporal and [...] Read more.
Landslide susceptibility mapping is the basis of regional landslide risk assessment and prevention. In recent years, deep learning models have been applied in landslide susceptibility mapping, but some problems remain, such as gradient disappearance, explosion, and degradation. Additionally, the potential nonlinear temporal and spatial characteristics between landslides and environmental factors may not be captured, and nonlandslide points may be randomly selected in the susceptibility mapping process. To overcome these shortcomings, in this paper, an information-gate recurrent unit residual network (Information-GRUResNet) model is proposed to produce a landslide susceptibility map by combining existing landslide records and environmental factor data. The model uses the information theory method to produce the initial landslide susceptibility map. Then, representative grid units and landslide points are selected as input variables of the GRUResNet model, from which nonlinear temporal and spatial characteristics are extracted to produce a landslide susceptibility map. Changzhou town in Wuzhou, China, is selected as a case study, and it is verified that the Information-GRUResNet model can accurately produce a landslide susceptibility map for the selected area. Finally, the Information-GRUResNet model is compared with GRU, RF, and LR models. The experimental results show that the Information-GRUResNet model is more accurate than the other three models. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation—2nd Edition)
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25 pages, 36647 KiB  
Article
Gravitational Deformation and Reactivation Mechanism of a Fault-Bounded Slope, Eastern Yanshan Mountains, China
by Hao Sun, Tiantao Li, Xiangjun Pei, Jian Guo, Jingjing Tian, Shoudao Wang and Mingfang Pu
Forests 2023, 14(3), 495; https://doi.org/10.3390/f14030495 - 02 Mar 2023
Cited by 1 | Viewed by 1195
Abstract
The Nandongzi landslide occurred in the Yanshan region of North China. From 2017, the slope of the Nandongzi landslide has been significantly deformed after several excavations. Field investigations show that the Nandongzi landslide is a special toppling deposit that does not have basic [...] Read more.
The Nandongzi landslide occurred in the Yanshan region of North China. From 2017, the slope of the Nandongzi landslide has been significantly deformed after several excavations. Field investigations show that the Nandongzi landslide is a special toppling deposit that does not have basic toppling conditions. The toppling deformation mechanism of the slope has become a difficult issue for engineers, attracting the attention of scientists. Joint, surface, and borehole lithology surveys revealed the surface and internal structural characteristics of the slope. The structure of the soft and hard interbedded rock and the proximity of the fault are the dominant factors of slope toppling deformation. The slope toppling failure process can be divided into four stages: initial deformation, compression and bending, toppling and overlapping, and reactivation. In the first three stages, slope toppling deformation is triggered by the downcutting of the upstream gully, gravity, and differential weathering of soft and hard rocks, which promote the dumping deformation of the slope. In the final stage, engineering excavations triggered the reactivation of residual deposits. Monitoring data indicate that slope deformation is directly related to rainfall events. Flac 3D was used to simulate the slope failure process under natural and rainfall conditions after the two excavations. The results show that multiple excavations changed the surface and runoff conditions of the slope, which led to slope failure. Rainfall promoted deformation of the back edge of the landslide, which led to shear failure from the back edge to the front edge. Our results provide new and unique understanding into the spatiotemporal evolution and deformation mechanism of similar toppling-accumulation landslides around the study area. Full article
(This article belongs to the Special Issue Landslides in Forests around the World: Causes and Mitigation—2nd Edition)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Comparison of Deep Learning and Ensemble Learning Methods in Slope-Unit-Based Landslide Susceptibility Prediction
2. Improving the Understanding of Landslide Formation in Alpine Region by InSAR Technique
3. A LightGBM-Based Landslide Susceptibility Model Considering the Uncertainty of Non-Landslide Samples
4. Analysis of Spatial Pattern and Occurrence Mechanism of Landslides Using XAI-Based Landslide Susceptibility Model
5. Susceptibility Mapping of Landslides Driven by Climate Change in the Karakoram
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