Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Leveraging on SAR Imagery for Landslide Detection and Monitoring
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Leveraging on SAR Imagery for Landslide Detection and Monitoring

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 December 2021) | Viewed by 12943

Special Issue Editors

Dr. Andrea Manconi

E-Mail Website
Guest Editor
ETH Zurich, Dept. of Earth Sciences, Sonneggstrasse 5, 8092 Zurich, Switzerland
Interests: geohazards; monitoring; remote sensing; radar interferometry
Dr. Alessandro Cesare Mondini

E-Mail Website
Guest Editor
Istituto di Ricerca per la Protezione Idrogeologica (IRPI), National Research Council, Via della Madonna Alta 126, Perugia, Italy
Interests: remote sensing; SAR; detection and mapping; landslides; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of SAR data acquired from different platforms (terrestrial, airborne, UAVs, spaceborne, etc.) for the detection, analysis, and monitoring of landslides has increased exponentially. This trend is expected to continue in future, and thus, the development of methods for efficient exploitation of SAR imagery is crucial. In this Special Issue, we aim at collecting papers dealing with topics ranging from regional mapping to local surveys and monitoring applications, where SAR data have been the key for a better understanding and interpretation of the landslide processes. Contributions highlighting the benefit of SAR vs. other data sources are welcomed, as well as papers dealing with new approaches specifically developed to extract detailed information related to landslide phenomena from large data archives, combining different data sources, and/or performing validations with recognized standards.

Dr. Andrea Manconi
Dr. Alessandro Cesare Mondini
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

  • SAR
  • Landslides
  • Mapping
  • Monitoring
  • Radar interferometry
  • Machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 30133 KiB  
Article
Detection and Mapping of Active Landslides before Impoundment in the Baihetan Reservoir Area (China) Based on the Time-Series InSAR Method
by Jiawei Dun, Wenkai Feng, Xiaoyu Yi, Guoqiang Zhang and Mingtang Wu
Remote Sens. 2021, 13(16), 3213; https://doi.org/10.3390/rs13163213 - 13 Aug 2021
Cited by 54 | Viewed by 3672
Abstract
Many potential landslides occured in the Baihetan reservoir area before impoundment. After impoundment, these landslides may still slide, affecting the safe operation of the reservoir area (e.g., causing barrier lakes and floods). Identifying the locations of landslides and their distribution pattern has attracted [...] Read more.
Many potential landslides occured in the Baihetan reservoir area before impoundment. After impoundment, these landslides may still slide, affecting the safe operation of the reservoir area (e.g., causing barrier lakes and floods). Identifying the locations of landslides and their distribution pattern has attracted attention in China and globally. In addition, due to the rolling terrain of the reservoir area, synthetic aperture radar (SAR) imaging will affect the interactive synthetic aperture radar (InSAR) deformation results. Only by obtaining effective deformation information can active landslides be accurately identified. Therefore, the banks of the Hulukou Xiangbiling section of the Baihetan reservoir area before impoundment in the Jinsha River Basin were studied in this paper. Using terrain data and the satellite parameters from Sentinel-1A ascending and descending orbits and ALOS PALSAR ascending orbit, the line-of-sight visibility was quantitatively analyzed, and an analysis method was proposed. Based on the SAR data visibility analysis, the small baseline subset (SBAS) technique was used to process the SAR data to acquire effective deformation. InSAR deformation data was combined with Google Earth imagery to identify 25 active landslides. After field verification, 21 active landslides (14 new) were determined. Most of the active landslides are controlled by faults, and the strata of the other landslides are relatively weak. This InSAR analysis method based on SAR data visibility can provide a reference for identifying and analyzing active landslides in other complicated terrain. Full article
(This article belongs to the Special Issue Leveraging on SAR Imagery for Landslide Detection and Monitoring)
►▼ Show Figures

Graphical abstract

17 pages, 5381 KiB  
Article
A Case Study of Novel Landslide Activity Recognition Using ALOS-1 InSAR within the Ragged Mountain Western Hillslope in Gunnison County, Colorado, USA
by Benjamin W Lowry, Scott Baker and Wendy Zhou
Remote Sens. 2020, 12(12), 1969; https://doi.org/10.3390/rs12121969 - 19 Jun 2020
Cited by 9 | Viewed by 3211
Abstract
The “East Muddy Creek Landslide Complex” in Gunnison County, Colorado, USA destroyed Colorado State Highway 133 from 1986 to 1987 and has been investigated over decades during different periods of reactivation. This paper presents a case study of novel landslide activity recognition related [...] Read more.
The “East Muddy Creek Landslide Complex” in Gunnison County, Colorado, USA destroyed Colorado State Highway 133 from 1986 to 1987 and has been investigated over decades during different periods of reactivation. This paper presents a case study of novel landslide activity recognition related to the landslide complex using Advanced Land Observing Satellite-1 (ALOS-1) Interferometric Synthetic Aperture Radar (InSAR) analysis. We compare the result from ALOS-1 InSAR analysis to landslide recognition investigations from traditional field methods for ground motions at a watershed scale. Line of Sight (LOS) velocity mapping is used to characterize displacement zonation, failure modes, and hazard assessment activities. Mass wasting estimates using existing geological modeling are discussed in terms of potential of landslide element dynamics. ALOS-1 InSAR analysis reveals newly detected ground displacement at very slow to extremely slow velocities with a significantly increased spatial extent. The implications of expanded displacement activity in the context of landslide geomorphology, mountain denudation, exhumation, and future monitoring efforts for hazard and risk assessment are also examined and discussed. Full article
(This article belongs to the Special Issue Leveraging on SAR Imagery for Landslide Detection and Monitoring)
►▼ Show Figures

Graphical abstract

15 pages, 7809 KiB  
Article
Unraveling Spatial and Temporal Heterogeneities of Very Slow Rock-Slope Deformations with Targeted DInSAR Analyses
by Chiara Crippa, Federico Franzosi, Mattia Zonca, Andrea Manconi, Giovanni B. Crosta, Luca Dei Cas and Federico Agliardi
Remote Sens. 2020, 12(8), 1329; https://doi.org/10.3390/rs12081329 - 22 Apr 2020
Cited by 19 | Viewed by 4928
Abstract
Spaceborne radar interferometry is a powerful tool to characterize landslides at local and regional scales. However, its application to very slow rock slope deformations in alpine environments (displacement rates < 5 cm/year) remains challenging, mainly due to low signal to noise ratio, atmospheric [...] Read more.
Spaceborne radar interferometry is a powerful tool to characterize landslides at local and regional scales. However, its application to very slow rock slope deformations in alpine environments (displacement rates < 5 cm/year) remains challenging, mainly due to low signal to noise ratio, atmospheric disturbances, snow cover effects, and complexities resulting from heterogeneous displacement in space and time. Here we combine SqueeSARTM data, targeted multi-temporal baseline DInSAR, GPS data, and detailed field morpho-structural mapping, to unravel the kinematics, internal segmentation, and style of activity of the Mt. Mater deep-seated gravitational slope deformation (DSGSD) in Valle Spluga (Italy). We retrieve slope kinematics by performing 2D decomposition (2D InSAR) of SqueeSARTM products derived from Sentinel-1 data acquired in ascending and descending orbits. To achieve a spatially-distributed characterization of DSGSD displacement patterns and activity, we process Sentinel-1 A/B images (2016-2019) with increasing temporal baselines (ranging from 24-days to 1-year) and generate several multi-temporal interferograms. Unwrapped displacement maps are validated using ground-based GPS data. Interferograms derived with different temporal baselines reveal a strong kinematic and morpho-structural heterogeneity and outline nested rockslides and active sectors, that arise from the background displacement signal of the main DSGSD. Seasonal interferograms, supported by GPS displacement measurements, reveal non-linear displacement trends suggesting a complex response of different slope sectors to rainfall and snowmelt. Our analyses clearly outline a composite slope instability with different nested sectors possibly undergoing different evolutionary trends towards failure. The results herein outline the potential of a targeted use of DInSAR for the detailed investigation of very slow rock slope deformations in different geological and geomorphological settings. Full article
(This article belongs to the Special Issue Leveraging on SAR Imagery for Landslide Detection and Monitoring)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop