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Remote Sensing for Rock Slope and Rockfall Analysis II

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 599

Special Issue Editors


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Guest Editor
Department of Engineering Geology and Hydrogeology, RWTH Aachen University, 52064 Aachen, Germany
Interests: rock slope stability; remote sensing; numerical modeling; rock mechanics; geotechnics

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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

Special Issue Information

Dear Colleagues,

Rock slope instability is a major and widespread phenomenon that can represent significant hazards, especially in areas characterized by high and steep natural or engineered slopes. As a consequence, depending on magnitude, size and velocity, slope failure and rockfall events can cause severe damage, injuries, and casualties. As such, effective mitigation measures are essential to control their effect. Over the last two decades, with the advent of more sophisticated remote sensing and numerical modelling techniques, the approach to rock slope investigation and monitoring has changed substantially. Remote sensing techniques such as LiDAR, radar, and photogrammetry in rock slope analysis have allowed for the rapid and safe acquisition of a huge amount of high-quality information to be used for slope analyses and monitoring. Such techniques represent valuable tools in rock mechanics. However, it is important to highlight that the use of these data both in conventional and more sophisticated numerical analyses can be complex. In this context, this Special Issue will present novel contributions including original research, case studies, and new approaches in rock slope and rockfall analysis and monitoring that take advantage of remote sensing techniques. This will also comprise the use of recent statistical and geotechnical methods, such as a discrete fracture network and advanced rock laboratory testing, that are able to improve understanding surrounding rock behavior.

Therefore, contributions related to the following topic will be welcome:

  • The integration of different remote sensing techniques for rock slope and rockfall analyses;
  • Rock mass characterization;
  • Rock slope stability assessment;
  • Back analysis;
  • Rockfall intensity, velocity, and probability assessment;
  • Rockfall hazard and risk assessment;
  • Rockfall mitigation measures design;
  • Rock slope and rockfall monitoring;
  • Early warning systems and evacuation planning;
  • Numerical modelling.

Dr. Mirko Francioni
Dr. Pooya Hamdi
Dr. Davide Donati
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Research

25 pages, 14900 KiB  
Article
Inventory and Spatial Distribution of Landslides on the Eastern Slope of Gongga Mountain, Southwest China
by Runze Ge, Jian Chen, Sheng Ma and Huarong Tan
Remote Sens. 2024, 16(18), 3360; https://doi.org/10.3390/rs16183360 - 10 Sep 2024
Viewed by 202
Abstract
The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it [...] Read more.
The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains. Full article
(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis II)
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