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Remote Sensing
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Application of Remote Sensing Approaches in Geohazard Risk
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Application of Remote Sensing Approaches in Geohazard Risk

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 26 May 2024 | Viewed by 1548

Special Issue Editors

Dr. Chao Zhou

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
Interests: landslide risk analysis; InSAR; Artificial Intelligence; landslide early earning and prediction
Prof. Dr. Kunlong Yin

E-Mail Website
Guest Editor
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Interests: landslide motoring and early warning; landslide risk analysis; reservoir landslide
Dr. Federico Raspini

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4-50121 Firenze, Italy
Interests: landslide; subsidence; risk analysis; monitoring; InSAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Under the influence of global climate change, rapid urban expansion, and drastic human activities, geological hazards, including landslides, debris flow, and subsidence, occur frequently every year around the world. Numerous geological disasters pose a great threat to human life and property safety, especially in less developed regions. Carrying out the risk study of geological disasters is considered as an effective method to reduce the losses.

Accurate observation of the geohazard phenomena from initiation to failure is the premise for risk analysis and prediction. Traditional ground-based monitoring techniques can directly observe various phenomena of geological hazards, but the high cost and sparse spatial distribution limit their application on the regional scale and in fine evaluations. In recent years, remote sensing methods, such as radar interferometry, UAVs, LiDAR, etc., have been widely used. These advanced approaches make significant contributions to various steps of geological disaster risk prevention, including detection, monitoring, and early warning.

The purpose of this Special Issue is to publish studies covering various applications of remote sensing in risk prevention for geohazards. We invite authors to submit research papers and technical notes on topics including but not limited to the following:

  • Advanced remote sensing methods for geohazard observation;
  • Detection and monitoring of geological hazards;
  • Multi-scale risk analysis and mapping.

Dr. Chao Zhou
Prof. Dr. Kunlong Yin
Dr. Federico Raspini
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

  • satellite-, UAV-, and ground-based remote sensing approaches
  • landslides, debris flow, rockfall, subsidence, etc.
  • susceptibility and hazard mapping
  • geohazard detection, monitoring, and early warning
  • risk analysis and prediction
  • mechanisms of geohazard
  • Artificial Intelligence
 

Published Papers (3 papers)

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Research

15 pages, 14256 KiB  
Article
Formative Period Tracing and Driving Factors Analysis of the Lashagou Landslide Group in Jishishan County, China
by Qianyou Fan, Shuangcheng Zhang, Yufen Niu, Jinzhao Si, Xuhao Li, Wenhui Wu, Xiaolong Zeng and Jianwen Jiang
Remote Sens. 2024, 16(10), 1739; https://doi.org/10.3390/rs16101739 - 14 May 2024
Viewed by 159
Abstract
The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, [...] Read more.
The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. Full article
(This article belongs to the Special Issue Application of Remote Sensing Approaches in Geohazard Risk)
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20 pages, 12238 KiB  
Article
Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation
by Xiangjie She, Deying Li, Shuo Yang, Xiaoxu Xie, Yiqing Sun and Wenjie Zhao
Remote Sens. 2024, 16(9), 1587; https://doi.org/10.3390/rs16091587 - 29 Apr 2024
Viewed by 447
Abstract
The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in [...] Read more.
The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control. Full article
(This article belongs to the Special Issue Application of Remote Sensing Approaches in Geohazard Risk)
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23 pages, 20613 KiB  
Article
Insights into Deformation and Mechanism of a Reactivated Landslide Occurrence from Multi-Source Data: A Case Study in Li County, China
by Yingjin Du, Kun He, Xiewen Hu and Hongsheng Ma
Remote Sens. 2024, 16(8), 1317; https://doi.org/10.3390/rs16081317 - 9 Apr 2024
Viewed by 504
Abstract
The investigation of reactivated landslides in the alpine-canyon areas suffers the difficult accessibility of precipitous terrain. In particular, when reactivated landslides occur along the major roads, efforts are focused on measuring ground surface displacements during road construction. Nevertheless, the ancient landslide deposits may [...] Read more.
The investigation of reactivated landslides in the alpine-canyon areas suffers the difficult accessibility of precipitous terrain. In particular, when reactivated landslides occur along the major roads, efforts are focused on measuring ground surface displacements during road construction. Nevertheless, the ancient landslide deposits may reactivate after several years of road operation, while they show a stable state during the road construction. The characterization of this type of reactivated landslides is challenging, due to their complex mechanism and the limited monitoring data. Appropriate multi-source data can provide insights into deformation fields and enhance the understanding of landslide mechanisms, ensuring the outperformance of remedial works. This paper reports a recent Tangjiawan reactivated landslide along the Wenchuan-Maerkang Highway in Li County, China. The outcomes, including satellite InSAR, in situ real-time monitoring, and detailed ground and UAV investigation, conducted at this landslide are presented. Early deformation of the reactivated landslide began from 2019, with an InSAR-derived velocity of −11.7 mm/year, furthermore, a significant subsidence of about 21.2 mm, which occurred within a span of only 12 days from 3 June 2020 to 15 June 2020, was observed. The deformation characteristics derived from in situ monitoring during the remedial works were likely firstly associated with the initial unreinforced slope condition and the heavy rainfall. Subsequently, the displacement evolution transformed into deformation induced by time-dependent reduction in slope strength under rainfall conditions. The existing of unconsolidated deposits derived from ancient landslides, along with a fragile geo-structure consisting of rock blocks and gravels interlayered with breccias, exacerbated by large relief created a predisposition for landslide reactivation. Furthermore, 13 days of antecedent cumulative rainfall totaling 224.5 mm directly triggered the occurrence of a landslide event. The significance and implications of integrating multiple monitoring techniques are emphasized. Full article
(This article belongs to the Special Issue Application of Remote Sensing Approaches in Geohazard Risk)
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