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Grasping Landslide Dynamics through Remote Sensing for Better Modelling and...
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Grasping Landslide Dynamics through Remote Sensing for Better Modelling and Risk Mitigation

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 (31 January 2024) | Viewed by 4879

Special Issue Editors

Dr. Giulia Bossi

E-Mail Website
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council of Italy, Consiglio Nazionale Delle Ricerche, Rome, Italy
Interests: engineering geology; geotechnics; landslides; monitoring, modelling, and mitigation
Dr. Roberta Bonì

E-Mail Website
Guest Editor
Department of Pure and Applied Sciences, University of Urbino “Carlo Bo”, Urbino, Italy
Interests: engineering geology; remote sensing; landslides; land subsidence; InSAR; monitoring; modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landslides are very well suited to be monitored through remote sensing techniques, and for the past decade, the scientific output has been there to prove it. The recent advances in the development of techniques for remote sensing coupled with the increasing availability of data should translate in a new way to challenge rock and soil instability phenomena in terms of in-depth characterization of the dynamic of the process to produce more reliable numerical models and tailored mitigation strategies.

It is thus our pleasure to announce the launch of a new Special Issue in Remote Sensing whose goal is to gather recent research and reviews on using remote sensing data to gather new insight on landslides, rockslides, and debris flow process. Data may include but are not limited to space or airborne remote sensing techniques, such as satellite imagery, Synthetic aperture radar (SAR), UAV/UAS, and LiDAR techniques, possibly coupled with in situ monitoring techniques. Methods to process/ analyze data with machine learning approaches are much appreciated as calibration and validation of numerical models based on monitoring. Both purely methodological as down to earth case study applications are welcome, particularly if they can lead to a better definition of hazard/risk mitigation strategy.

Dr. Giulia Bossi
Dr. Roberta Bonì
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

  • monitoring
  • modeling
  • mitigation
  • machine learning
  • InSAR
  • LiDAR
  • UAV/UAS
  • landslide risk

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Published Papers (2 papers)

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Research

20 pages, 18939 KiB  
Article
A Low-Cost and Fast Operational Procedure to Identify Potential Slope Instabilities in Cultural Heritage Sites
by Stefano Morelli, Roberta Bonì, Mauro De Donatis, Lucia Marino, Giulio Fabrizio Pappafico and Mirko Francioni
Remote Sens. 2023, 15(23), 5574; https://doi.org/10.3390/rs15235574 - 30 Nov 2023
Cited by 1 | Viewed by 1536
Abstract
Italy is famous for its one-of-a-kind landscapes and the many cultural heritage sites characterizing the story of its regions. In central Italy, during the medieval age, some of them were built on the top of high and steep cliffs, often on the top [...] Read more.
Italy is famous for its one-of-a-kind landscapes and the many cultural heritage sites characterizing the story of its regions. In central Italy, during the medieval age, some of them were built on the top of high and steep cliffs, often on the top of ancient ruins, to protect urban agglomerations, goods and people. The geographical locations of these centers allowed them to maintain their original conformation over time, but, at the same time, exposed them to a high risk of landslides. In this context, this research aimed to present an integrated and low-cost approach to study the potential landslide phenomena affecting two medieval towns. Field surveys and mapping were carried out through the use of innovative digital mapping tools to create a digital database directly on the field. Data gathered during field surveys were integrated with GIS analyses for an improved interpretation of the geological and geomorphological features. Due to the inaccessibility of the cliffs surrounding the two villages, a more detailed analysis of these areas was performed through the use of unmanned aerial vehicle-based photogrammetry, while advanced differential synthetic aperture radar interferometry (A-DInSAR) interpretation was undertaken to verify the stability of the buildings in proximity to the cliffs and other potential active failures. The results of the study highlighted the similar geometry and structural settings of the two areas. Kinematically, the intersection of three main joint sets tends to detach blocks (sometimes in high volumes) from the cliffs. The A-DInSAR analysis demonstrated the presence of a landslide failure along the northwest side of the Monte San Martino town. The buildings in proximity to the cliffs did not show evidence of movements. More generally, this research gives insights into the pro and cons of different survey and analysis approaches and into the benefits of their procedural integration in space and in time. Overall, the procedure developed here may be applied in similar contexts in order to understand the structural features driving slopes’ instabilities and create digital databases of geological/monitoring data. Full article
(This article belongs to the Special Issue Grasping Landslide Dynamics through Remote Sensing for Better Modelling and Risk Mitigation)
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Graphical abstract

27 pages, 20003 KiB  
Article
Identification and Analysis of Landslides in the Ahai Reservoir Area of the Jinsha River Basin Using a Combination of DS-InSAR, Optical Images, and Field Surveys
by Yongfa Li, Xiaoqing Zuo, Daming Zhu, Wenhao Wu, Xu Yang, Shipeng Guo, Chao Shi, Cheng Huang, Fang Li and Xinyu Liu
Remote Sens. 2022, 14(24), 6274; https://doi.org/10.3390/rs14246274 - 11 Dec 2022
Cited by 17 | Viewed by 2582
Abstract
We employed ascending and descending Sentinel-1A, optical image data, and field investigation methods to identify and monitor landslides in the Jinsha River Basin to overcome the difficulties associated with the use of a single method and its inaccuracies in identifying landslides in the [...] Read more.
We employed ascending and descending Sentinel-1A, optical image data, and field investigation methods to identify and monitor landslides in the Jinsha River Basin to overcome the difficulties associated with the use of a single method and its inaccuracies in identifying landslides in the alpine and canyon areas. Using distributed scatterer-synthetic aperture radar interferometry (DS-InSAR), Sentinel-1A ascending and descending data were integrated to obtain surface deformation information within the study area from July 2017 to May 2019. Thereafter, high-resolution optical image data were introduced to interpret landslides, and field investigations were conducted to validate landslides. These combined methods enabled the assessment of spatiotemporal evolutionary characteristics, and their accuracy in identifying typical landslides was verified. The results showed that the use of both ascending and descending data effectively avoided certain problems, such as the inability to identify certain landslide hazards or the retrieval of incomplete identification results due to geometric distortion associated with single-track SAR imaging. The combined use of these methods effectively improves the timeliness and verification of the accuracy of landslides. Fifteen landslides were identified in the study area, which had different degrees of tension cracks, vertical dislocations, and slip marks that were verified in the field. Of these, two landslides show serious deformation characteristics that currently pose a serious threat to lives and infrastructure. Follow-up monitoring of these landslides is essential. These findings will assist in obtaining comprehensive information about the distribution of landslides and their deformation developmental trends in the Ahai Reservoir area of the Jinsha River Basin and show that the combined methods can be employed to prevent and control landslides in this area. Full article
(This article belongs to the Special Issue Grasping Landslide Dynamics through Remote Sensing for Better Modelling and Risk Mitigation)
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