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Remote Sensing
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Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning
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Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning

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 (31 July 2024) | Viewed by 8331

Special Issue Editors

Prof. Dr. Chun Zhu

E-Mail Website
Guest Editor
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Interests: rock mechanics; geological hazard monitoring; surrounding rock support; slope stability; tunneling and excavation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhigang Tao

E-Mail Website
Guest Editor
State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Interests: rock mechanics; geological hazard monitoring; surrounding rock support; slope stability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wen Zhang

E-Mail Website
Guest Editor
College of Construction Engineering, Jilin University, Changchun 130026, China
Interests: remote sensing for geological hazards; geological hazards evolution
Special Issues, Collections and Topics in MDPI journals
Dr. Jianhua Yan

E-Mail Website
Guest Editor
College of Construction Engineering, Jilin University, Changchun, China
Interests: landslide warning; slope stability analysis; material point modeling; soil-structure interaction
Prof. Dr. Manchao He

E-Mail Website
Guest Editor
Shool of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Interests: geotechnical monitoring; surrounding rockl reinforcement; rock mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhanbo Cheng

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore
Interests: geotechnical engineering; rock engineering

Special Issue Information

Dear Colleagues,

With a rapid increase in engineering construction, the necessity for landslide prevention and control is more serious and critical. Remote sensing technology applications has shown tremendous potential in investigation, evaluation, forecast, monitoring, and warning in recent decades due to its remarkable characteristics of a large range, being all-weather, and dynamically recording the spatio-temporal changes of disasters.

This Special Issue aims to report the recent advances and trends concerning landslide prediction, monitoring, and early warning in relation to modern remote sensing techniques (ground-based, air-borne, and space-borne sensors). In particular, this Special Issue intends to give the floor to novel studies in landslide informatization construction, susceptibility analysis, runout modelling, monitoring, early warning, controlling and reinforcement. Case studies and other experiences for the successful assessment and management of recent catastrophic landslides with remote sensing techniques are also welcomed as long as they are rigorously presented and evaluated. The contributions to this Special Issue will encompass a broad spectrum of topics in the main components of landslide risk assessment and remote sensing including, but not limited to, the following topics:

  • Landslide informatization construction from multi-temporal/source remote sensing images using advanced computer algorithms;
  • Landslide susceptibility assessment by integrating probabilistic/physically based approaches and remote sensing techniques;
  • Landslide runout modeling based on a high-resolution three-dimensional terrain generated from advanced remote sensing techniques;
  • Ground-based and remote sensing techniques for landslide monitoring;
  • Development of landslide early warning framework/platform.

Prof. Dr. Chun Zhu
Prof. Dr. Zhigang Tao
Prof. Dr. Wen Zhang
Dr. Jianhua Yan
Prof. Dr. Manchao He
Dr. Zhanbo Cheng
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • remote sensing techniques
  • landslide informatization construction
  • landslide susceptibility assessment
  • landslide runout modelling
  • landslide monitoring
  • landslide early warning system
  • landslide risk mitigation

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Published Papers (6 papers)

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Research

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22 pages, 94287 KiB  
Article
Time Series Prediction of Reservoir Bank Slope Deformation Based on Informer and InSAR: A Case Study of Dawanzi Landslide in the Baihetan Reservoir Area, China
by Qiyu Li, Chuangchuang Yao, Xin Yao, Zhenkai Zhou and Kaiyu Ren
Remote Sens. 2024, 16(15), 2688; https://doi.org/10.3390/rs16152688 - 23 Jul 2024
Viewed by 729
Abstract
Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in [...] Read more.
Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning)
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20 pages, 14393 KiB  
Article
Insights into the Movement and Diffusion Accumulation Characteristics of a Catastrophic Rock Avalanche Debris—A Case Study
by Yifei Gong, Xiansen Xing, Yanan Li, Chun Zhu, Yanlin Li, Jianhua Yan, Huilin Le and Xiaoshuang Li
Remote Sens. 2023, 15(21), 5154; https://doi.org/10.3390/rs15215154 - 28 Oct 2023
Cited by 1 | Viewed by 1402
Abstract
In this study, the 1991 rock avalanche, in Touzhai, Zhaotong, Yunnan, China, was considered the study object. The investigation of the landslide accumulation body revealed that the Touzhai rock avalanche accumulation body has the characteristics of wide gradation and poor sorting. A combination [...] Read more.
In this study, the 1991 rock avalanche, in Touzhai, Zhaotong, Yunnan, China, was considered the study object. The investigation of the landslide accumulation body revealed that the Touzhai rock avalanche accumulation body has the characteristics of wide gradation and poor sorting. A combination of field investigations, indoor and outdoor experiments, and numerical simulations were used to invert the occurrence and spreading range of rock avalanche-debris flow hazards. To invert and analyze its dynamics and the crushing process, a three-dimensional discrete element modeling was performed on the real terrain data. Simulation results showed that the movement time of the numerically simulated Touzhai rock avalanche was approximately 200 s. After 50 s of movement, the peak velocity reached 32 m/s, and the velocity gradually decayed after the sliding mass rubbed violently against the valley floor and collided with the mountain. Due to the meandering nature of the gully, the sliding mass makes its way down the gully and constantly collides with the mountain, making particles appear to climb, with some particles being blocked by the valley. After 150 s of movement, the average velocity rate decreased substantially, and the landslide-avalanche debris reached the mouth of the trench. After 200 s of movement, the average sliding velocity tends to 0 m/s, where the avalanche debris tends to stop and accumulate. When the rock avalanche movement reaches the mouth of the gully, the avalanche debris spreads to the sides as it is no longer bounded by the hills on either side of the narrow gully, eventually forming a ‘trumpet-shaped’ accumulation, and the granular flow simulation matched the findings of the landslide site accumulation. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning)
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23 pages, 26668 KiB  
Article
RETRACTED: Forecasting the Landslide Blocking River Process and Cascading Dam Breach Flood Propagation by an Integrated Numerical Approach: A Reservoir Area Case Study
by Jianhua Yan, Xiansen Xing, Xiaoshuang Li, Chun Zhu, Xudong Han, Yong Zhao and Jianping Chen
Remote Sens. 2023, 15(19), 4669; https://doi.org/10.3390/rs15194669 - 23 Sep 2023
Cited by 2 | Viewed by 1649 | Retraction
Abstract
This paper aims to introduce a numerical technique for forecasting the hazard caused by the disaster chain of landslide blocking river-dam breach floods through an integration of the distinct element method (DEM) and a well-balanced finite volume type shallow water model (SFLOW). A [...] Read more.
This paper aims to introduce a numerical technique for forecasting the hazard caused by the disaster chain of landslide blocking river-dam breach floods through an integration of the distinct element method (DEM) and a well-balanced finite volume type shallow water model (SFLOW). A toppling slope in a reservoir area, the southeastern Tibetan Plateau, was chosen for the study. Creep has been observed in the potential instability area, and a possible sliding surface was identified based on the data collected from adits and boreholes. Catastrophic rock avalanches may be triggered after reservoir impoundment, and the associated landslide disaster chain needed to be predicted. First, the landslide blocking river process was modeled by the DEM using the three-dimensional particle flow code (PFC 3D). The landslide duration, runout distance, and kinematic characteristics were obtained. In addition, the landslide dam and barrier lake were constructed. Then, the cascading dam breach flood propagation was simulated using the self-developed SFLOW. The flow velocity, inundation depth, and area were obtained. The hazard maps derived from the combined numerical technique provided a quantitative reference for risk mitigation. The influences of two involved parameters on the final hazard-affected area are discussed herein. It is expected that the presented model will be applied in more prediction cases. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning)
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20 pages, 12118 KiB  
Article
Parametric Test of the Sentinel 1A Persistent Scatterer- and Small Baseline Subset-Interferogram Synthetic Aperture Radar Processing Using the Stanford Method for Persistent Scatterers for Practical Landslide Monitoring
by Farid Nur Bahti, Chih-Chung Chung and Chun-Chen Lin
Remote Sens. 2023, 15(19), 4662; https://doi.org/10.3390/rs15194662 - 22 Sep 2023
Cited by 1 | Viewed by 1242
Abstract
The landslide monitoring method that uses the Sentinel 1A Interferogram Synthetic Aperture Radar (InSAR) through the Stanford Method for Persistent Scatterers (StaMPS) method is a complimentary but complex procedure without exact guidelines. Hence, this paper delivered a parametric test by examining the optimal [...] Read more.
The landslide monitoring method that uses the Sentinel 1A Interferogram Synthetic Aperture Radar (InSAR) through the Stanford Method for Persistent Scatterers (StaMPS) method is a complimentary but complex procedure without exact guidelines. Hence, this paper delivered a parametric test by examining the optimal settings of the Sentinel 1A Persistent Scatterer (PS)- and Small Baseline Subset (SBAS)-InSAR using the StaMPS compared to the Global Navigation Satellite Systems (GNSS) in landslide cases. This study first revealed parameters with the suggested values, such as amplitude dispersion used to describe amplitude stability, ranging from 0.47 to 0.48 for PS and equal to or more than 0.6 for SBAS in WuWanZai, Ali Mt. The study further examined the suggested values for other factors, including the following: unwrap grid size to re-estimate the size of the grid; unwrap gold n win as the Goldstein filtering window to reduce the noise; and unwrap time win as the smoothing window (in days) for estimating phase noise distributions between neighboring pixels. Furthermore, the study substantiated the recommended settings in the Woda and Shadong landslide cases with the GNSS, inferring that the SBAS has adequate feasibility in practical landslide monitoring. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning)
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26 pages, 6929 KiB  
Article
A Novel Heterogeneous Ensemble Framework Based on Machine Learning Models for Shallow Landslide Susceptibility Mapping
by Haozhe Tang, Changming Wang, Silong An, Qingyu Wang and Chenglin Jiang
Remote Sens. 2023, 15(17), 4159; https://doi.org/10.3390/rs15174159 - 24 Aug 2023
Cited by 6 | Viewed by 1320
Abstract
Landslides are devastating natural disasters that seriously threaten human life and property. Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management. Machine learning (ML) models are widely used in LSM but suffer from limitations such as overfitting and unreliable accuracy. [...] Read more.
Landslides are devastating natural disasters that seriously threaten human life and property. Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management. Machine learning (ML) models are widely used in LSM but suffer from limitations such as overfitting and unreliable accuracy. To improve the classification performance of a single machine learning (ML) model, this study selects logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), and proposes a novel heterogeneous ensemble framework based on Bayesian optimization (BO), namely, stratified weighted averaging (SWA), to test its applicability in a typical landslide area in Yanbian Prefecture, China. Firstly, a dataset consisting of 1531 historical landslides was collected from field investigations and historical records, and a spatial database containing 16 predisposing factors was established. The dataset was divided into a training set and a test set in a ratio of 7:3. The results showed that SWA effectively improved the Accuracy, AUC, and robustness of the model compared to a single ML model. The SWA achieved the best classification results (Accuracy = 91.39% and AUC = 0.967). To verify the generalization ability of SWA, we selected published landslide datasets from Yanshan country and Yongxin country in China for testing. SWA also performed well, with an AUC of 0.871 and 0.860, respectively. As indicated by shapely values (SVs), Normalized Difference Vegetation Index (NDVI) is the factor that has the greatest impact on landslide occurrence. The landslide susceptibility maps obtained from this study will provide an effective reference program for land use planning and disaster prevention and mitigation projects in Yanbian Prefecture, China. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning)
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Review

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33 pages, 12109 KiB  
Review
Research Advances and Prospects of Underwater Terrain-Aided Navigation
by Rupeng Wang, Jiayu Wang, Ye Li, Teng Ma and Xuan Zhang
Remote Sens. 2024, 16(14), 2560; https://doi.org/10.3390/rs16142560 - 12 Jul 2024
Viewed by 929
Abstract
Underwater terrain-aided navigation (TAN) can obtain high-precision positioning independently and autonomously under the conditions of a communication rejection space, which is an important breakthrough for the autonomous and refined operation of deep-sea autonomous underwater vehicles near the seabed. Although TAN originated in the [...] Read more.
Underwater terrain-aided navigation (TAN) can obtain high-precision positioning independently and autonomously under the conditions of a communication rejection space, which is an important breakthrough for the autonomous and refined operation of deep-sea autonomous underwater vehicles near the seabed. Although TAN originated in the aviation field, the particularity of the underwater physical environment has led to the formation of a different theoretical and technical system. In this article, the application background, operating principles, and most important technical aspects of underwater TAN are introduced. Then, the relevant algorithms involved in the two main modules (the terrain-aided positioning module and the iterative filtering estimation module) of the underwater TAN are reviewed. Finally, other cutting-edge issues in the field of underwater TAN are summarized. The purpose of this article is to provide researchers with a comprehensive understanding of the current research status and possible future developments in the TAN field. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Landslide Prediction, Monitoring, and Early Warning)
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