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Rockfall Hazard Analysis Using Remote Sensing Techniques

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 13291

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Dr. Tao Chen

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School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China
Interests: intelligent representation and calculation of geological information; geological environment monitoring and evaluation; geospatial information
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Prof. Dr. Weile Li

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State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China
Interests: InSAR; optical remote sensing; landslide detection and monitoring
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Prof. Dr. Chaoying Zhao

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School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
Interests: SAR; InSAR; land subsidence; landslides; glacier movement; collapse
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Prof. Dr. Chong Xu

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National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: landslides; geological hazards; remote sensing; geology
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Prof. Dr. Antonio J. Plaza

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Department of Technology of Computers and Communications, University of Extremadura, 10003 Caceres, Spain
Interests: hyperspectral imaging; parallel computing; remote sensing; geoscience; GPU
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Dear Colleagues,

Rockfall is one of the most dangerous and harmful phenomena in mountainous and hilly areas. Such incidents can cause huge losses to lives, property and infrastructure. Therefore, appropriate rockfall hazards analysis methods are needed to save lives and provide guidance for regional development. Remote sensing technology, which has undergone significant development, has become an important technical means and has been popularly used in numerous studies. Unmanned aerial vehicle (UAV) photogrammetry and 3D laser scanning, for example, have been successively applied to the early identification of rockfall hazards. Multi-temporal imagery data acquired by optical satellites and aircrafts have also been used to identify potential rockfall source areas.

This Special Issue aims to collect studies that cover different means of remote sensing data acquired by various platforms or sensors in rockfall hazard analysis.

This Special Issue aims to publish high-quality research papers, as well as salient and informative review articles, addressing emerging trends in remote sensing-based rockfall hazard analysis. Original contributions, not currently under review in a journal or a conference, solicited in relevant areas are welcome. We look forward to receiving your contributions.

Dr. Tao Chen
Prof. Dr. Weile Li
Prof. Dr. Chaoying Zhao
Prof. Dr. Chong Xu
Prof. Dr. Antonio J. Plaza
Guest Editors

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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

mapping and measurement of rockfall based on remote sensing
remote sensing-based methods for rockfall hazard analysis
rockfall-triggering factors
parameters for rockfall analysis
detection and characterization of rockfall
landslides
rockfall monitoring
optical/multispectral/hyperspectral image processing
SAR/LiDAR
unmanned aerial system (UAS)

Related Special Issue

Rockfall Hazard Analysis Using Remote Sensing Techniques (Second Edition) in Remote Sensing

Published Papers (8 papers)

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Research

17 pages, 27986 KiB

Open AccessArticle

The Detection and Control Factor Analysis of Active Landslides in Guizhou Province, China, Using Sentinel-1 SAR Imagery

by Liquan Chen, Chaoying Zhao, Hengyi Chen, Ya Kang, Bin Li and Xiaojie Liu

Remote Sens. 2023, 15(23), 5468; https://doi.org/10.3390/rs15235468 - 23 Nov 2023

Cited by 1 | Viewed by 782

Abstract

Catastrophic landslides occur frequently in Guizhou Province, China, and the landslides in this area have special geomorphological, geological, and anthropogenic features. In order to detect and explore the distribution pattern and control factors of active landslides in Guizhou, firstly, a total of 693 active landslides throughout Guizhou Province were mapped based on the deformation rate, which was obtained by spatiotemporal filtering and Intermittent Small Baseline Subset (ISBAS) Interferometric Synthetic Aperture Radar (InSAR) techniques. Then, the relationships between the detected landslides and elevation, aspect, slope gradient, and stratigraphic lithology were analysed. Moreover, it was found that the landslides were mainly concentrated in three stratigraphic combinations, that is

T_{1} f ~ P_{2} l - d

T_{1} f ~ T_{1} y n

, and

T_{2} g ~ T_{1} y n

. Thereafter, the correlation coefficients between the landslide density and elevation and distance to the stratigraphic boundary were 0.54 and −0.19, indicating that the distribution of landslides was significantly controlled by the elevation and the boundary of specific stratigraphic combinations. Finally, we chose a typical landslide to explore how landslide development was controlled by the combined effects of elevation and stratigraphy by using ascending and descending InSAR results. We revealed that landslides occurred primarily in areas with a steep slope and a stratigraphy characterized by mudstone and sandstone. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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21 pages, 109354 KiB

Open AccessArticle

Deformation Monitoring and Dynamic Analysis of Long-Runout Bedding Landslide Based on InSAR and Particle Flow Code

by Yang Gao, Jun Li, Xiaojie Liu, Weile Wu, Han Zhang and Pengfei Liu

Remote Sens. 2023, 15(21), 5105; https://doi.org/10.3390/rs15215105 - 25 Oct 2023

Viewed by 840

Abstract

Long-runout landslides occur frequently in the sandstone and mudstone mountainous areas in southwestern China under heavy rainfall conditions. This has been a key issue in the field of disaster prevention and reduction. Considering the Niuerwan landslide in Wulong, Chongqing, on 13 July 2020, as an example, we employed technical methodologies, including unmanned aerial vehicle (UAV) images, field investigation, geological condition analysis (including geomorphology and topography, stratigraphic structure and formation lithology, etc.), interferometric synthetic aperture radar (InSAR) monitoring and Particle Flow Code 3D (PFC^3D) simulations to study failure mechanism and a long-runout motion model of flow-like landslides induced by the heavy rainfall. The results showed that (1) the large differences between the upper and lower strata are the root cause of the instability and long-runout fluidization movement; (2) heavy rainfall is the key driving factor of slope instability and deep-seated landslides, leading to long-distance movement of the upper saturated residual soil; (3) the long-runout fluidization model of bedding landslides is mainly divided into the overall sliding in the lower layer, the mixing of coarse and fine particles in the middle layer, and saturation fluidization in the upper layer; and (4) the long-runout fluidization process of bedding landslides is composed of three stages: overall instability, mixed acceleration, and fluidization accumulation. In view of these findings, in the risk evaluation and prediction of long-runout fluidization landslides in sandstone and mudstone mountainous areas, this particular disaster model can be used to provide quantitative references for disaster prevention and mitigation. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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22 pages, 8432 KiB

Open AccessArticle

Drone Photogrammetry for Accurate and Efficient Rock Joint Roughness Assessment on Steep and Inaccessible Slopes

by Jiamin Song, Shigui Du, Rui Yong, Changshuo Wang and Pengju An

Remote Sens. 2023, 15(19), 4880; https://doi.org/10.3390/rs15194880 - 09 Oct 2023

Cited by 2 | Viewed by 1101

Abstract

The roughness of rock joints exerts a substantial influence on the mechanical behavior of rock masses. In order to identify potential failure mechanisms and to design effective protection measures, the accurate measurement of joint roughness is essential. Traditional methods, such as contact profilometry, laser scanning, and close-range photogrammetry, encounter difficulties when assessing steep and inaccessible slopes, thus hindering the safety and precision of data collection. This study aims to assess the feasibility of utilizing drone photogrammetry to quantify the roughness of rock joints on steep and inaccessible slopes. Field experiments were conducted, and the results were compared to those of 3D laser scanning in order to validate the approach’s procedural details, applicability, and measurement accuracy. Under a 3 m image capture distance using drone photogrammetry, the root mean square error of the multiscale model-to-model cloud comparison (M3C2) distance and the average roughness measurement error were less than 0.5 mm and 10%, respectively. The results demonstrate the feasibility and potential of drone photogrammetry for joint roughness measurement challenges, providing a useful tool for practitioners and researchers pursuing innovative solutions for assessing rock joint roughness on precipitous and hazardous slopes. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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33 pages, 16484 KiB

Open AccessArticle

Rapid Landslide Extraction from High-Resolution Remote Sensing Images Using SHAP-OPT-XGBoost

by Na Lin, Di Zhang, Shanshan Feng, Kai Ding, Libing Tan, Bin Wang, Tao Chen, Weile Li, Xiaoai Dai, Jianping Pan and Feifei Tang

Remote Sens. 2023, 15(15), 3901; https://doi.org/10.3390/rs15153901 - 07 Aug 2023

Cited by 4 | Viewed by 1549

Abstract

Landslides, the second largest geological hazard after earthquakes, result in significant loss of life and property. Extracting landslide information quickly and accurately is the basis of landslide disaster prevention. Fengjie County, Chongqing, China, is a typical landslide-prone area in the Three Gorges Reservoir Area. In this study, we newly integrate Shapley Additive Explanation (SHAP) and Optuna (OPT) hyperparameter tuning into four basic machine learning algorithms: Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Additive Boosting (AdaBoost). We construct four new models (SHAP-OPT-GBDT, SHAP-OPT-XGBoost, SHAP-OPT-LightGBM, and SHAP-OPT-AdaBoost) and apply the four new models to landslide extraction for the first time. Firstly, high-resolution remote sensing images were preprocessed, landslide and non-landslide samples were constructed, and an initial feature set with 48 features was built. Secondly, SHAP was used to select features with significant contributions, and the important features were selected. Finally, Optuna, the Bayesian optimization technique, was utilized to automatically select the basic models’ best hyperparameters. The experimental results show that the accuracy (ACC) of these four SHAP-OPT models was above 92% and the training time was less than 1.3 s using mediocre computational hardware. Furthermore, SHAP-OPT-XGBoost achieved the highest accuracy (96.26%). Landslide distribution information in Fengjie County from 2013 to 2020 can be extracted by SHAP-OPT-XGBoost accurately and quickly. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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29 pages, 15248 KiB

Open AccessArticle

Study on the Damage Mechanism of Sandstone under Different Water Content States

by Hongjian Wang, Zhendong Cui, Chong Xu, Rui Yong, Fei Zhao and Shangyuan Chen

Remote Sens. 2023, 15(12), 3127; https://doi.org/10.3390/rs15123127 - 15 Jun 2023

Viewed by 1182

Abstract

Understanding the failure mechanisms of rocks that are exposed to different water contents is important for rock stability in rock engineering applications, and the quantitative analysis of rock behavior is necessary for predicting and preventing the occurrence of rock failure due to water effects. Mechanical tests using real-time acoustic emission (AE) technology were carried out to reveal the damage evolution in sandstone rocks in a dried state, natural state, and saturated state, which includes a quantitative analysis of AE characteristics and cracking properties. The testing results indicate that with the growth of water content, sandstone rocks show a decreasing trend in strength and tend to experience gentle damage with relatively fewer fractures. The crack morphology of the main fracture surfaces is quantitatively described, including a fractal dimension calculation and cracking length measurements. As the water content rises, when rock failure occurs, a higher AE b-value can be obtained, revealing an increasing proportion of large-scale cracks. The fractal dimension of the acoustic emission hit rate shows that the evolution of rock damage and deformation has self-similarity, that is, the transformation from order to disorder to order, and it is affected by different water contents. The AE waveforms of the sandstone have two dominant frequency bands (0~75 kHz and 75~150 kHz) no matter which water-bearing state they are in. The increase in rock water content has resulted in the decline of AE waveforms located in the range of 200–300 kHz, whereas the rise of AE waveforms is located in the range of 0–50 kHz. The findings of this study deepen our understanding of the mechanism behind rock failure and provide a meaningful reference for disaster assessment and control. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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18 pages, 18917 KiB

Open AccessArticle

Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation

by Baohang Wang, Chaoying Zhao, Qin Zhang, Xiaojie Liu, Zhong Lu, Chuanjin Liu and Jianxia Zhang

Remote Sens. 2023, 15(8), 2097; https://doi.org/10.3390/rs15082097 - 16 Apr 2023

Cited by 1 | Viewed by 1492

Abstract

Today, synthetic aperture radar (SAR) satellites provide large amounts of SAR data at unprecedented temporal resolutions, which promotes hazard dynamic monitoring and disaster mitigation with interferometric SAR (InSAR) technology. This study focuses on big InSAR data dynamical processing in areas of serious decorrelation and large gradient deformation. A new stepwise temporal phase optimization method is proposed to alleviate the decorrelation, customized for deformation parameter dynamical estimation. Subsequently, the sequential estimation theory is introduced to the intermittent small baseline subset (ISBAS) approach to dynamically obtain deformation time series with dense coherent targets. Then, we analyze the reason for the unstable accuracy of deformation parameters using sequential distributed scatterers-ISBAS technology, and construct five indices to describe the quality of deformation parameters pixel-by-pixel. Finally, real data of the post-failure Baige landslide at the Jinsha River in China is used to demonstrate the validity of the proposed approach. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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17 pages, 33523 KiB

Open AccessArticle

Landslide Detection Using Time-Series InSAR Method along the Kangding-Batang Section of Shanghai-Nyalam Road

by Yaning Yi, Xiwei Xu, Guangyu Xu and Huiran Gao

Remote Sens. 2023, 15(5), 1452; https://doi.org/10.3390/rs15051452 - 05 Mar 2023

Cited by 3 | Viewed by 3452

Abstract

Due to various factors such as urban development, climate change, and tectonic movements, landslides are a common geological phenomenon in the Qinghai–Tibet Plateau region, especially on both sides of a road, where large landslide hazards often result in traffic disruptions and casualties. Identifying the spatial distribution of landslides and monitoring their stability are essential for predicting landslide occurrence and implementing prevention measures. In this study, taking the Kangding-Batang section of Shanghai-Nyalam Road as the study area, we adopted a semi-automated time-series interferometric synthetic aperture radar (InSAR) method to identify landslides and monitor their activity. A total of 446 Sentinel-1 ascending and descending SAR images from January 2018 to December 2021 were thus collected and processed by using open-source InSAR processing software. After a series of error corrections, we obtained surface deformation maps covering the study area, and a total of 236 potential landslides were subsequently identified and classified into three categories, namely slow-sliding rockslides, debris flows, and debris avalanches, by combining deformation maps, optical images, and a digital elevation model (DEM). For a typical landslide, we performed deformation decomposition and analyzed the relationship between its deformation and rainfall, revealing the contribution of rainfall to the landslide. In addition, we discussed the effect of SAR geometric distortion on landslide detection, highlighting the importance of joint ascending and descending observations in mountainous areas. We analyzed the controlling factors of landslide distribution and found that topographic conditions are still the dominant factor. Our results may be beneficial for road maintenance and disaster mitigation. Moreover, the entire processing is semi-automated based on open-source tools or software, which provides a paradigm for landslide-related studies in other mountainous regions of the world. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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23 pages, 43818 KiB

Open AccessArticle

The Early Identification and Spatio-Temporal Characteristics of Loess Landslides with SENTINEL-1A Datasets: A Case of Dingbian County, China

by Zhuo Jiang, Chaoying Zhao, Ming Yan, Baohang Wang and Xiaojie Liu

Remote Sens. 2022, 14(23), 6009; https://doi.org/10.3390/rs14236009 - 27 Nov 2022

Cited by 4 | Viewed by 1289

Abstract

Loess landslides represent an important geohazard in relation to the deformation of unstable loess structures occurred on the slope of loess-covered area. It has become one of the important topics to accurately identify the distribution and activity of loess landslides and describe the spatio-temporal kinematics in the western-project construction in China. Interferometric synthetic aperture radar (InSAR) proves to be effective for landslides investigation. This study proposes an improved InSAR-based procedure for large-area landslide mapping in loess-hilly areas, including tropospheric-delay correction based on quadtree segmentation and automatic selection of interferograms based on minimum-error boundary. It is tested in Dingbian County in Shaanxi Province, China. More than 200 SAR images were processed and a total of 50 potential loess landslides were detected and mapped. Results show that the landslides are mainly distributed along the river basins and concentrated in areas with elevation ranging from 1450 m to 1650 m, and with slope angles of 10–40°. Then, a total of eight (16%) loess landslides are classified as active ones based on three parameters derived from InSAR-deformation rates: activity index (AI), mean deformation rate, and maximum deformation rate. Moreover, we characterize the segmentation of detected landslides and describe the discrepancy of local topography and deformation rates by coupling the peak in probability-density curves of deformation rates and profiles of the elevation and deformation rates. Finally, correlation between landslide deformation and rainfall is given through wavelet analysis. Full article

(This article belongs to the Special Issue Rockfall Hazard Analysis Using Remote Sensing Techniques)

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