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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15 pages, 10267 KiB

Open AccessArticle

Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform

by Junlei Zhao, Chong Xu and Xinwu Huang

Data 2024, 9(5), 63; https://doi.org/10.3390/data9050063 - 29 Apr 2024

Viewed by 202

Abstract

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The occurrence of landslide disasters often causes a large number of casualties along with economic losses in the local area, seriously restricting local economic development. Although risk assessment and deformation mechanism analysis for single landslides have been performed for landslide disasters in the Hancheng area, this area lacks a landslide traces database. A complete landslide database comprises the basic data required for the study of landslide disasters and is an important requirement for subsequent landslide-related research. Therefore, this study used multi-temporal high-resolution optical images and human-computer interaction visual interpretation methods of the Google Earth platform to construct a landslide traces database in Hancheng County. The results showed that at least 6785 landslides had occurred in the study area. The total area of the landslides was about 95.38 km², accounting for 5.88% of the study area. The average landslide area was 1406.04 m², the largest landslide area was 377,841 m², and the smallest landslide area was 202.96 m². The results of this study provides an important basis for understanding the spatial distribution of landslides in Hancheng County, the evaluation of landslide susceptibility, and local disaster prevention and mitigation work. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

23 pages, 42549 KiB

Open AccessArticle

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 538

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 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

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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14 pages, 11098 KiB

Open AccessArticle

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 847

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 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

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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22 pages, 2093 KiB

Open AccessArticle

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 1362

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 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

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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21 pages, 15536 KiB

Open AccessArticle

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 848

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 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

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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19 pages, 17130 KiB

Open AccessArticle

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 947

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 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

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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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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