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Ground Deformation Source Modeling Using Remote Sensing Techniques

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 12073

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Special Issue Editors

Prof. Dr. Yiping Wu

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Guest Editor

Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Interests: ground deformation; slope stability; remote sensing

Prof. Dr. Ákos Török

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Guest Editor

Department of Engineering Geology and Geotechnics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
Interests: site investigation; sedimentology; dimension stones; engineering geology; weathering
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Dr. Fasheng Miao

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Guest Editor

Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Interests: geohazards; prediction; risk assessment; remote sensing; landslides
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Abdolreza Ghods

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Guest Editor

Department of Earth Sciences, Institute for Advanced Studies in Basic Sciences, Zanjan 45, Iran
Interests: seismotectonics; geophysics; seismology; earthquake; geodynamics; seismic tomography; seismic hazard; tectonophysics

Special Issue Information

Dear Colleagues,

The application of remote sensing technology has greatly changed the access to surface data and greatly improved the accuracy of data. The development of remote sensing technology provides a new technical means for the research of earth science and engineering geology. However, how to combine remote sensing technology with ground deformation research to effectively improve the accuracy and timeliness of stability evaluation model is still a problem at the exploratory stage and needs to be solved.

This topic aims to improve the application prospect of remote sensing technology, summarize the frontier scientific achievements in ground deformation and stability modeling based on remote sensing technology, and promote the development of interdisciplinary fields centered on remote sensing to provides a satisfactory solution to the problem of the timeliness of ground deformation and stability evaluation. This topic covers the application of remote sensing technology in geoscience, geomorphology, engineering geology and other disciplines, which is in line with the objectives and themes of Remote Sensing journal.

This Topic collects original papers and inherent studies of application of remote sensing technology in earth science research (natural hazards, ground deformation, landslides prediction, stability modeling, risk assessment, etc.), but also manuscripts whose contents can help to promote the development of interdisciplinary fields centered on remote sensing technology. The following two directions are encouraged: prediction of ground deformation by remote sensing, and establishing stability model by remote sensing technology. Numerical and experimental investigations for basic or application research and representative case studies, as well as research on models and methods for geological hazards coupled with deep learning-driven remote sensing techniques are welcome too. Interdisciplinary and multidisciplinary approaches are considered added values to contribute to progress in the field of remote sensing.

Prof. Dr. Yiping Wu
Prof. Dr. Török Ákos
Dr. Fasheng Miao
Prof. Dr. Abdolreza Ghods
Guest Editors

Manuscript Submission Information

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Keywords

ground deformation
stability modeling
remote sensing
natural hazards
landslide
prediction
risk

Published Papers (6 papers)

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Research

25 pages, 19046 KiB

Open AccessArticle

Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model

by Fasheng Miao, Qiuyu Ruan, Yiping Wu, Zhao Qian, Zimo Kong and Zhangkui Qin

Remote Sens. 2023, 15(22), 5427; https://doi.org/10.3390/rs15225427 - 20 Nov 2023

Cited by 3 | Viewed by 1234

Abstract

Complex and fragile geological conditions combined with periodic fluctuations in reservoir water levels have led to frequent landslide disasters in the Three Gorges Reservoir area. With the development of remote sensing technology, many scholars have applied it to landslide susceptibility assessment to improve model accuracy; however, how to couple these two to obtain the optimal susceptibility assessment model remains to be studied. Based on Sentinel-1 data, relevant data, and existing research results, the information value method (IV), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) models were selected to analyze landslide susceptibility in the urban area of Wanzhou. Models with superior performance will be coupled with PS-InSAR deformation data using two methods: joint training and weighted overlay. The accuracy of different models was assessed and compared with the aim of determining the optimal coupling model and the role of InSAR in the model. The results indicate that the accuracy of different landslide susceptibility prediction models is ranked as RF > SVM > CNN > IV. Among the coupled dynamic models, the performance ranking was as follows: InSAR jointly trained RF (IJRF) > InSAR weighted overlay RF (IWRF) > InSAR jointly trained SVM (IJSVM) > InSAR weighted overlay SVM (IWSVM). Notably, the IJRF model, which combines InSAR deformation data through joint training, exhibited the highest accuracy, with an AUC value of 0.995. In the factor importance analysis within the IJRF model, InSAR deformation data ranked third after hydrological distance (0.210) and elevation (0.163), with a value of 0.154. A comparison between landslide dynamic susceptibility mapping (LDSM) and landslide susceptibility mapping (LSM) revealed that the inclusion of InSAR deformation data effectively reduced false positives around the landslide areas. The results suggest that joint training is the most suitable coupling method, allowing for the optimal expression of InSAR deformation data and enhancing the predictive accuracy of the model. This study serves as a reference for future research and provides a foundation for landslide risk management. Full article

(This article belongs to the Special Issue Ground Deformation Source Modeling Using Remote Sensing Techniques)

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18 pages, 8282 KiB

Open AccessArticle

Pointwise Modelling and Prediction for Ground Surface Uplifts in Abandoned Coal Mines from InSAR Observations

by Xiwen Yin, Jiayao Chai, Weinan Deng, Zefa Yang, Guochan Tian and Chao Gao

Remote Sens. 2023, 15(9), 2337; https://doi.org/10.3390/rs15092337 - 28 Apr 2023

Viewed by 898

Abstract

Interferometric synthetic aperture radar (InSAR) is a useful tool for monitoring surface uplifts due to groundwater rebound in abandoned coal mines. However, InSAR-based prediction for surface uplifts has rarely been focused on so far, hindering the scientifical assessment and controlling of uplift-related geohazards in a wide area. In this study, we firstly revealed that the temporal evolution of surface uplifts caused by groundwater rebound at a surface point approximately followed an exponential distribution. Following the result, a varied cumulative distribution function (CDF) of the Weibull distribution was then used to model the temporal evolution of surface uplifts on a point-by-point basis. Finally, the parameters of the varied Weibull CDF were inverted from historical InSAR observations of surface uplifts and were forward used to predict uplift trends. Two abandoned coal mines in Beipiao city, China, were chosen to test the presented method. The results suggest that the varied Weibull CDF is able to well describe the processing of time-series uplifts, and the root mean square errors of the predicted uplifts were about 1.2 mm. The presented pointwise method predicts surface uplifts based on historical uplift observations and a mathematical function (i.e., the varied Weibull CDF), without the requirement of in situ geological and hydrological information about the focused abandoned coal mines. Therefore, it offers a new tool for predicting surface uplifts in abandoned mines, especially in case they lack in situ geological and hydrological information. Full article

(This article belongs to the Special Issue Ground Deformation Source Modeling Using Remote Sensing Techniques)

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20 pages, 27134 KiB

Open AccessArticle

Landslide Identification in Human-Modified Alpine and Canyon Area of the Niulan River Basin Based on SBAS-InSAR and Optical Images

by Shuo Yang, Deying Li, Yujie Liu, Zhihui Xu, Yiqing Sun and Xiangjie She

Remote Sens. 2023, 15(8), 1998; https://doi.org/10.3390/rs15081998 - 10 Apr 2023

Cited by 6 | Viewed by 1464

Abstract

Landslide identification in alpine and canyon areas is difficult due to the terrain limitations. The main objective of this research was to explore the method of combining small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), multi-temporal optical images and field surveys to identify potential landslides in the human-modified alpine and canyon area of the Niulan River in southwestern China based on terrain visibility analysis. The visibility of the terrain is analyzed using the different incident and heading angles of the Sentinel satellite’s ascending and descending orbits. Based on the SAR image data of Sentinel-1A satellites from 2016 to 2019, the SBAS-InSAR method was used to identify landslides, and then multi-temporal optical images were used to facilitate landslide identification. Field surveys were carried out to verify the identification accuracy. A total of 28 landslides were identified, including 13 indicated by SBAS-InSAR, 8 by optical imaging and 7 by field investigation. Many landslides were induced by the impoundment and fluctuation of reservoir water. The comparison and verification of typical landslide monitoring data and reservoir water fluctuations revealed that a sudden drop of reservoir water had a great influence on landslide stability. These research results can facilitate a comprehensive understanding of landslide distribution in the reservoir area and guide the follow-up landslide risk management. Full article

(This article belongs to the Special Issue Ground Deformation Source Modeling Using Remote Sensing Techniques)

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19 pages, 13883 KiB

Open AccessArticle

Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas

by Liangxuan Yan, Quanbing Gong, Fei Wang, Lixia Chen, Deying Li and Kunlong Yin

Remote Sens. 2023, 15(6), 1518; https://doi.org/10.3390/rs15061518 - 10 Mar 2023

Cited by 5 | Viewed by 1842

Abstract

It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation. Full article

(This article belongs to the Special Issue Ground Deformation Source Modeling Using Remote Sensing Techniques)

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19 pages, 40672 KiB

Open AccessArticle

Deformation Monitoring and Trend Analysis of Reservoir Bank Landslides by Combining Time-Series InSAR and Hurst Index

by Xingchen Zhang, Lixia Chen and Chao Zhou

Remote Sens. 2023, 15(3), 619; https://doi.org/10.3390/rs15030619 - 20 Jan 2023

Cited by 12 | Viewed by 3296

Abstract

Landslides along the Three Gorges Reservoir in China pose a threat to coastal residents and waterway safety. To reduce false positive misjudgments caused by a sudden local change in the landslide deformation curve, in this paper, we propose an effective method for predicting the deformation trend of reservoir bank landslides. We take reservoir bank landslides in the Wanzhou District of the Three Gorges Reservoir area as the research object. The Time-Series Interferometric Synthetic Aperture Radar (InSAR) method and 62 Sentinel-1A images from 2018 to 2022 were selected for landslide deformation monitoring, and the Hurst index was calculated to characterize the deformation trend. Furthermore, we propose a method for predicting the deformation trend based on the statistical distribution of deformation rates and the physical significance of the Hurst index. After the field survey and Global Positioning System (GPS) verification, the Time-Series InSAR results are shown to be reliable. We take the Sifangbei landslide as a representative case to analyze the validation results. It is found that the determined Sifangbei landslide deformation trend is consistent with the conclusions for the region. In addition, the deformation trend of a reservoir bank slope has obvious spatial and temporal differences. Changes in the reservoir water level and concentrated rainfall play roles similar to those of catalysts. The proposed method, involving the combination of Time-Series InSAR and the Hurst index, can effectively monitor deformation and predict the stability trend of reservoir bank landslides. The presented research results provide new ideas and solutions for landslide prevention and risk mitigation in reservoir areas. Full article

(This article belongs to the Special Issue Ground Deformation Source Modeling Using Remote Sensing Techniques)

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29 pages, 46026 KiB

Open AccessArticle

Failure Mechanism Analysis of Mining-Induced Landslide Based on Geophysical Investigation and Numerical Modelling Using Distinct Element Method

by Jun Li, Bin Li, Kai He, Yang Gao, Jiawei Wan, Weile Wu and Han Zhang

Remote Sens. 2022, 14(23), 6071; https://doi.org/10.3390/rs14236071 - 30 Nov 2022

Cited by 6 | Viewed by 1490

Abstract

Underground mining activity in the karst mountain in southwestern China has induced several large-scale rocky landslides and has caused serious casualties. At present, there is a lack of systematic research on the formation mechanism of landslides in this area using multi-method fusion technology. First, the orthophoto images of the landslide area obtained by UAV photography were used to analyze the deformation characteristics of the landslide. Second, the failure characteristics of the strata overlying the goaf were analyzed by geophysical detection. Finally, the deformation response characteristics of the mountain under underground mining were analyzed by UDEC numerical simulation. The results revealed that during the underground mining, the failure process of the mountain occurred in four stages: fracture expansion, subsidence and collapse, shear sliding, and multi-level sliding. Gently dipping soft–hard alternant strata and a blocky rock mass structure formed the geological foundation of the landslides. Underground mining accelerated the fracturing of the overlying strata and the formation of a stepped penetrating sliding surface. Tensile movement of the structural planes of hard sandstone in the free face, and shear sliding of the weak mudstone layer, were the main causes of the landslides. The slope instability mode was tension-shear fracturing, shear sliding, back toppling, and compressive shear failure. In addition, the fracture propagation in the overlying strata and damaged geological structure revealed by the geophysical detection were consistent with the simulation results. This study provides ideas for the precise countermeasures of disaster prevention and mitigation for similar landslides in this area. Full article

(This article belongs to the Special Issue Ground Deformation Source Modeling Using Remote Sensing Techniques)

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