Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing in Earth Surface Changes and Deformations Caused by...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide

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 December 2022) | Viewed by 35064

Special Issue Editors

Prof. Dr. Yi Wang

E-Mail Website
Guest Editor
Institute of Geophysics & Geomatics, China University of Geosciences, 430074, China
Interests: remote sensing image processing; deep learning; geological data intelligent learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun Hu

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: InSAR; PolInSAR; 3-D deformation mapping; geohazard monitoring and interpreting; earthquake; landslide
Special Issues, Collections and Topics in MDPI journals
Dr. Weile Li

E-Mail Website
Guest Editor
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Interests: landslide detection; landslide monitoring; landslide prediction; landslide risk assessment; remote sensing; InSAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The occurrence of earthquake and landslide events often leads to significant surface changes, and understanding these changes is of great significance for post-disaster early warning, prevention, and risk management. In addition, surface deformations before, during, and after earthquake and landslide events provide useful information for the interpretation and evaluation of the disasters. With the rapid development of earth observation technology, the types of hyperspectral, multi-polarization, high spatial, and temporal resolution sensors are becoming more and more abundant, and the data volume is increasing explosively, providing important support for the monitoring and investigation of earth surface changes and deformations.

Contactless devices are not invasive and allow measuring without access to the study area, which is a superior advantage as earth surface changes and deformations often occur in remote areas and can be potentially dangerous or accessible with difficulty. Today, remote sensing data play a big role in geosciences. With recent advancements in technologies such as UAVs, multi-band high-resolution satellite images, and multi-polarization microwave-based SAR images coupled with the state-of-the-art machine learning tools, the application of observing and mapping earth surface changes and deformations has become more popular.

The aim of this Special Issue is to collect the most recent research on remote sensing applications in earth sciences. In particular, this Special Issue is dedicated to satellite, aerial and terrestrial contactless devices for observation and evaluation of earth surface changes and deformations caused by earthquake and landslide, and new processing techniques related to remote sensing. We invite you to submit scientific, technological, or review articles about recent research within one or more of these topics:

  • Detection of earth surface changes—multitemporal remote sensing;
  • Mapping, modeling, and/or monitoring approaches in earth surface changes and deformations;
  • Evaluating the earth surface status and creating novel solutions by integrating remote sensing and GIS techniques;
  • Remote sensing of earthquake and landslide deformation monitoring.

Prof. Dr. Yi Wang
Prof. Dr. Jun Hu
Prof. Dr. Weile Li
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
  • earth surface change
  • surface deformation
  • earthquakes
  • landslides
  • hazard detection
  • hazard mapping
  • hazard evaluation

Published Papers (17 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

21 pages, 4200 KiB  
Article
Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets
by Liuru Hu, Roberto Tomás, Xinming Tang, Juan López Vinielles, Gerardo Herrera, Tao Li and Zhiwei Liu
Remote Sens. 2023, 15(4), 996; https://doi.org/10.3390/rs15040996 - 10 Feb 2023
Cited by 3 | Viewed by 1525
Abstract
Slope failures, subsidence, earthworks, consolidation of waste dumps, and erosion are typical active deformation processes that pose a significant hazard in current and abandoned mining areas, given their considerable potential to produce damage and affect the population at large. This work proves the [...] Read more.
Slope failures, subsidence, earthworks, consolidation of waste dumps, and erosion are typical active deformation processes that pose a significant hazard in current and abandoned mining areas, given their considerable potential to produce damage and affect the population at large. This work proves the potential of exploiting space-borne InSAR and airborne LiDAR techniques, combined with data inferred through a simple slope stability geotechnical model, to obtain and update inventory maps of active deformations in mining areas. The proposed approach is illustrated by analyzing the region of Sierra de Cartagena-La Union (Murcia), a mountainous mining area in southeast Spain. Firstly, we processed Sentinel-1 InSAR imagery acquired both in ascending and descending orbits covering the period from October 2016 to November 2021. The obtained ascending and descending deformation velocities were then separately post-processed to semi-automatically generate two active deformation areas (ADA) maps by using ADATool. Subsequently, the PS-InSAR LOS displacements of the ascending and descending tracks were decomposed into vertical and east-west components. Complementarily, open-access, and non-customized LiDAR point clouds were used to analyze surface changes and movements. Furthermore, a slope stability safety factor (SF) map was obtained over the study area adopting a simple infinite slope stability model. Finally, the InSAR-derived maps, the LiDAR-derived map, and the SF map were integrated to update a previously published landslides’ inventory map and to perform a preliminary classification of the different active deformation areas with the support of optical images and a geological map. Complementarily, a level of activity index is defined to state the reliability of the detected ADA. A total of 28, 19, 5, and 12 ADAs were identified through ascending, descending, horizontal, and vertical InSAR datasets, respectively, and 58 ADAs from the LiDAR change detection map. The subsequent preliminary classification of the ADA enabled the identification of eight areas of consolidation of waste dumps, 11 zones in which earthworks were performed, three areas affected by erosion processes, 17 landslides, two mining subsidence zone, seven areas affected by compound processes, and 23 possible false positive ADAs. The results highlight the effectiveness of these two remote sensing techniques (i.e., InSAR and LiDAR) in conjunction with simple geotechnical models and with the support of orthophotos and geological information to update inventory maps of active deformation areas in mining zones. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

22 pages, 7105 KiB  
Article
A Combination of Deep Autoencoder and Multi-Scale Residual Network for Landslide Susceptibility Evaluation
by Zhuolu Wang, Shenghua Xu, Jiping Liu, Yong Wang, Xinrui Ma, Tao Jiang, Xuan He and Zeya Han
Remote Sens. 2023, 15(3), 653; https://doi.org/10.3390/rs15030653 - 22 Jan 2023
Cited by 6 | Viewed by 1416
Abstract
Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression [...] Read more.
Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression of nonlinear relationships among evaluation factors, this paper proposes a new evaluation method of landslide susceptibility combining deep autoencoder and multi-scale residual network (DAE-MRCNN). In the first step, a deep autoencoder network was used to learn the feature expression of the original landslide data in order to acquire effective features in the data. Next, a multi-scale residual network was constructed; specifically, the model was improved into a deep residual network model by adding skip connections in the convolutional layer. In addition, the multi-scale idea was utilized to fully extract the scale characteristics of the evaluation factors. Finally, the model was used for feature training, and the results were input into the Softmax classifier to complete the prediction of landslide susceptibility. For this purpose, a machine learning method and two state-of-the-art deep learning methods, namely SVM, CPCNN-ML, and 2D-CNN, were utilized to model landslide susceptibility in Hanzhong City, Shaanxi Province. The proposed method produced the highest model performance of 0.891, followed by 0.842, 0.869, and 0.873. The experimental results show that the DAE-MRCNN method can fully express the complex nonlinear relationships among the evaluation factors, alleviate the problem of insufficient samples in convolutional neural networks (CNN) training, and significantly improve the accuracy of susceptibility prediction. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

24 pages, 8739 KiB  
Article
Landslide Hazard Assessment Method Considering the Deformation Factor: A Case Study of Zhouqu, Gansu Province, Northwest China
by Cong Dai, Weile Li, Huiyan Lu and Shuai Zhang
Remote Sens. 2023, 15(3), 596; https://doi.org/10.3390/rs15030596 - 19 Jan 2023
Cited by 8 | Viewed by 1912
Abstract
Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; [...] Read more.
Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

24 pages, 15270 KiB  
Article
Effects of the Gully Land Consolidation Project on Geohazards on a Typical Watershed on the Loess Plateau of China
by Xiaochen Wang, Qiang Xu, Chuanhao Pu, Weile Li, Kuanyao Zhao, Zhigang Li, Wanlin Chen and Dehao Xiu
Remote Sens. 2023, 15(1), 113; https://doi.org/10.3390/rs15010113 - 25 Dec 2022
Cited by 1 | Viewed by 2195
Abstract
From 2011 to 2013, a mega project, known as the Gully Land Consolidation Project (GLCP), was implemented in the hilly gully region atop China’s Loess Plateau. However, the GLCP involved large-scale slope excavation and gully backfilling that changed the local geological environment, which [...] Read more.
From 2011 to 2013, a mega project, known as the Gully Land Consolidation Project (GLCP), was implemented in the hilly gully region atop China’s Loess Plateau. However, the GLCP involved large-scale slope excavation and gully backfilling that changed the local geological environment, which led to serious geohazards, such as erosion, soil salinization, and dam failure. In this study, various geohazards caused by the GLCP in the Gutun watershed (GTW) were investigated by combined remote sensing analysis, geophysical exploration, and field surveys, and the relationships between the hazards were also explored. According to the achieved results, increased soil erosion with an average doubling from 2018 to 2020 is widely distributed in the GTW. Furthermore, 195 areas containing clear evidence of salt precipitates were observed in some of the newly created arable lands, especially downstream of the dam. This was mainly attributed to the high water table, evaporation, and soluble salt concentration of the loess. Fifty-nine newly built silt dams, primarily located in the branch channels and at the gully mouth of the Gutun channel, broke in 2020. The osmotic damage and softening caused by the combined effect of the incomplete compaction of the dam body and concentrated heavy rainfall were the main reasons of the dam breaks. The different types of disasters in the GTW after the implementation of the GLCP show a strong spatial relationship that follows the surface water flow path and forms a disaster chain consisting of slope erosion, silt dam breaks, and the soil salinization of near-dam farmlands downstream. This disaster chain amplifies disaster risks and losses. These findings can guide the improvement of the GLCP and inform geohazard mitigation strategies in areas impacted by the GLCP. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

29 pages, 7077 KiB  
Article
Prediction Interval Estimation of Landslide Displacement Using Bootstrap, Variational Mode Decomposition, and Long and Short-Term Time-Series Network
by Dongxin Bai, Guangyin Lu, Ziqiang Zhu, Xudong Zhu, Chuanyi Tao, Ji Fang and Yani Li
Remote Sens. 2022, 14(22), 5808; https://doi.org/10.3390/rs14225808 - 17 Nov 2022
Cited by 6 | Viewed by 1458
Abstract
Using multi-source monitoring data to model and predict the displacement behavior of landslides is of great significance for the judgment and decision-making of future landslide risks. This research proposes a landslide displacement prediction model that combines Variational Mode Decomposition (VMD) and the Long [...] Read more.
Using multi-source monitoring data to model and predict the displacement behavior of landslides is of great significance for the judgment and decision-making of future landslide risks. This research proposes a landslide displacement prediction model that combines Variational Mode Decomposition (VMD) and the Long and Short-Term Time-Series Network (LSTNet). The bootstrap algorithm is then used to estimate the Prediction Intervals (PIs) to quantify the uncertainty of the proposed model. First, the cumulative displacements are decomposed into trend displacement, periodic displacement, and random displacement using the VMD with the minimum sample entropy constraint. The feature factors are also decomposed into high-frequency components and low-frequency components. Second, this study uses an improved polynomial function fitting method combining the time window and threshold to predict trend displacement and uses feature factors obtained by grey relational analysis to train the LSTNet networks and predict periodic and random displacements. Finally, the predicted trend, periodic, and random displacement are summed to the predicted cumulative displacement, while the bootstrap algorithm is used to evaluate the PIs of the proposed model at different confidence levels. The proposed model was verified and evaluated by the case of the Baishuihe landslide in the Three Gorges reservoir area of China. The case results show that the proposed model has better point prediction accuracy than the three baseline models of LSSVR, BP, and LSTM, and the reliability and quality of the PIs constructed at 90%, 95%, and 99% confidence levels are also better than those of the baseline models. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

20 pages, 23251 KiB  
Article
Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China
by Cong Zhao, Jingtao Liang, Su Zhang, Jihong Dong, Shengwu Yan, Lei Yang, Bin Liu, Xiaobo Ma and Weile Li
Remote Sens. 2022, 14(19), 5031; https://doi.org/10.3390/rs14195031 - 9 Oct 2022
Cited by 3 | Viewed by 1417
Abstract
In the process of using InSAR technology to identify active landslides, phenomena such as steep terrain, dense vegetation, and complex clouds may lead to the missed identification of some landslides. In this paper, an active landslide identification method combining InSAR technology and optical [...] Read more.
In the process of using InSAR technology to identify active landslides, phenomena such as steep terrain, dense vegetation, and complex clouds may lead to the missed identification of some landslides. In this paper, an active landslide identification method combining InSAR technology and optical satellite remote sensing technology is proposed, and the method is successfully applied to the Three Parallel Rivers Region (TPRR) in the northwest of Yunnan Province, China. The results show that there are 442 landslides identified in the TPRR, and the fault zone is one of the important factors affecting the distribution of landslides in this region. In addition, 70% of the active landslides are distributed within 1 km on both sides of the fault zone. The larger the scale of the landslide, the closer the relationship between landslides and the fault zone. In this identification method, the overall landslide identification rate based on InSAR technology is 51.36%. The combination of Sentinel-1 and ALOS-2 data is beneficial to improve the active landslide identification rate of InSAR. In the northern region with large undulating terrain, shadows and overlaps occur easily. The southern area with gentle terrain is prone to the phenomenon where the scale of landslides is too small. Both phenomena are not conducive to the application of InSAR. Thus, in the central region, with moderate terrain and slope, the identification rate of active landslides based on InSAR is highest. The active landslide identification method proposed in this paper, which combines InSAR and optical satellite remote sensing technology, can integrate the respective advantages of the two technical methods, complement each other’s limitations and deficiencies, reduce the missed identification of landslides, and improve the accuracy of active landslide inventory maps. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

25 pages, 11902 KiB  
Article
A Universal Landslide Detection Method in Optical Remote Sensing Images Based on Improved YOLOX
by Heyi Hou, Mingxia Chen, Yongbo Tie and Weile Li
Remote Sens. 2022, 14(19), 4939; https://doi.org/10.3390/rs14194939 - 3 Oct 2022
Cited by 21 | Viewed by 3018
Abstract
Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX [...] Read more.
Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX (You Only Look Once) target detection model to address the poor detection of complex mixed landslides. Wherein the VariFocal is used to replace the binary cross entropy in the original classification loss function to solve the uneven distribution of landslide samples and improve the detection recall; the coordinate attention (CA) mechanism is added to enhance the detection accuracy. Firstly, 1200 historical landslide optical remote sensing images in thirty-eight areas of China were extracted from Google Earth to create a mixed sample set for landslide detection. Next, the three attention mechanisms were compared to form the YOLOX-Pro model. Then, we tested the performance of YOLOX-Pro by comparing it with four models: YOLOX, YOLOv5, Faster R-CNN, and Single Shot MultiBox Detector (SSD). The results show that the YOLOX-Pro(m) has significantly improved the detection accuracy of complex and small landslides than the other models, with an average precision (AP0.75) of 51.5%, APsmall of 36.50%, and ARsmall of 49.50%. In addition, optical remote sensing images of a 12.32 km2 group-occurring landslides area located in Mibei village, Longchuan County, Guangdong, China, and 750 Unmanned Aerial Vehicle (UAV) images collected from the Internet were also used for landslide detection. The research results proved that the proposed method has strong generalization and good detection performance for many types of landslides, which provide a technical reference for the broad application of landslide detection using UAV. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

18 pages, 20333 KiB  
Article
Calculating Co-Seismic Three-Dimensional Displacements from InSAR Observations with the Dislocation Model-Based Displacement Direction Constraint: Application to the 23 July 2020 Mw6.3 Nima Earthquake, China
by Jun Hu, Jianwen Shi, Jihong Liu, Wanji Zheng and Kang Zhu
Remote Sens. 2022, 14(18), 4481; https://doi.org/10.3390/rs14184481 - 8 Sep 2022
Cited by 2 | Viewed by 1660
Abstract
As one of the most prevailing geodetic tools, the interferometric synthetic aperture radar (InSAR) technique can accurately obtain co-seismic displacements, but is limited to the one-dimensional line-of-sight (LOS) measurement. It is therefore difficult to completely reveal the real three-dimensional (3D) surface displacements with [...] Read more.
As one of the most prevailing geodetic tools, the interferometric synthetic aperture radar (InSAR) technique can accurately obtain co-seismic displacements, but is limited to the one-dimensional line-of-sight (LOS) measurement. It is therefore difficult to completely reveal the real three-dimensional (3D) surface displacements with InSAR. By employing azimuth displacement observations from pixel offset tracking (POT) and multiple aperture InSAR (MAI) techniques, 3D displacements of large-magnitude earthquakes can be obtained by integrating the ascending and descending data. However, this method cannot be used to accurately realize the 3D surface displacement measurements of small-magnitude earthquakes due to the low accuracies of the POT/MAI-derived azimuth displacement measurements. In this paper, an alternative method is proposed to calculate co-seismic 3D displacements from ascending and descending InSAR-LOS observations with the dislocation model-based displacement direction constraint. The main contribution lies in the two virtual observation equations that are obtained from the dislocation model-based forward-modeling 3D displacements, which are then combined with the ascending/descending InSAR observations to calculate the 3D displacements. The basis of the two virtual observation equations is that the directions of the 3D displacement vectors are very similar for real and model-based 3D displacements. In addition, the weighted least squares (WLS) method is employed to solve the final 3D displacements, which aims to consider and balance the possible errors in the InSAR observations as well as the dislocation model-based displacement direction constraint. A simulation experiment demonstrates that the proposed method can achieve more accurate 3D displacements compared with the existing methods. The co-seismic 3D displacements of the 2020 Nima earthquake are then accurately obtained by the proposed method. The results show that co-seismic displacements are dominated by the vertical displacement, the magnitude of the horizontal displacement is relatively small, and the overall displacement pattern fits well with the tensile rupture. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

21 pages, 21990 KiB  
Article
Integration of Vertical and Horizontal Deformation Derived by SAR Observation for Identifying Landslide Motion Patterns in a Basaltic Weathered Crust Region of Guizhou, China
by Yifei Zhu, Xin Yao, Chuangchuang Yao, Zhenkai Zhou, Zhenkui Gu and Leihua Yao
Remote Sens. 2022, 14(16), 4014; https://doi.org/10.3390/rs14164014 - 18 Aug 2022
Cited by 1 | Viewed by 1649
Abstract
In recent years, due to adverse geological conditions, intense human engineering activities, and extreme weather conditions, catastrophic landslides have frequently occurred in southwest China, causing severe loss of life and property. Identifying the kinematic features of potential landslides can effectively support landslide hazard [...] Read more.
In recent years, due to adverse geological conditions, intense human engineering activities, and extreme weather conditions, catastrophic landslides have frequently occurred in southwest China, causing severe loss of life and property. Identifying the kinematic features of potential landslides can effectively support landslide hazard prevention. This study proposes a remote sensing identification method for rotational, planar traction, and planar thrust slides based on geomorphic features as well as vertical and slope-oriented deformation rates. Rotational landslides are characterized by similar vertical and horizontal deformation rates, with vertical deformation mainly occurring at the head and gradually decreasing along the slope, while horizontal deformation mainly occurs at the foot and gradually increases along the slope. As for the planar slide, the dominant deformation is in the horizontal direction. It is further classified into the planar traction and planar thrust types according to the driving position. The vertical deformation of planar traction slides is concentrated at the foot, while the vertical deformation of planar thrust slides is concentrated at the head of the landslide. We identified 1 rotational landslide, 10 planar traction landslides and 10 planar thrust landslides in the basalt weathering crust area of Guizhou. Field investigations of three landslides verified the method’s accuracy. Combining two-dimensional rainfall and time-series deformations, we found that there is a significant positive correlation between landslide deformation acceleration and precipitation. The landslide kinematic identification method proposed in this paper overcomes the shortcomings of the inability to accurately characterize landslide motion by line-of-sight displacement and realizes the non-contact identification of active landslide motion patterns, which is an essential reference value for geological disaster prevention and control in the study area. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

17 pages, 9799 KiB  
Article
Distribution and Mobility of Coseismic Landslides Triggered by the 2018 Hokkaido Earthquake in Japan
by Jiayan Lu, Weile Li, Weiwei Zhan and Yongbo Tie
Remote Sens. 2022, 14(16), 3957; https://doi.org/10.3390/rs14163957 - 15 Aug 2022
Cited by 4 | Viewed by 1605
Abstract
At 3:08 on 6 September 2018 (UTC +9), massive landslides were triggered by an earthquake of Mw 6.6 that occurred in Hokkaido, Japan. In this paper, a coseismic landslide inventory that covers 388 km2 of the earthquake-impacted area and includes 5828 coseismic [...] Read more.
At 3:08 on 6 September 2018 (UTC +9), massive landslides were triggered by an earthquake of Mw 6.6 that occurred in Hokkaido, Japan. In this paper, a coseismic landslide inventory that covers 388 km2 of the earthquake-impacted area and includes 5828 coseismic landslides with a total landslide area of 23.66 km2 was compiled by using visual interpretations of various high-resolution satellite images. To analyze the spatial distribution and characteristics of coseismic landslides, five factors were considered: the peak ground acceleration (PGA), elevation, slope gradient, slope aspect, and lithology. Results show more than 87% of the landslides occurred at 100 to 200 m elevations. Slopes in the range of 10~20°are the most susceptible to failure. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16 g is obviously larger than those with PGA less than 0.02 g. Compared with the number and scale of coseismic landslides caused by other strong earthquakes and the mobility of the coseismic landslides caused by the Haiyan and Wenchuan earthquakes, it was found that the distribution of coseismic landslides was extremely dense and that the mobility of the Hokkaido earthquake was greater than that of the Wenchuan earthquake and weaker than that of the Haiyuan earthquake, and is described by the following relationship: L = 18.454 ∗ H0.612. Comparative analysis of coseismic landslides with similar magnitude has important guiding significance for disaster prevention and reduction and reconstruction planning of landslides in affected areas. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

22 pages, 5138 KiB  
Article
Using Electrical Resistivity Tomography to Monitor the Evolution of Landslides’ Safety Factors under Rainfall: A Feasibility Study Based on Numerical Simulation
by Dongxin Bai, Guangyin Lu, Ziqiang Zhu, Xudong Zhu, Chuanyi Tao and Ji Fang
Remote Sens. 2022, 14(15), 3592; https://doi.org/10.3390/rs14153592 - 27 Jul 2022
Cited by 10 | Viewed by 2041
Abstract
Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as [...] Read more.
Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as stress, strength, etc. Based on ERT monitoring data, a framework for quantitatively and directly evaluating the evolution of the factor of safety (FOS) of landslides during rainfall is proposed. The framework first inverts ERT observation data using the inexact Gauss–Newton method based on multiple constraints to obtain a more realistic resistivity distribution, then calculates the saturation distribution using Archie’s equation, and finally calculates the FOS of landslides using the finite element strength reduction method. Twelve sets of numerical experiments were designed and carried out based on the synthetic data of a theoretical model. The experimental results show that the proposed framework is valid and reliable under various arrays, apparent resistivity noise, and uncertainty in the water-electric correlation curve, with the Dipole-Dipole array outperforming the others in terms of accuracy, sensitivity, and anti-noise capability. The proposed framework is significant in improving ERT monitoring and early warning capabilities for rainfall-induced landslides. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

32 pages, 11540 KiB  
Article
A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China
by Xinyue Yuan, Chao Liu, Ruihua Nie, Zhengli Yang, Weile Li, Xiaoai Dai, Junying Cheng, Junmin Zhang, Lei Ma, Xiao Fu, Min Tang, Yina Xu and Heng Lu
Remote Sens. 2022, 14(14), 3259; https://doi.org/10.3390/rs14143259 - 6 Jul 2022
Cited by 21 | Viewed by 2226
Abstract
After the “5·12” Wenchuan earthquake in 2008, collapses and landslides have occurred continuously, resulting in the accumulation of a large quantity of loose sediment on slopes or in gullies, providing rich material source reserves for the occurrence of debris flow and flash flood [...] Read more.
After the “5·12” Wenchuan earthquake in 2008, collapses and landslides have occurred continuously, resulting in the accumulation of a large quantity of loose sediment on slopes or in gullies, providing rich material source reserves for the occurrence of debris flow and flash flood disasters. Therefore, it is of great significance to build a collapse and landslide susceptibility evaluation model in Wenchuan County for local disaster prevention and mitigation. Taking Wenchuan County as the research object and according to the data of 1081 historical collapse and landslide disaster points, as well as the natural environment, this paper first selects six categories of environmental factors (13 environmental factors in total) including topography (slope, aspect, curvature, terrain relief, TWI), geological structure (lithology, soil type, distance to fault), meteorology and hydrology (rainfall, distance to river), seismic impact (PGA), ecological impact (NDVI), and impact of human activity (land use). It then builds three single models (LR, SVM, RF) and three CF-based hybrid models (CF-LR, CF-SVM, CF-RF), and makes a comparative analysis of the accuracy and reliability of the models, thereby obtaining the optimal model in the research area. Finally, this study discusses the contribution of environmental factors to the collapse and the landslide susceptibility prediction of the optimal model. The research results show that (1) the areas prone to extremely high collapse and landslide predicted by the six models (LR, CF-LR, SVM, CF-SVM, RF and CF-RF) have an area of 730.595 km2, 377.521 km2, 361.772 km2, 372.979 km2, 318.631 km2, and 306.51 km2, respectively, and the frequency ratio precision of collapses and landslides is 0.916, 0.938, 0.955, 0.956, 0.972, and 0.984, respectively; (2) the ranking of the comprehensive index based on the confusion matrix is CF-RF>RF>CF-SVM>CF-LR>SVM>LR and the ranking of the AUC value is CF-RF>RF>CF-SVM>CF-LR>SVM>LR. To a certain extent, the coupling models can improve precision more over the single models. The CF-RF model ranks the highest in all indexes, with a POA value of 257.046 and an AUC value of 0.946; (3) rainfall, soil type, and distance to river are the three most important environmental factors, accounting for 24.216%, 22.309%, and 11.41%, respectively. Therefore, it is necessary to strengthen the monitoring of mountains and rock masses close to rivers in case of rainstorms in Wenchuan county and other similar areas prone to post-earthquake landslides. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Figure 1

20 pages, 12762 KiB  
Article
3D Rock Structure Digital Characterization Using Airborne LiDAR and Unmanned Aerial Vehicle Techniques for Stability Analysis of a Blocky Rock Mass Slope
by Qiang Xu, Zhen Ye, Qian Liu, Xiujun Dong, Weile Li, Shanao Fang and Chen Guo
Remote Sens. 2022, 14(13), 3044; https://doi.org/10.3390/rs14133044 - 24 Jun 2022
Cited by 6 | Viewed by 2431
Abstract
Airborne light detection and ranging (LiDAR) and unmanned aerial vehicle-structure from motion (UAV-SfM) provide point clouds with unprecedented resolution and accuracy that are well suited for the digital characterization of rock outcrops where direct contact measurements cannot be obtained due to terrain or [...] Read more.
Airborne light detection and ranging (LiDAR) and unmanned aerial vehicle-structure from motion (UAV-SfM) provide point clouds with unprecedented resolution and accuracy that are well suited for the digital characterization of rock outcrops where direct contact measurements cannot be obtained due to terrain or safety constraints. Today, however, how to better apply these techniques to the practice of geostructural analysis is a topic of research that must be further explored. This study presents a processing procedure for extracting three-dimensional (3D) rock structure parameters directly from point clouds using open-source software and a three-dimensional distinct element code-assisted (3DEC) simulation of slope failure based on carbonate rock cliffs in the Jiuzhaigou Scenic Area. The procedure involves (1) processing point clouds obtained with different remote sensing techniques; (2) using the Hough transform to estimate normals for the hue, saturation, and value (HSV) rendering of unstructured point clouds; (3) automatically clustering and extracting the set-based point clouds; (4) estimating set-based geometric parameters; and (5) performing a subsequent stability analysis based on rock structure parameters. The results show that integrating different remote sensing techniques and rock structure computing can provide a quick way for slope engineers to assess the safety of blocky rock masses. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

19 pages, 46747 KiB  
Article
Characterizing Spatiotemporal Patterns of Land Deformation in the Santa Ana Basin, Los Angeles, from InSAR Time Series and Independent Component Analysis
by Kang Zhu, Xing Zhang, Qian Sun, Hai Wang and Jun Hu
Remote Sens. 2022, 14(11), 2624; https://doi.org/10.3390/rs14112624 - 31 May 2022
Cited by 8 | Viewed by 2377
Abstract
The excessive extraction and recharge of groundwater lead to long-time seasonal land subsidence in Los Angeles, USA, and especially in the Santa Ana basin. The rate of land subsidence in the Santa Ana basin has been rising, which could pose a danger to [...] Read more.
The excessive extraction and recharge of groundwater lead to long-time seasonal land subsidence in Los Angeles, USA, and especially in the Santa Ana basin. The rate of land subsidence in the Santa Ana basin has been rising, which could pose a danger to infrastructure and human lives. However, the most recent research on land surface deformation in the area was conducted using the traditional parameter estimation method, resulting in little understanding of the regional spatiotemporal characteristics. The parametric method consists of a least square linear inversion, using the pre-defined mathematical geometric or geophysical theoretical models to describe groundwater deformation, and it requires precise external environmental variables and accurate geophysical parameters, which are more difficult to implement. In this study, multitemporal InSAR-derived deformation time series are analyzed by using 69 descending C-band Sentinel-1A SAR scenes acquired from 2015 to 2018. A method based on independent component analysis (ICA) is applied to characterize the spatial pattern and temporal evolution of land subsidence in the Santa Ana basin. The results reveal two different spatial and temporal deformation patterns in the basin. First, a widespread seasonal deformation is identified by the first component, related to annual seasonal groundwater level changes, and the overall deformation shows a concentrated spatial pattern. The second component captures a long-term signal with a large-scale spatial pattern. For quantitative assessment, the obtained deformation time series are compared with the GNSS data, validating an accuracy of millimeters. We further calculate the cross-correlation coefficient and the elastic skeletal storage coefficient from the ICA-derived seasonal deformation and groundwater level, which reveals that the deformation responds quickly (i.e., a lag of 8 days) to the change in groundwater and the Santa Ana aquifer retains almost the same elasticity for at least 15 years. Quantifying the spatiotemporal characteristics of the deformation in the Santa Ana basin can provide a reference for the monitoring and managing of groundwater. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

16 pages, 12527 KiB  
Technical Note
Analysis of Seismic Impact on Hailuogou Glacier after the 2022 Luding Ms 6.8 Earthquake, China, Using SAR Offset Tracking Technology
by Weile Li, Junyi Chen, Huiyan Lu, Congwei Yu, Yunfeng Shan, Zhigang Li, Xiujun Dong and Qiang Xu
Remote Sens. 2023, 15(5), 1468; https://doi.org/10.3390/rs15051468 - 6 Mar 2023
Cited by 3 | Viewed by 1694
Abstract
An Ms 6.8 earthquake struck Luding County, Ganzi Prefecture, Sichuan Province on 5 September 2022, with the epicenter about 10 km away from Hailuogou Glacier. How Hailuogou Glacier was affected by the earthquake was of major concern to society. Sentinel-1 SAR satellite imaging [...] Read more.
An Ms 6.8 earthquake struck Luding County, Ganzi Prefecture, Sichuan Province on 5 September 2022, with the epicenter about 10 km away from Hailuogou Glacier. How Hailuogou Glacier was affected by the earthquake was of major concern to society. Sentinel-1 SAR satellite imaging was used to monitor the glacier surface velocity during different periods before and after the Luding earthquake based on pixel offset tracking (POT) technology, which applies a feature-tracking algorithm to overcome the phase co-registration problems commonly encountered in large displacement monitoring. The results indicated that the velocity had a positive correlation with the average daily maximum temperature and the slope gradient on the small-slope surfaces. The correlation was not apparent on the steeper surfaces, which corresponded spatially with the identified ice avalanche region in the Planet images. It was deduced that this may be because of the occurrence of ice avalanches on surfaces steeper than 25°, or that the narrower front channel impeded the glacier’s movement. The Luding earthquake did not cause a significant increase in the velocity of Hailuogou Glacier within a large range, but it disturbed the front area of the ice cascade, where the maximum velocity reached 2.5 m/d. Although the possibility of directly-induced destruction by ice avalanches after the earthquake was low, and the buffering in the downstream glacier tongue further reduced the risk of ice avalanches, the risk of some secondary hazards such as debris flow increased. The proposed method in this study might be the most efficient in monitoring and evaluating the effects of strong earthquakes on glaciers because it would not be limited by undesirable weather or traffic blockage. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

17 pages, 5495 KiB  
Technical Note
Evaluating and Analyzing the Potential of the Gaofen-3 SAR Satellite for Landslide Monitoring
by Ningling Wen, Fanru Zeng, Keren Dai, Tao Li, Xi Zhang, Saied Pirasteh, Chen Liu and Qiang Xu
Remote Sens. 2022, 14(17), 4425; https://doi.org/10.3390/rs14174425 - 5 Sep 2022
Cited by 8 | Viewed by 1734
Abstract
Gaofen-3 is the first Chinese spaceborne C-band SAR satellite with multiple polarizations. The Gaofen-3 satellite’s data has few applications for monitoring landslides at present, and its potential for use requires further investigation. Consequently, we must evaluate and analyze the landslide interference quality and [...] Read more.
Gaofen-3 is the first Chinese spaceborne C-band SAR satellite with multiple polarizations. The Gaofen-3 satellite’s data has few applications for monitoring landslides at present, and its potential for use requires further investigation. Consequently, we must evaluate and analyze the landslide interference quality and displacement monitoring derived from the Gaofen-3 SAR satellite’s data, particularly in high and steep, mountainous regions. Based on the nine Gaofen-3 SAR datasets gathered in 2020–2021, this study used DInSAR technology to track landslide displacement in Mao County, Sichuan Province, utilizing data from Gaofen-3. Our findings were compared to SENTINEL-1 and ALOS-2 data for the same region. This study revealed that due to its large spatial baseline, Gaofen-3’s SAR data have a smaller interference effect and weaker coherence than the SENTINEL-1 and ALOS-2 SAR data. In addition, the displacement sensitivity of the Gaofen-3 and SENTINEL-1 data (C-band) is higher than that of the ALOS-2 data (L-band). Further, we conducted a study of observation applicability based on the geometric distortion distribution of the three forms of SAR data. Gaofen-3’s SAR data are very simple to make layover and have fewer shadow areas in hilly regions, and it theoretically has more suitable observation areas (71.3%). For its practical application in mountainous areas, we introduced the passive geometric distortion analysis method. Due to its short incidence angle (i.e., 25.8°), which is less than the other two satellites’ SAR data, only 39.6% of the Gaofen-3 SAR data in the study area is acceptable for suitable observation areas. This study evaluated and analyzed the ability of using Gaofen-3’s data to monitor landslides in mountainous regions based on the interference effect and observation applicability analysis, thereby providing a significant reference for the future use and design of Gaofen-3’s data for landslide monitoring. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

15 pages, 33382 KiB  
Technical Note
Insights into the Landslides Triggered by the 2022 Lushan Ms 6.1 Earthquake: Spatial Distribution and Controls
by Bo Zhao, Weile Li, Lijun Su, Yunsheng Wang and Haochen Wu
Remote Sens. 2022, 14(17), 4365; https://doi.org/10.3390/rs14174365 - 2 Sep 2022
Cited by 11 | Viewed by 2168
Abstract
On 1 June 2022, a magnitude Ms 6.1 (Mw 5.8) earthquake, named the 2022 Lushan earthquake, struck the southern segment of the Longmenshan fault zone, with an epicenter at 30.395°N, 102.958°E and a focal depth of approximately 12.0 km. To gain insight into [...] Read more.
On 1 June 2022, a magnitude Ms 6.1 (Mw 5.8) earthquake, named the 2022 Lushan earthquake, struck the southern segment of the Longmenshan fault zone, with an epicenter at 30.395°N, 102.958°E and a focal depth of approximately 12.0 km. To gain insight into the landslides triggered by this event and the characteristics of coseismic landslides in the Longmenshan fault zone, we collected multitemporal satellite images and carried out field investigations. The results reveal that the 2022 Lushan event triggered at least 1288 landslides over an affected area of 1470 km2. The total landslide area is 5.33 km2, and the highest landslide concentration reaches 22.3 landslides/km2. The landslide distribution has a hanging wall effect, and the right bank area of the Qingyi River, featuring deep-cutting gorges, is part of an area with obvious concentrated landslides; this area consists mainly of intrusive rocks, including granite, gabbro and hornblende. The coseismic landslides in the Longmenshan fault zone have hanging wall effects, and the landslides triggered by the 2022 Lushan event are distributed in higher and steeper areas. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-17
Back to TopTop