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Remote Sensing
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Remote Sensing-Based Monitoring and Modeling of Ground Movements
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Remote Sensing-Based Monitoring and Modeling of Ground Movements

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 (30 June 2022) | Viewed by 9828

Special Issue Editors

Dr. Wojciech T. Witkowski

E-Mail Website
Guest Editor
Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Science and Technology in Krakow, 30-059 Kraków, Poland
Interests: earth observation satellite; modeling of land subsidence; satellite radar interferometry; analysis of the displacement field and strain tensor deformation; artificial intelligence and machine learning; spatial analysis; geostatistics; landslide and glacier motions; natural hazard
Dr. Ling Chang

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514AE Enschede, The Netherlands
Interests: geodetic analysis of imaging remote sensing data and data integration; deformation time series modeling and statistical hypothesis testing; physical interpretation of deformation processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of remote sensing to monitoring ground movements has been systematically increasing in the past several decades. Reliable analysis of earth observations such as optical and radar imagery, allows for effective learning about natural and human-induced phenomena. For instance, as one of the products of remote sensing techniques, the displacement field for pre-, co-, and post-seismic movements, landslides, volcanic eruptions, and glacial motion enables learning and a better understanding of the physics of natural issues. The remote-sensing-derived ground movement estimations related to anthropogenic phenomena such as land subsidence due to underground mining, oil, or gas, and water withdrawal, mining tremors, earth fissure, or sinkholes are also of significance for interpreting deformation processes and building up (near-real-time) early warning systems. The availability of remote-sensing-based long-term monitoring of ground movement facilitates robust modeling and prediction of these natural and anthropogenic phenomena. All of these aspects are essential for natural and anthropogenic hazard monitoring and interpretation.

In this Special Issue, the relevant original research articles, reviews, and technical notes are welcome. Topics include, but are not limited to the following:

  • Monitoring technologies of land surface movements
  • Ground deformation modeling and prediction
  • Applied earth observations for natural and anthropogenic hazards

Dr. Wojciech T. Witkowski
Dr. Ling Chang
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

  • InSAR (interferometric synthetic aperture radar)
  • Deformation modeling and prediction
  • Change detection
  • Land subsidence and uplift
  • Spatio-temporal statistics
  • Natural hazards

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Published Papers (2 papers)

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Research

20 pages, 22740 KiB  
Article
Sinkhole Scanner: A New Method to Detect Sinkhole-Related Spatio-Temporal Patterns in InSAR Deformation Time Series
by Anurag Kulshrestha, Ling Chang and Alfred Stein
Remote Sens. 2021, 13(15), 2906; https://doi.org/10.3390/rs13152906 - 24 Jul 2021
Cited by 5 | Viewed by 4230
Abstract
Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal [...] Read more.
Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal deformations, this study presents a sinkhole pattern detector, called the Sinkhole Scanner. The Sinkhole Scanner includes a spatio-temporal mathematical model such as a 2-dimensional time evolving Gaussian function as a kernel, which moves over the study area using a sliding window approach. The scanner attempts to fit the model over deformation time series of Constantly Coherent Scatterers (CCS) intersected by the window and returns the posterior variance as a measure of goodness of fit. In this way, the scanner searches for subsiding regions resembling sinkhole shapes over a sinkhole prone area. It is designed to detect large sinkholes with a high efficiency, and small sinkholes with a lower efficiency. It is tested at four different spatial scales, and on a simulated and real set of deformation data. Real data were obtained from Sentinel-1A SLC data in IW mode, over Ireland where a large sinkhole occurred on 24 September 2018. The Sinkhole Scanner was able to identify a pattern of low posterior variance zones consistent with the simulated set. In case of the real data, it is able to identify significantly low posterior variance zones near the sinkhole area with the lowest value being 51.1% of the maximum value. The results from Sinkhole Scanner over the real sinkhole site were compared with Multiple Hypothesis Testing (MHT), which identifies Breakpoint and Heaviside temporal anomalies in the deformation time series of CCS. MHT was able to identify high likelihood for Heaviside anomalies in deformation time series of CCS near the sinkhole site about 10 epochs before the sinkhole occurrence. We show that the Sinkhole Scanner is efficient in monitoring a large area and search for sinkholes and that MHT can be used successively to identify temporal anomalies in the vicinity of areas detected by the Sinkhole Scanner. Future research may address other Sinkhole shapes whereas the underlying stochastic model may be adjusted. We conclude that the Sinkhole Scanner is important to be applied at different levels of scale to converge on potential sinkhole centers. Full article
(This article belongs to the Special Issue Remote Sensing-Based Monitoring and Modeling of Ground Movements)
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24 pages, 9277 KiB  
Article
Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas
by Juan López-Vinielles, José A. Fernández-Merodo, Pablo Ezquerro, Juan C. García-Davalillo, Roberto Sarro, Cristina Reyes-Carmona, Anna Barra, José A. Navarro, Vrinda Krishnakumar, Massimiliano Alvioli and Gerardo Herrera
Remote Sens. 2021, 13(10), 2008; https://doi.org/10.3390/rs13102008 - 20 May 2021
Cited by 23 | Viewed by 4360
Abstract
Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The [...] Read more.
Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The development of slope monitoring and modeling programs usually entails the exploitation of in situ and remote sensing data, together with the application of numerical modeling, and it plays an important role in the definition of prevention and mitigation measures aimed at minimizing the impact of slope failures in mining areas. In this paper, a new methodology is presented; one that combines satellite radar interferometry and 2D finite element modeling for slope stability analysis at a regional scale, and applied within slope unit polygons. Although the literature includes many studies applying radar interferometry and modeling for slope stability analysis, the addition of slope units as input data for radar interferometry and modeling purposes has, to our knowledge, not previously been reported. A former mining area in southeast Spain was studied, and the method proved useful for detecting and characterizing a large number of unstable slopes. Out of the 1959 slope units used for the spatial analysis of the radar interferometry data, 43 were unstable, with varying values of safety factor and landslide size. Out of the 43 active slope units, 21 exhibited line of sight velocities greater than the maximum error obtained through validation analysis (2.5 cm/year). Finally, this work discusses the possibility of using the results of the proposed approach to devise a proxy for landslide hazard. The proposed methodology can help to provide non-expert final users with intelligible, clear, and easily comparable information to analyze slope instabilities in different settings, and not limited to mining areas. Full article
(This article belongs to the Special Issue Remote Sensing-Based Monitoring and Modeling of Ground Movements)
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