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Remote Sensing and Numerical Modeling for Landslide Analysis

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 1610

Special Issue Editors


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Guest Editor
Department of Civil, Chemical, Environmental, and Material Engineering, Alma Mater Studiorum—University of Bologna, 40136 Bologna, Italy
Interests: landslides; rock mass characterization remote sensing; numerical modelling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Earth Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Interests: rock mechanics; engineering geology; numerical modelling; remote sensing

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Guest Editor
Department of Civil, Chemical, Environmental, and Material Engineering, Alma Mater Studiorum—University of Bologna, 40136 Bologna, Italy
Interests: geotechnics; natural hazards; remote sensing; landslide hazard
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, Italy
Interests: geotechnics; natural hazards; remote sensing; numerical modelling

Special Issue Information

Dear Colleagues,

Over the last two decades, advanced remote sensing methods and their applications have allowed geoscientists and engineers to investigate, characterize, and monitor the evolution and behavior of soil and rock slopes. Terrestrial, airborne, and satellite methods, including digital photogrammetry, laser scanning, and synthetic aperture radar, are today routinely employed in slope characterization, monitoring, as well as in hazard analysis and risk assessment. Remote sensing data are also important in the construction, constraint, and validation of numerical modelling analyses. Three-dimensional terrain models can be used in the creation of the numerical model slope geometry. Rock mass quality and discontinuity data can be used to determine slope model input material parameters and to define both discrete discontinuities and fracture networks at multiple scales, from the outcrop to the regional scale. Monitoring data can be used to constrain and validate the numerical modelling results and to assist in the identification of mechanism of failure and the factors that control the evolution and stability of a slope.

The aim of this Special Issue is to showcase novel applications, approaches, and workflows used to combine and integrate remote sensing data collection and numerical modelling analyses. This will encompass a broad variety of remote sensing methods and application techniques for the investigation of stable and unstable natural and engineered slopes, including open pit slopes, river embankments, and others.

Contributions that relate to, but may be not limited to, the following topics are encouraged:

  • Remote sensing-based (e.g., digital photogrammetry and laser scanning) rock mass characterization for numerical stability analyses.
  • Infrared and thermal remote sensing analyses for constraining groundwater and seepage in numerical models of slopes and for the identification, mapping, and characterization of rock bridges, exfoliation joints, weathering, and damage.
  • Multi-spectral and hyperspectral data collection and/or analysis to evaluate moisture, discontinuity, or rock surface conditions for soil and rock slope simulations.
  • Numerical modelling of slopes constrained and/or validated using terrestrial, airborne, and satellite monitoring data analysis (e.g., displacement, subsidence, and erosion).
  • Remote sensing data collection and processing for the construction of discrete fracture networks (DFN) to be used for numerical modelling of slopes.
  • Combination of remote sensing and geophysical datasets (e.g., acoustic emission, microseismicity, and tomography) for numerical modelling investigations.
  • Applications of synthetic remote sensing datasets derived from numerical models.

Dr. Davide Donati
Prof. Dr. Doug Stead
Prof. Dr. Lisa Borgatti
Dr. Monica Ghirotti
Dr. Mirko Francioni
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

  • landslides
  • digital photogrammetry
  • laser scanning
  • infrared and multi-spectral methods
  • slope monitoring
  • numerical modelling
  • rock mass characterization

Published Papers (1 paper)

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Research

22 pages, 21367 KiB  
Article
Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing
by Jiaxin Zhong, Qiaomin Li, Jia Zhang, Pingping Luo and Wei Zhu
Remote Sens. 2024, 16(2), 345; https://doi.org/10.3390/rs16020345 - 15 Jan 2024
Viewed by 1363
Abstract
Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes [...] Read more.
Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters. Full article
(This article belongs to the Special Issue Remote Sensing and Numerical Modeling for Landslide Analysis)
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