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Remote Sensing
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Advancement of Remote Sensing in Landslide Monitoring and Early Warning
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Advancement of Remote Sensing in Landslide Monitoring and Early Warning

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 26062

Special Issue Editors

Dr. Emanuele Intrieri

E-Mail Website
Guest Editor
Department of Earth Sciences, Università degli Studi di Firenze, via G. La Pira 4, 50121 Firenze, Italy
Interests: cultural heritage; early warning systems; remote sensing; landslides; forecasting methods; SAR interferometry
Special Issues, Collections and Topics in MDPI journals
Dr. Federico Raspini

E-Mail Website
Co-Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4-50121 Firenze, Italy
Interests: landslide; subsidence; risk analysis; monitoring; InSAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last two decades, many of the advancements in our knowledge of landslides and our ability to cope with the risk they represent have been reached thanks to developments and improvements in remote sensing. Remote sensing techniques are numerous and diverse, but they all share some common advantages that explain their popularity, such as the ability to monitor wide zones with limited or no access to dangerous or remote areas.

When remote sensing techniques are capable of performing frequent acquisitions with respect to the velocity of the slope, then early warning applications will become possible.

The entire field of remote sensing is experiencing strong growth in this regard. The relatively short revisit times of recent interferometric constellations have enabled, for the first time, the possibility to provide landslide early warnings from satellite platforms. The development of new ground-based interferometric radar algorithms and devices enables acquisition to be performed in a few tens of seconds. The rapidly expanding field of low-cost wireless sensor networks allows flexible and economic setups to be used. Drones are emergent and versatile tools whose full potential is yet to be explored and that may provide new possibilities for photogrammetry, laser scanning, and thermal and hyperspectral imagery.

Therefore, the scope of this Special Issue is to address the advances of these and other remote sensing techniques in relation to monitoring and characterization of landslides by assessing their risk, setting early warning systems, or forecasting their failure.

To address this aim, we invite authors to submit papers on the following non-exhaustive list of topics:

  • Case studies showing the results from cutting edge and innovative remote sensing techniques;
  • Review papers on recent advancements on one or more remote sensing techniques in relation to landslide monitoring and early warning;
  • Papers presenting the development of new sensors, algorithms, and devices used for remote sensing;
  • Best practices of landslide early warning systems employing remote sensing techniques;
  • Examples of landslide forecasting performed with remote sensing techniques.

Dr. Emanuele Intrieri
Dr. Federico Raspini
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.

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Published Papers (5 papers)

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Research

17 pages, 21576 KiB  
Article
Low-Cost Automatic Slope Monitoring Using Vector Tracking Analyses on Live-Streamed Time-Lapse Imagery
by Muhammad Waqas Khan, Stuart Dunning, Rupert Bainbridge, James Martin, Alejandro Diaz-Moreno, Hamdi Torun, Nanlin Jin, John Woodward and Michael Lim
Remote Sens. 2021, 13(5), 893; https://doi.org/10.3390/rs13050893 - 27 Feb 2021
Cited by 10 | Viewed by 3810
Abstract
Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. This approach is applied [...] Read more.
Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. This approach is applied to the Rest and Be Thankful slope, Argyle, Scotland, where past debris flows have blocked the A83 or forced preemptive closure. TLI of the Rest and Be Thankful are taken from a fixed station, 28 mm lens, time lapse camera every 15 min. Imagery is filtered to counter the effects of misalignment from wind induced vibration of the camera, asymmetric lighting, and fog. Particle image velocimetry (PIV) algorithms are then run to produce slope movement velocity vectors. PIV generated vectors are automatically post-processed to separate vectors generated by slope movement from false positives generated by harsh environmental conditions. Results for images over a 20-day period indicated precursor slope movement initiated by a rainfall event, a period of quiescence for 10 days, followed by a large landslide failure during proceeding rainfall where over 3000 tons of sediment reached the road. Results suggest low cost, live streamed TLI and this novel PIV approach correctly detect and, importantly, report precursor slope movement, allowing early warning, effective management and landslide impact mitigation. Future applications of this technique will allow the development of an effective decision-making tool for asset management of the A83, reducing the risk to life of motorists. The technique can also be applied to other critical infrastructure sites, allowing hazard risk reduction. Full article
(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Monitoring and Early Warning)
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23 pages, 14677 KiB  
Article
Monitoring Surface Displacement of a Deep-Seated Landslide by a Low-Cost and near Real-Time GNSS System
by Ela Šegina, Tina Peternel, Tilen Urbančič, Eugenio Realini, Matija Zupan, Jernej Jež, Stefano Caldera, Andrea Gatti, Giulio Tagliaferro, Angelo Consoli, Joaquín Reyes González and Mateja Jemec Auflič
Remote Sens. 2020, 12(20), 3375; https://doi.org/10.3390/rs12203375 - 15 Oct 2020
Cited by 39 | Viewed by 4657
Abstract
A prototype of a low-cost GNSS (Global Navigation Satellite System) monitoring system was installed on a deep-seated landslide in north-western Slovenia to test its performance under field conditions. The system consists of newly developed GNSS stations based on low-cost, dual-frequency receivers and open-source [...] Read more.
A prototype of a low-cost GNSS (Global Navigation Satellite System) monitoring system was installed on a deep-seated landslide in north-western Slovenia to test its performance under field conditions. The system consists of newly developed GNSS stations based on low-cost, dual-frequency receivers and open-source GNSS processing software. It automatically receives GNSS data and transmits them over the Internet. The system processes the data server-side and makes them available to the end user via a web portal. The detected surface displacements were evaluated through a comparison with the network of classic geodetic measurements. The results of a nine-month monitoring period using seven GNSS stations provided a detailed insight into the spatial and temporal pattern of deep-seated landslide surface movements. The displacement data were correlated with precipitation measurements at the site to reveal how different parts of the landslide react to rainfall. These data form the basis for the further development of an early-warning system which will help to manage the risk the landslide poses to the local population and infrastructure. Full article
(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Monitoring and Early Warning)
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33 pages, 16148 KiB  
Article
Small Scale Landslide Detection Using Sentinel-1 Interferometric SAR Coherence
by Marios Tzouvaras, Chris Danezis and Diofantos G. Hadjimitsis
Remote Sens. 2020, 12(10), 1560; https://doi.org/10.3390/rs12101560 - 14 May 2020
Cited by 40 | Viewed by 9073
Abstract
Infrastructure is operational under normal circumstances and is designed to cope with common natural disruptions such as rainfall and snow. Natural hazards can lead to severe problems at the areas where such phenomena occur, but also at neighboring regions as they can make [...] Read more.
Infrastructure is operational under normal circumstances and is designed to cope with common natural disruptions such as rainfall and snow. Natural hazards can lead to severe problems at the areas where such phenomena occur, but also at neighboring regions as they can make parts of a road network virtually impassable. Landslides are one of the most devastating natural hazards worldwide, triggered by various factors that can be monitored via ground-based and/or satellite-based techniques. Cyprus is in an area of high susceptibility to such phenomena. Currently, extensive field campaigns including geotechnical drilling investigations and geophysical excavations are conducted to monitor land movements, and, at the same time, determine the geological suitability of areas. Active satellite remote sensors, namely Synthetic Aperture Radar (SAR), have been widely used for detecting and monitoring landslides and other ground deformation phenomena using Earth Observation based techniques. This paper aims to demonstrate how the use of Copernicus open-access and freely distributed datasets along with the exploitation of the open-source processing software SNAP (Sentinel’s Application Platform), provided by the European Space Agency, can be used for landslide detection, as in the case study near Pissouri, where a landslide was triggered by heavy rainfall on 15 February 2019, which caused a major disturbance to everyday commuters since the motorway connecting the cities of Limassol and Paphos remained closed for more than a month. The Coherent Change Detection (CCD) methodology was applied successfully by detecting the phenomenon under study accurately, using two indicators (the coherence difference and the normalized coherence difference). Receiver Operating Characteristic (ROC) analysis was carried out to measure their performance with the coherence difference having an overall accuracy of 93% and the normalized coherence difference having an overall accuracy of 94.8% for detecting the landslide and non-landslide areas. The probability of landslide detection was 63.2% in the case of the coherence difference and increased to 73.7% for the normalized coherence difference, whereas the probability of false alarm for both indicators was approximately 1%. Full article
(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Monitoring and Early Warning)
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Graphical abstract

24 pages, 8900 KiB  
Article
Landslide Prediction Method Based on a Ground-Based Micro-Deformation Monitoring Radar
by Lin Qi, Weixian Tan, Pingping Huang, Wei Xu, Yaolong Qi and Mingzhi Zhang
Remote Sens. 2020, 12(8), 1230; https://doi.org/10.3390/rs12081230 - 12 Apr 2020
Cited by 9 | Viewed by 3642
Abstract
As remote sensing methods have received a lot of attention, ground-based micro- deformation monitoring radars have been widely used in recent years due to their wide range, high accuracy, and all-day monitoring capability. On the one hand, these monitoring radars break through the [...] Read more.
As remote sensing methods have received a lot of attention, ground-based micro- deformation monitoring radars have been widely used in recent years due to their wide range, high accuracy, and all-day monitoring capability. On the one hand, these monitoring radars break through the limitations of traditional point monitoring equipment such as the Global Navigation Satellite System (GNSS) and fissure meters in terms of monitoring scope and ease of installation. On the other hand, the data types of these monitoring radars are more varied. Therefore, it may be difficult for the data-processing method of traditional point monitoring equipment to take all advantages of this type of radar. In this paper, based on time-series monitoring data of ground-based micro-deformation monitoring radars, three parameters—extent of change (EOC), extent of stability (EOS), and extent of mutation (EOM)—are calculated according to deformation value, coherence and deformation pixels size. Then a method for landslide prediction by combining these three parameters with the inverse velocity method is proposed. The effectiveness of this method is verified by the measured data of a landslide in Yunnan Province, China. The experimental results show that the method can correctly discern deformation areas and provide more accurate monitoring results, especially when the deformation trend changes rapidly. In summary, this method can improve the response rate and prediction accuracy in extreme cases, such as rapid deformation. Full article
(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Monitoring and Early Warning)
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17 pages, 6716 KiB  
Article
Pre-Event Deformation and Failure Mechanism Analysis of the Pusa Landslide, China with Multi-Sensor SAR Imagery
by Liquan Chen, Chaoying Zhao, Ya Kang, Hengyi Chen, Chengsheng Yang, Bin Li, Yuanyuan Liu and Aiguo Xing
Remote Sens. 2020, 12(5), 856; https://doi.org/10.3390/rs12050856 - 6 Mar 2020
Cited by 20 | Viewed by 3645
Abstract
The Pusa landslide, in Guizhou, China, occurred on 28 August 2017, caused 26 deaths with 9 missing. However, few studies about the pre-event surface deformation are provided because of the complex landslide formation and failure mechanism. To retrieve the precursory signal of this [...] Read more.
The Pusa landslide, in Guizhou, China, occurred on 28 August 2017, caused 26 deaths with 9 missing. However, few studies about the pre-event surface deformation are provided because of the complex landslide formation and failure mechanism. To retrieve the precursory signal of this landslide, we recovered pre-event deformation with multi-sensor synthetic aperture radar (SAR) imagery. First, we delineated the boundary and source area of the Pusa landslide based on the coherence and SAR intensity maps. Second, we detected the line-of-sight (LOS) deformation rate and time series before the Pusa landslide with ALOS/PALSAR-2 and Sentinel-1A/B SAR imagery data, where we found that the onset of the deformation is four months before landslide event. Finally, we conceptualized the failure mechanism of the Pusa landslide as the joint effects of rainfall and mining activity. This research provides new insights into the failure mechanism and early warning of rock avalanches. Full article
(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Monitoring and Early Warning)
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