Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Remote Sensing for Geohazards Monitoring: Towards Refined Risk Analysis and Management

Special Issue Editors
Special Issue Information
Published Papers

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 (25 March 2024) | Viewed by 5092

Share This Special Issue

Special Issue Editors

Dr. Faming Huang

E-Mail Website
Guest Editor

School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
Interests: failure mechanism analysis of engineering and natural hazards; slope stability and reliability analysis; landslide susceptibility, hazard and risk mapping; machine learning and numerical simulation in slope engineering; remote sensing and geographic information system
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jinsong Huang

E-Mail Website
Guest Editor

ARC Centre of Excellence for Geotechnical Science and Engineering, University of Newcastle, Callaghan, NSW, Australia
Interests: risk assessment in geotechnical engineering; computational geomechanics; modelling of spatial variability; stress integration techniques for elastoplastic models; contact dynamics of granular media; analysis of hydraulic fracturing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Filippo Catani

E-Mail Website
Guest Editor

Department of Geosciences, University of Padova, Via Gradenigo, 35131 Padova, Italy
Interests: landslide hazard; monitoring and modelling of basin scale surface processes; natural hazards; applications of remote sensing to landslide studies; oil & gas environmental impact and risk; surface monitoring in open pit mines; scaling processes in geomorphology; machine learning applied to land surface processes
Special Issues, Collections and Topics in MDPI journals

Dr. Shuihua Jiang

E-Mail Website
Guest Editor

School of Infrastructure Engineering, Nanchang University, Xuefu Road 999, Nanchang 330031, China
Interests: modelling of spatial variability of geomaterials; geotechnical reliability and risk assessment; bayesian inverse analysis and reliability updating; probabilistic site characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Humans have always striven to predict and understand risk. The ability to make better predictions has offered refined risk analysis and management in diverse contexts (such as weather, flood disaster, geohazards or natural hazards).

In this context, remote sensing is showing high potential to provide valuable information, at various spatial and temporal scales, concerning refined risk analysis and management. The explosive growth and diversity of remote sensing data are strongly contributing to the development of natural hazards research. The combination of unprecedented data sources, increased computational power, and recent advances in data-driven modeling offer exciting new opportunities for expanding our knowledge of risk analysis and management from data.

This Special Issue invites research works in refined risk analysis and management based on remote sensing for geohazards monitoring: (1) extracting knowledge from the remote sensing data deluge for geohazards monitoring, and (2) deriving models that learn much more from data for refined risk analysis and management than traditional data assimilation approaches can. We are inviting submissions including, but not limited to, hazards associated with the following:

Geohazards monitoring;
Remote sensing;
Landslides;
Susceptibility, hazard, and risk prediction and mapping of regional and/or single disasters;
Hybrid modeling approach, coupling physical process models with the versatility of data-driven machine learning;
Data-driven modeling;
Refined risk analysis and management.

Dr. Faming Huang
Prof. Dr. Jinsong Huang
Prof. Dr. Filippo Catani
Dr. Shuihua Jiang
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.

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

17 pages, 6759 KiB

Open AccessArticle

Identification of Complex Slope Subsurface Strata Using Ground-Penetrating Radar

by Tiancheng Wang, Wensheng Zhang, Jinhui Li, Da Liu and Limin Zhang

Remote Sens. 2024, 16(2), 415; https://doi.org/10.3390/rs16020415 - 21 Jan 2024

Viewed by 689

Abstract

Identification of slope subsurface strata for natural soil slopes is essential to assess the stability of potential landslides. The highly variable strata in a slope are hard to characterize by traditional boreholes at limited locations. Ground-penetrating radar (GPR) is a non-destructive method that is capable of capturing continuous subsurface information. However, the accuracy of subsurface identification using GPRs is still an open issue. This work systematically investigates the capability of the GPR technique to identify different strata via both laboratory experiments and on-site examination. Six large-scale models were constructed with various stratigraphic interfaces (i.e., sand–rock, clay–rock, clay–sand, interbedded clay, water table, and V–shaped sand–rock). The continuous interfaces of the strata in these models were obtained using a GPR, and the depths at different points of the interfaces were interpreted. The interpreted depths along the interface were compared with the measured values to quantify the interpretation accuracy. Results show that the depths of interfaces should be interpreted with the relative permittivity, back-calculated using on-site borehole information instead of empirical values. The relative errors of the depth of horizontal interfaces of different strata range within ±5%. The relative and absolute errors of the V–shaped sand–rock interface depths are in the ranges of [−9.9%, 10.5%] and [−107, 119] mm, respectively. Finally, the GPR technique was used in the field to identify the strata of a slope from Tanglang Mountain in China. The continuous profile of the subsurface strata was successfully identified with a relative error within ±5%. Full article

(This article belongs to the Special Issue Remote Sensing for Geohazards Monitoring: Towards Refined Risk Analysis and Management)

► Show Figures

Figure 1

31 pages, 6937 KiB

Open AccessEditor’s ChoiceArticle

Data-Driven Landslide Spatial Prediction and Deformation Monitoring: A Case Study of Shiyan City, China

by Yifan Sheng, Guangli Xu, Bijing Jin, Chao Zhou, Yuanyao Li and Weitao Chen

Remote Sens. 2023, 15(21), 5256; https://doi.org/10.3390/rs15215256 - 06 Nov 2023

Cited by 4 | Viewed by 1455

Abstract

Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived from the MT-InSAR (Multi-Temporal InSAR) method. Reliable landslide susceptibility maps (LSMs) can inform landslide risk managers and government officials. First, sixteen factors were selected to construct a causal factor system for LSM. Next, Pearson correlation analysis, multicollinearity analysis, information gain ratio, and GeoDetector methods were applied to remove the least important factors of STI, plan curvature, TRI, and slope length. Subsequently, information quantity (IQ), logistic regression (LR), frequency ratio (FR), artificial neural network (ANN), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) methods were performed to construct the LSM. The results showed that the distance to a river, slope angle, distance from structure, and engineering geological rock group were the main factors controlling landslide development. A comprehensive set of statistical indicators was employed to evaluate these methods’ effectiveness; sensitivity, F1-measure, and AUC (area under the curve) were calculated and subsequently compared to assess the performance of the methods. Machine learning methods’ training and prediction accuracy were higher than those of statistical methods. The AUC values of the IQ, FR, LR, BP-ANN, RBF-ANN, RF, SVM, and CNN methods were 0.810, 0.854, 0.828, 0.895, 0.916, 0.932, 0.948, and 0.957, respectively. Although the performance order varied for other statistical indicators, overall, the CNN method was the best, while the BP-ANN and RBF-ANN method was the worst among the five examined machine methods. Hence, adopting the CNN approach in this study can enhance LSM accuracy, catering to the needs of planners and government agencies responsible for managing landslide-prone areas and preventing landslide-induced disasters. Full article

(This article belongs to the Special Issue Remote Sensing for Geohazards Monitoring: Towards Refined Risk Analysis and Management)

► Show Figures

Figure 1

19 pages, 4365 KiB

Open AccessArticle

Experimental Investigation on Fragmentation Identification in Loose Slope Landslides by Infrared Emissivity Variability Features

by Xiangxin Liu, Lixin Wu, Wenfei Mao and Licheng Sun

Remote Sens. 2023, 15(21), 5132; https://doi.org/10.3390/rs15215132 - 27 Oct 2023

Viewed by 795

Abstract

Infrared radiation (IR) features that are influenced by infrared emissivity

ε

and physical temperature

T_{d}

have been successfully applied to the early-warning of landslides. Although the infrared emissivity of a rock is a key parameter to determine its thermal radiation properties, the [...] Read more.

Infrared radiation (IR) features that are influenced by infrared emissivity

ε

and physical temperature

T_{d}

have been successfully applied to the early-warning of landslides. Although the infrared emissivity of a rock is a key parameter to determine its thermal radiation properties, the effect of particle size on the infrared emissivity of rock fragments is unknown. So in this paper, granite, marble, and sandstone were used as examples to conduct infrared imaging experiments on rock fragments. Their equivalent emissivity was used to interpret the detected infrared emission, including that from indoor backgrounds. In addition, the characteristics of changes in equivalent emissivity were discussed with reference to changes in observation direction and zenith angle. Then, a computation model of equivalent emissivity based on multiple observation directions and zenith angles was built to reveal the change in equivalent emissivity with particle sizes. The result indicates that the indoor background radiation has a predominant direction just above the rock fragments. The maximum deviation of infrared brightness temperature (IBT) was 0.260 K, and the maximum deviation of equivalent emissivity among different observation directions and zenith angles was 0.0065. After eliminating the influence of directional and angle effects with the operation of normalization, the general law of equivalent emissivity for all rock fragments that change with particle size is consistent. The maximum equivalent emissivity occurs at particle size 5 mm in the condition of particle size larger than 1 mm, while the equivalent emissivity changes inversely with particle size in the condition of particle size smaller than 1 mm. Above all, this study contributes new cognitions to Remote Sensing Rock Mechanics, and provides valuable evidence for better thermal infrared remote sensing monitoring on loose slope landslides. Full article

(This article belongs to the Special Issue Remote Sensing for Geohazards Monitoring: Towards Refined Risk Analysis and Management)

► Show Figures

Figure 1

19 pages, 34606 KiB

Open AccessArticle

Research on Detection and Safety Analysis of Unfavorable Geological Bodies Based on OCTEM-PHA

by Tao Zhu, Jianhua Hu, Guanping Wen and Tan Zhou

Remote Sens. 2023, 15(15), 3888; https://doi.org/10.3390/rs15153888 - 05 Aug 2023

Cited by 1 | Viewed by 842

Abstract

The caving method and mining disturbance may cause geological issues. The advance prediction of unfavorable geological bodies should be conducted to ensure product safety in the underground mine. In this study, we proposed the OCTEM-PHA analysis process and analyzed the Tongkeng Mine in Guangxi. Further, we conducted opposing-coil transient electromagnetic method (OCTEM) detection on four detection lines in T5-1 stope at mine level 386 by using portable geological remote sensing equipment and created inversion maps. Plot profiles and coupling were analyzed with inversion maps to explore the five types of risk factors presented in the mine. The preliminary hazard analysis (PHA) method was used for five types of risk factors to predict the accident consequence and develop safety countermeasures. The results indicate the following: (1) the OCTEM-PHA safety analysis process for unfavorable geological bodies is realistic and feasible. (2) OCTEM shows an excellent response to both high- and low-resistance anomalies in practical engineering applications. The coupling analysis of profiles and inversion maps helps visually analyze the area of apparent resistivity anomalies. (3) The studied mine did not show overhanging formed by the overlying rock layer and large loose void areas. However, the crumbling mining area should be further optimized for balanced mining, the treatment of groundwater and surface water should be improved, and the comparative analysis with the follow-up detection results should be increased. Full article

(This article belongs to the Special Issue Remote Sensing for Geohazards Monitoring: Towards Refined Risk Analysis and Management)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing for Geohazards Monitoring: Towards Refined Risk Analysis and Management

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI