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

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 16855

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Special Issue Editors

Prof. Dr. Yi Wang

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Guest Editor

Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing image processing; deep learning; geological data intelligent learning
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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
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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, multipolarization, high spatial, and temporal resolution sensors are becoming more and more abundant, and the data volume is increasing exponentially, 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 significant advantage as Earth surface changes and deformations often occur in remote areas and can be potentially dangerous or difficult to access. Today, remote sensing data play a big role in geosciences. With recent advancements in technologies such as UAVs, multiband high-resolution satellite images, and multipolarization microwave-based SAR images coupled with 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 earthquakes and landslides, and new processing techniques related to remote sensing. We invite you to submit scientific, technological, or review articles on 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.

This is the Second Edition of the Special Issue, and experts and scholars in related fields are welcome to submit their original works to this Special Issue: Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide.

Prof. Dr. Yi Wang
Prof. Dr. Jun Hu
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

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Related Special Issue

Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide (Third Edition) in Remote Sensing (3 articles)

Published Papers (8 papers)

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Research

28 pages, 24166 KiB

Open AccessEditor’s ChoiceArticle

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

by Adel Asadi, Laurie Gaskins Baise, Christina Sanon, Magaly Koch, Snehamoy Chatterjee and Babak Moaveni

Remote Sens. 2023, 15(19), 4883; https://doi.org/10.3390/rs15194883 - 9 Oct 2023

Cited by 5 | Viewed by 2312

Abstract

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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17 pages, 33267 KiB

Open AccessArticle

Slow Slip Events Associated with Seismic Activity in the Hikurangi Subduction Zone, New Zealand, from 2019 to 2022

by Li Yan, Yanling Sun, Meng Li, Ahmed El-Mowafy and Lei Ma

Remote Sens. 2023, 15(19), 4767; https://doi.org/10.3390/rs15194767 - 29 Sep 2023

Viewed by 1926

Abstract

Slow slip events (SSEs) are geophysical phenomena primarily occurring in subduction zones. These events are often associated with seismic activity and can be detected by Global Positioning System (GPS). However, the relationship between SSEs and seismic activity remains unclear. To further investigate SSEs associated with seismic activity, we conducted SSE detection and inversion for the period from 2019 to 2022 on New Zealand’s North Island, where both SSEs and seismic activity frequently occur. By modeling daily GPS coordinate time series from 40 GPS stations and applying the Network Inversion Filter (NIF) method, we obtain surface displacements, cumulative slips, and slip rates for eight shallow SSEs. Subsequently, we conduct a statistical analysis of seismic activity concerning its spatial distribution and frequency before, during, and after SSE occurrences. The results indicate that SSE1 and SSE7 exhibited larger cumulative slips, at 14.35 and 7.20 cm, and surface displacements, at 4.97 and 2.53 cm, respectively. During their occurrences, the seismic frequency noticeably increased to 6.5 and 5.6 events per day in the Eastern Coastal Region (ECR) of New Zealand’s North Island. However, the other six SSEs, characterized by cumulative slips of less than 6 cm and maximum surface displacements of less than 2 cm, did not lead to a noticeable increase in seismic frequency during their occurrences in the ECR. In the Main Slip Regions (MSR) of these eight SSEs, a significant upward trend in seismic frequency was observed during their occurrences. Therefore, it can be inferred that in the ECR of New Zealand’s North Island, all SSEs result in an increased seismic frequency within their respective MSRs, but only significant SSEs impact the seismic frequency of the ECR. Monitoring shallow SSEs may contribute to the identification and recording of seismic activity. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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17 pages, 12382 KiB

Open AccessArticle

Analysis of Mass Wasting Processes in the Slumgullion Landslide Using Multi-Track Time-Series UAVSAR Images

by Jiehua Cai, Changcheng Wang and Lu Zhang

Remote Sens. 2023, 15(19), 4746; https://doi.org/10.3390/rs15194746 - 28 Sep 2023

Cited by 1 | Viewed by 1263

Abstract

The Slumgullion landslide is a large translational debris slide whose currently active part has likely been sliding for approximately 300 years. Its permanent motion and evolutionary processes have attracted the attention of many researchers. In order to study its mass wasting processes and evolution trend, the spatial–temporal displacement of the Slumgullion landslide was retrieved using an adaptive pixel offset tracking (POT) method with multi-track Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images. Based on three-dimensional displacement and slope information, we then revealed the spatial–temporal distribution of surface mass depletion or accumulation in the landslide, which provides a new perspective to analyze the evolutionary processes of landslides. The results indicate that the Slumgullion landslide had a spatially variable displacement, with a maximum displacement of 35 m. The novel findings of this study mainly include two parts. First, we found that the surface mass accumulated in the toe of the landslide and depleted in the top and middle area during the interval, which could increase the resisting force and decrease the driving force of the Slumgullion landslide. This result is compelling evidence which indicates the Slumgullion landslide should eventually tend to be stable. Second, we found that the distribution of geological structures can well explain some of the unique mass wasting in the Slumgullion landslide. The larger local mass depletion in the landslide neck area verifies that the sharp velocity increase in this region is not only caused by the reduction in width but is also significantly affected by the local normal faults. In summary, this study provides an insight into the relation between the landslide motion, mass volume change, and geological structure. The results demonstrate the great potential of multi-track airborne SAR for displacement monitoring and evolutionary analysis of landslides. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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26 pages, 22810 KiB

Open AccessArticle

Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms

by Mahyat Shafapourtehrany, Fatemeh Rezaie, Changhyun Jun, Essam Heggy, Sayed M. Bateni, Mahdi Panahi, Haluk Özener, Farzin Shabani and Hamidreza Moeini

Remote Sens. 2023, 15(18), 4501; https://doi.org/10.3390/rs15184501 - 13 Sep 2023

Cited by 7 | Viewed by 2116

Abstract

Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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16 pages, 20425 KiB

Open AccessArticle

The Stability Analysis of Mt. Gongga Glaciers Affected by the 2022 Luding M_S 6.8 Earthquake Based on LuTan-1 and Sentinel-1 Data

by Hao Li, Bingquan Li, Yongsheng Li and Huizhi Duan

Remote Sens. 2023, 15(15), 3882; https://doi.org/10.3390/rs15153882 - 5 Aug 2023

Cited by 1 | Viewed by 1601

Abstract

On 5 September 2022, an M_S 6.8 earthquake occurred in Luding county, Sichuan province, China, with the epicenter located approximately 20 km from the main peak of Mount (Mt.) Gongga. The dynamic situation of Mt. Gongga glaciers has received widespread attention. In this study, Mt. Gongga was selected as the study area, and L-band LuTan-1 (LT-1) satellite data were used for differential interferometric synthetic aperture radar (D-InSAR) processing to obtain the coseismic landform in Luding. Based on Sentinel-1A images, pixel offset tracking (POT) technology was used to obtain the surface movement velocities of the glaciers before, during, and after the earthquake. The results showed that the overall preseismic movement of the glaciers was fast in the area where the ice cascade of the Hailuogou Glacier reached a maximum average deformation rate of 0.94 m/d. Moreover, time-series monitoring of the postseismic glaciers showed that the surface flow velocities of some glaciers in the study area increased after the earthquake. The flow velocity at the main peak of Mt. Gongga and the tongue of the Mozigou Glacier accelerated for a period after the earthquake. The study concluded that the earthquake was one of the direct causes of the increase in glacier flow velocity, which returned to a stable state more than 70 days after the earthquake. The relevant monitoring results and research data can provide a reference for earthquake-triggered glacial hazards and indicate the effectiveness of LT-1 in identifying and monitoring geological hazards. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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19 pages, 19951 KiB

Open AccessArticle

Learning a Deep Attention Dilated Residual Convolutional Neural Network for Landslide Susceptibility Mapping in Hanzhong City, Shaanxi Province, China

by Yu Ma, Shenghua Xu, Tao Jiang, Zhuolu Wang, Yong Wang, Mengmeng Liu, Xiaoyan Li and Xinrui Ma

Remote Sens. 2023, 15(13), 3296; https://doi.org/10.3390/rs15133296 - 27 Jun 2023

Cited by 8 | Viewed by 1773

Abstract

The analysis and evaluation of landslide susceptibility are of great significance in preventing and managing geological hazards. Aiming at the problems of insufficient information caused by the limited number of landslide datasets, complex information of landslide evaluation factors, and low prediction accuracy of landslide susceptibility, a landslide susceptibility evaluation method based on the deep attention dilated residual convolutional neural network (DADRCNN) is proposed. First, the dilated convolution unit (DCU) is used to increase the network receptive field, aggregate multi-scale information, and enhance the model ability to capture the characteristics of landslide evaluation factors. Second, the deep residual module (DRM) is used to solve the issue of gradient disappearance and better extract data features by overlaying the residual function mapping layer and increasing the network depth. Finally, the channel attention residual module (CARM) is introduced to learn the varying importance of different landslide evaluation factors, and assign different weights to improve the susceptibility prediction accuracy. The experimental results show that the DADRCNN method can extract features around the sample points, expand the receptive field, and deeply mine the information. It mitigates the lack of sample information in training, focuses on important feature information, and significantly improves the prediction accuracy. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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12 pages, 10708 KiB

Open AccessCommunication

Coseismic Source Model of the February 2023 Mw 6.8 Tajikistan Earthquake from Sentinel-1A InSAR Observations and Its Associated Earthquake Hazard

by Ying Shi, Yongzhe Wang and Yinju Bian

Remote Sens. 2023, 15(12), 3010; https://doi.org/10.3390/rs15123010 - 8 Jun 2023

Cited by 4 | Viewed by 1714

Abstract

On 23 February 2023, an Mw 6.8 earthquake struck the border of Tajikistan and Xinjiang China, the source mechanism remains controversial according to different seismic inversions. To better comprehend the source characteristics and the surface deformation pattern, we used the ascending and descending orbital Sentinel-1A SAR data to obtain the coseismic deformation of this earthquake based on the traditional two-pass differential interferometric synthetic aperture radar (InSAR). The source model is inverted from the InSAR coseismic deformation results. The possible Coulomb Failure Stress (CFS) transfer is analyzed based on the preferred source model. The results illustrate that the earthquake ruptured a blind left-lateral strike-slip fault of strike 28.1° with a maximum slip of 1.53 m and the total geodetic moment is 1.99 × 10¹⁹ N·m (Mw 6.83). The strike direction and the fault characteristics suggest the Seismogenic fault is a secondary fault of the Sarez–Karakul Fault System. The 2015 Mw 7.2 Sarez Earthquake plays a triggering role in the occurrence of the 2023 Tajikistan earthquake. Earthquake hazard on Sarez–Karakul Fault System and Sarez–Murghab Thrust System is enhanced due to the Coulomb stress loaded by the Tajikistan earthquake. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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20 pages, 17062 KiB

Open AccessArticle

Location and Activity Changes of Slow-Moving Landslides Due to an Earthquake: Perspective from InSAR Observations

by Caihong He, Qian Sun, Jun Hu and Rong Gui

Remote Sens. 2023, 15(8), 1977; https://doi.org/10.3390/rs15081977 - 8 Apr 2023

Cited by 1 | Viewed by 2828

Abstract

Strong earthquakes can not only trigger many landslides in a short period of time but can also change the stability of slopes in the earthquake area, causing them to be active for a long time after the earthquake. Research on the variation of slow-motion slopes before and after earthquakes can help us to better understand the mechanism of earthquake-affected landslides, which is also crucial for assessing the long-term landslide risk in seismically active areas. Here, L-band ALOS-2 PALSAR-2 images are utilized with the SBAS-InSAR algorithm to monitor and assess the location and activity changes of slow-moving landslides in the Iburi region (Hokkaido, Japan) before and after an earthquake occurred on 6 September 2018. Unlike previous studies, which focused on single typical landslides, we tracked all the landslides within a 33 × 55 km region close to the epicenter. According to the results, the majority of the co-seismic landslides that quickly failed during the earthquake are now stable, and a few of them are still moving. In contrast, due to near-field seismic shaking, certain slopes that did not show substantial surface changes during the earthquake period continued to move and eventually developed into slow-moving landslides. In addition, it can be seen from the spatial distribution of slow-moving landslides after the earthquake that this distribution is not only dependent on strong earthquake seismic vibration or the hanging-wall effect. Far-field weak vibrations can also accelerate landslides. Additionally, we discovered that the earthquake made the unstable slopes move more quickly but also tended to stabilize the slopes that were already in motion before the earthquake. The various response modes of slow-moving landslides to seismic events are related not only to the intensity of seismic vibration but also to the geological conditions of the region and to the size of the landslide itself. These findings are extremely valuable for studying the mechanism of earthquake-affected landslides. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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