Topic Editors

National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Dr. Yingying Tian
Institute of Geology, China Earthquake Administration, Beijing 100029, China
Dr. Xiaoyi Shao
Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
School of Civil Engineering and Architecture, Anhui University of Science and Technology, Huainan 232001, China

Database, Mechanism and Risk Assessment of Slope Geologic Hazards

Abstract submission deadline
30 November 2024
Manuscript submission deadline
28 February 2025
Viewed by
5233

Topic Information

Dear Colleagues,

The slope geo-disaster is a significant hazard in mountainous areas. With an extreme climate and tectonic events (i.e., rainfall, wildfire, earthquake, and snow or ice melting) becoming frequent, slope failures are becoming more and more common throughout the world. Landslides, debris flows, and avalanches are the three main sub-categories of slope instabilities. They cause serious casualties and economic loss by burying buildings and farmlands, blocking rivers, destroying roads and railways, and inducing fires. Thus, slope instability is the hot topic in earth science research. So far, the most effective way to explore the temporal and spatial distribution laws and cause mechanisms of slope failures has been based on disasters that have already happened. Though lots of related research has been published, it is necessary to keep our eyes on different kinds of slope failures in various places. This topic focuses on slope geo-disasters and collects articles on disaster detection and mapping, database compiling, cause mechanisms, susceptibility, and risk mapping. Topics of interest include, but are not limited to, the following:

  • New techniques to detect slope instability (including landslides, debris flows, and avalanches);
  • Database of slope instability hazards related to extreme events (e.g., rainfalls, earthquakes, or wildfires) or mountainous areas;
  • Characteristics and mechanisms of slope instabilities;
  • Numerical modeling and the whole life-circle analyses of large slope failure(s);
  • Susceptibility mapping and risk assessment of slope failures;
  • Post-failure evolution and prediction of slope geo-disasters temporally and spatially.

Prof. Dr. Chong Xu
Dr. Yingying Tian
Dr. Xiaoyi Shao
Dr. Zikang Xiao
Dr. Yulong Cui
Topic Editors

Keywords

  • slope geo-disaster
  • database
  • mechanism
  • susceptibility
  • risk
  • evolution
  • prediction
  • remote sensing
  • GIS
  • machine learning

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Big Data and Cognitive Computing
BDCC
3.7 4.9 2017 18.2 Days CHF 1800 Submit
Data
data
2.6 4.6 2016 22 Days CHF 1600 Submit
Environments
environments
3.7 5.9 2014 23.7 Days CHF 1800 Submit
Geosciences
geosciences
2.7 5.2 2011 23.6 Days CHF 1800 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit

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Published Papers (5 papers)

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23 pages, 42549 KiB  
Article
Quick Extraction of Joint Surface Attitudes and Slope Preliminary Stability Analysis: A New Method Using Unmanned Aerial Vehicle 3D Photogrammetry and GIS Development
by Qiyu Li, Xin Yao, Renjiang Li, Zhenkai Zhou, Chuangchuang Yao and Kaiyu Ren
Remote Sens. 2024, 16(6), 1022; https://doi.org/10.3390/rs16061022 - 14 Mar 2024
Viewed by 474
Abstract
The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated [...] Read more.
The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics’ surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices. Full article
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14 pages, 11098 KiB  
Article
Inventory of Landslides in the Northern Half of the Taihang Mountain Range, China
by Xuewei Zhang, Chong Xu, Lei Li, Liye Feng and Wentao Yang
Geosciences 2024, 14(3), 74; https://doi.org/10.3390/geosciences14030074 - 10 Mar 2024
Viewed by 706
Abstract
The Taihang Mountains are a critical mountain range and geographical boundary in eastern China. Landslide disasters are particularly common in this region and usually cause serious casualties and property damage. However, previous landslide inventories in the region are limited and lack comprehensive landslide [...] Read more.
The Taihang Mountains are a critical mountain range and geographical boundary in eastern China. Landslide disasters are particularly common in this region and usually cause serious casualties and property damage. However, previous landslide inventories in the region are limited and lack comprehensive landslide cataloguing. To address this gap, the northern half of the Taihang Mountain Range was selected for this study. A landslide database for the area was constructed using multi-temporal high-resolution optical imagery from the Google Earth and human–computer interactive visual interpretation technology. The results indicate that at least 8349 landslides have occurred in the Taihang Mountain Range, with a total landslide area of about 151.61 km2. The size of the landslides varies, averaging about 18,159.23 m2, with the largest landslide covering 2.83 km2 and the smallest landslide only 5.95 m2. The significance of this study lies in its ability to enhance our understanding of the distribution of landslides in the northern half of the Taihang Mountains. Furthermore, it offers valuable data references and supports for landslide assessment, early warning systems, disaster management, and ecological protection efforts. Full article
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22 pages, 2093 KiB  
Article
Comparison of Rating Systems for Alberta Rock Slopes, and Assessment of Applicability for Geotechnical Asset Management
by Taylor Del Gerhard Wollenberg-Barron, Renato Macciotta Pulisci, Chris Gräpel, Kristen Tappenden and Roger Skirrow
Geosciences 2023, 13(11), 348; https://doi.org/10.3390/geosciences13110348 - 14 Nov 2023
Viewed by 1332
Abstract
In 1999, Alberta Transportation and Economic Corridors (TEC) implemented the Geohazard Risk Management Program (GRMP) to identify, assess, monitor, and prioritize the mitigation of risk resulting from geohazard events at specific sites along the provincial highway network. The GRMP was developed to address [...] Read more.
In 1999, Alberta Transportation and Economic Corridors (TEC) implemented the Geohazard Risk Management Program (GRMP) to identify, assess, monitor, and prioritize the mitigation of risk resulting from geohazard events at specific sites along the provincial highway network. The GRMP was developed to address a variety of geohazard types including rockfall hazards that occur at natural and constructed (cut) highway backslopes. An evaluation of various methods for the condition assessment of rockfall geohazards, including TEC’s current GRMP risk rating system, has been completed with the intent of better understanding the suitability of each method as TEC transitions to a formalized GAM program. The GRMP risk rating values for selected rockfall geohazard sites along highway corridors in Alberta were compared to values developed from the results of five established rock mass and rock slope rating systems. The results of this study demonstrate that TEC’s current GRMP risk rating system is a viable tool for the condition assessment and performance monitoring of rockfall geohazards, which could be utilized within a formalized GAM program, further benefitting from years of recorded application in Alberta. Of the other rating systems tested, the rockfall hazard rating system (RHRS) showed a strong correlation with the GRMP risk rating while Q-Slope, the Geological Strength Index (GSI) and Rock Mass Rating (RMR) correlation were marginal but displayed a potential for use as condition assessment tools. The work presented in this paper provides the first evaluation of rock slope rating systems for rockfall hazards along corridors in Alberta, directly comparing them to the slope performance as observed by TEC in a quantitative manner. Full article
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21 pages, 15536 KiB  
Article
Analysis of the Controlling Effect of Excess Topography on the Distribution of Coseismic Landslides during the Iburi Earthquake, Japan, on 6 September 2018
by Pengfei Zhang, Hengzhi Qiu, Chong Xu, Xiaoli Chen and Qing Zhou
Remote Sens. 2023, 15(20), 5035; https://doi.org/10.3390/rs15205035 - 20 Oct 2023
Viewed by 790
Abstract
Coseismic landslides cause changes in the hillside material, and this erosion process plays an important role in the evolution of the topography. Previous studies seldom involved research on the influence of excess topography on the occurrences of coseismic landslides. The Iburi earthquake, which [...] Read more.
Coseismic landslides cause changes in the hillside material, and this erosion process plays an important role in the evolution of the topography. Previous studies seldom involved research on the influence of excess topography on the occurrences of coseismic landslides. The Iburi earthquake, which occurred in Japan on 6 September 2018 and triggered a large number of landslides, provided a research example to explore the relationship between coseismic landslides and excess topography. We used the average slope of the lithology as the threshold slope of the corresponding stratum to calculate the excess topography of the different lithological units. Based on the advanced spaceborne thermal emission and reflection radiometer (ASTER) digital elevation model (DEM) with a resolution of 30 m, a quantitative analysis was conducted on the excess topography in the study area. The results indicate that the excess topography in the study area was mainly distributed in the valleys on both sides of the river, and the thickness of the excess topography on the high and steep ridges was generally greater than that at the foot of the slope, which has a relatively flat topography or a low elevation. In the area affected by the earthquake, approximately 94.66% of the coseismic landslides (with an area of approximately 28.23 m2) developed in the excess topography area, indicating that the distribution of the excess topography had a strong controlling influence on the spatial distribution of the coseismic landslides. The Iburi earthquake mainly induced shallow landslides, but the thickness of the landslide body was much smaller than the excess topography height in the landslides-affected area. This may imply that the excess topography was not completely removed by the coseismic landslides, and the areas where the earthquake landslides occurred still have the possibility of producing landslides in the future. Full article
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19 pages, 17130 KiB  
Article
Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake
by Siyuan Ma, Xiaoyi Shao and Chong Xu
Remote Sens. 2023, 15(19), 4733; https://doi.org/10.3390/rs15194733 - 27 Sep 2023
Cited by 3 | Viewed by 900
Abstract
The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with [...] Read more.
The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with this event and assess the performance of the most widely used machine learning algorithms of logistic regression (LR) and random forest (RF). Eight controlling factors were considered, including elevation, hillslope gradient, aspect, relief, distance to rivers, peak ground velocity (PGV), peak ground acceleration (PGA), and lithology. To evaluate model uncertainty, training samples were randomly selected and used to establish the models 20 times, resulting in 20 susceptibility maps for different models. The quality of the landslide susceptibility maps was evaluated using several metrics, including the mean landslide susceptibility index (LSI), modelling uncertainty, and predictive accuracy. The results demonstrate that both models effectively capture the actual distribution of landslides, with areas exhibiting high LSI predominantly concentrated on both sides of the seismogenic fault. The RF model exhibits less sensitivity to changes in training samples, whereas the LR model displays significant variation in LSI with sample changes. Overall, both models demonstrate satisfactory performance; however, the RF model exhibits superior predictive capability compared to the LR model. Full article
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