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Remote Sensing
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Remote Sensing in Engineering Geology (Third Edition)
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Remote Sensing in Engineering Geology (Third Edition)

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 3229

Special Issue Editors

Dr. Mirko Francioni

E-Mail Website
Guest Editor
Department of Pure and Applied Sciences, University of Urbino Carlo Bo, 61029 Urbino, Italy
Interests: engineering geology; remote sensing; natural hazards; landslides; numerical modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thomas Oommen

E-Mail Website
Guest Editor
Department of Geology & Geological Engineering, University of Mississippi, Oxford, MS 38677, USA
Interests: liquefaction susceptibility evaluation at local and regional scales using in-situ measurements and remote sensing observations; estimating liquefaction-induced damage such as lateral spread displacement; active learning to identify data gaps in empirical models; documenting earthquake-induced damages, especially liquefaction, using aerial/satellite images that are sensitive to surficial moisture; transportation geotechnics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last two decades, the use of remote sensing for the investigation of geological or geotechnical engineering problems has significantly increased. The availability of high spatial and temporal resolution datasets from aerial and satellite platforms, and the use of unmanned aerial vehicles (drones) for data collection has accelerated the adoption of remote sensing in geosciences and geoengineering. The most commonly used remote sensing sensors and techniques include Light Detection and Ranging (LiDAR), Synthetic Aperture Radar (SAR), thermal, hyper-spectral, multi-spectral, and photogrammetry. These remote sensing technologies are being widely used for problems related to ground subsidence, slope monitoring, hydrogeology, site characterization, coastal engineering, erosion, and geo-hazard studies.

In this context, this Special Issue invites high-quality and innovative scientific papers that advance the science of remote sensing in solving problems related to engineering, geology and geoscience. These could include analyzing and monitoring landslides and volcanos, the characterization of rock masses and geotechnical sites, ground deformation analyses, and mining applications. Special consideration will also be given to the use of GIS, big datasets, and artificial intelligence- and machine learning-based methods for remotely sensed data processing and modeling.

Dr. Mirko Francioni
Prof. Dr. Thomas Oommen
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

  • engineering geology
  • ground subsidence
  • landslides monitoring
  • hydrogeology
  • ground deformation analyses
  • coastal engineering
  • natural hazards
  • remote sensing technologies

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Related Special Issues

Published Papers (3 papers)

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Research

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17 pages, 7645 KiB  
Article
Strain and Deformation Analysis Using 3D Geological Finite Element Modeling with Comparison to Extensometer and Tiltmeter Observations
by Meng Li, Hexiong Lu, Ahmed El-Mowafy, Tieding Lu and Aiping Zhao
Remote Sens. 2024, 16(21), 3967; https://doi.org/10.3390/rs16213967 - 25 Oct 2024
Viewed by 524
Abstract
This study verifies the practicality of using finite element analysis for strain and deformation analysis in regions with sparse GNSS stations. A digital 3D terrain model is constructed using DEM data, and regional rock mass properties are integrated to simulate geological structures, resulting [...] Read more.
This study verifies the practicality of using finite element analysis for strain and deformation analysis in regions with sparse GNSS stations. A digital 3D terrain model is constructed using DEM data, and regional rock mass properties are integrated to simulate geological structures, resulting in the development of a 3D geological finite element model (FEM) using the ANSYS Workbench module. Gravity load and thermal constraints are applied to derive directional strain and deformation solutions, and the model results are compared to actual strain and tilt measurements from the Jiujiang Seismic Station (JSS). The results show that temperature variations significantly affect strain and deformation, particularly due to the elevation difference between the mountain base and summit. Higher temperatures increase thermal strain, causing tensile effects, while lower temperatures reduce thermal strain, leading to compressive effects. Strain and deformation patterns are strongly influenced by geological structures, gravity, and topography, with valleys experiencing tensile strain and ridges undergoing compression. The deformation trend indicates a southwestward movement across the study area. A comparison of FEM results with ten years of strain and tiltmeter data from JSS reveals a strong correlation between the model predictions and actual measurements, with correlation coefficients of 0.6 and 0.75 for strain in the NS and EW directions, and 0.8 and 0.9 for deformation in the NS and EW directions, respectively. These findings confirm that the 3D geological FEM is applicable for regional strain and deformation analysis, providing a feasible alternative in areas with limited GNSS monitoring. This method provides valuable insights into crustal deformation in regions with sparse strain and deformation measurement data. Full article
(This article belongs to the Special Issue Remote Sensing in Engineering Geology (Third Edition))
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21 pages, 9517 KiB  
Article
Stability Assessment of the Maltravieso Cave (Caceres, Spain) Through Engineering Rock Mass Classification, Empirical, Numerical and Remote Techniques
by Abdelmadjid Benrabah, Salvador Senent Domínguez, Hipolito Collado Giraldo, Celia Chaves Rodríguez and Luis Jorda Bordehore
Remote Sens. 2024, 16(20), 3883; https://doi.org/10.3390/rs16203883 - 18 Oct 2024
Viewed by 443
Abstract
Caves have long fascinated humanity, serving as shelters, canvases for artistic expression and now significant attractions in the realm of tourism. Among these remarkable geological formations, the Maltravieso cave in Extremadura, Spain, stands out for its rich archaeological and paleontological heritage, particularly its [...] Read more.
Caves have long fascinated humanity, serving as shelters, canvases for artistic expression and now significant attractions in the realm of tourism. Among these remarkable geological formations, the Maltravieso cave in Extremadura, Spain, stands out for its rich archaeological and paleontological heritage, particularly its collection of Paleolithic rock art. Despite its cultural significance, there is a notable dearth of studies addressing the stability of the cave from an engineering perspective. This article presents a pioneering study aimed at assessing the stability of the Maltravieso cave through a multidisciplinary approach: using empirical geomechanical classifications such as the Q Index, Rock Mass Rating (RMR) and the recently formulated Cave Geomechanical Index (CGI), alongside other techniques like Structure from Motion (SfM), 2D numerical modeling and 3D wedge analysis. This research aims to fill the gap in our opinion of cave stability assessment. By combining field data collection with sophisticated analysis methods, this study seeks to provide valuable insights into the geomechanical properties of the Maltravieso cave and validate a simple yet effective methodology for evaluating the stability of natural caves. This work not only contributes to the body of knowledge regarding cave geomechanics but also underscores the importance of preserving these invaluable cultural and geological treasures for future generations. Full article
(This article belongs to the Special Issue Remote Sensing in Engineering Geology (Third Edition))
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Review

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39 pages, 28523 KiB  
Review
Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing
by Katarzyna Strząbała, Paweł Ćwiąkała and Edyta Puniach
Remote Sens. 2024, 16(15), 2781; https://doi.org/10.3390/rs16152781 - 30 Jul 2024
Cited by 1 | Viewed by 1919
Abstract
Landslides are a widely recognized phenomenon, causing huge economic and human losses worldwide. The detection of spatial and temporal landslide deformation, together with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Advanced landslide monitoring systems based on [...] Read more.
Landslides are a widely recognized phenomenon, causing huge economic and human losses worldwide. The detection of spatial and temporal landslide deformation, together with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Advanced landslide monitoring systems based on remote sensing techniques (RSTs) play a crucial role in risk management and provide important support for early warning systems (EWSs) at local and regional scales. The purpose of this article is to present a review of the current state of knowledge in the development of RSTs used for identifying landslide precursors, as well as detecting, monitoring, and predicting landslides. Almost 200 articles from 2010 to 2024 were analyzed, in which the authors utilized RSTs to detect potential precursors for early warning of hazards. The applications, challenges, and trends of RSTs, largely dependent on the type of landslide, deformation pattern, hazards posed by the landslide, and the size of the area of interest, were also discussed. Although the article indicates some limitations of the RSTs used so far, integrating different techniques and technological developments offers the opportunity to create reliable EWSs and improve existing ones. Full article
(This article belongs to the Special Issue Remote Sensing in Engineering Geology (Third Edition))
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