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Applications of InSAR for Monitoring Surface Deformation in the Energy Transition

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 3844

Special Issue Editors

Dr. Damien Dhont

E-Mail Website
Guest Editor
TotalEnergies E&P Uganda, Geosciences & Reservoir Department, Kampala, Uganda
Interests: geological monitoring; remote sensing; energy
Dr. Marine Larrey

E-Mail Website
Guest Editor
CLS Group (Collecte Localisation Satellites) Remote Sensing Engineer - Post Processing Operator - TRE ALTAMIRA, Toulouse, Occitanie, France
Interests: geology; remote sensing; InSAR

Special Issue Information

Dear Colleagues,

The increased worldwide energy demand and recent climate change challenges require the development of sustainable energy resources. New technologies, improved industrial processes, and currently unexplored systems will play a pivotal role in this energy transition. The study of their associated impacts by monitoring different parameters is fundamental to grant sustainability and social responsibility.

In this framework, measuring surface deformation, both natural and possibly induced by human activities, is key to ensuring such development and mitigating environmental and societal risks.

The extraction of subsurface resources, notably through mining, oil and gas, and geothermal processes, can result in surface deformation leading to subsidence, landslide, or other phenomena. Induced earthquakes also represent significant hazards for people, infrastructures, and the environment. The geological storage of greenhouse gas as a tool to limit global warming also requires persistent monitoring of the surface dynamics, both for a better understanding of the geomechanical behaviour of rocks and for public safety.

Synthetic aperture radar interferometry (InSAR) has evolved into a mature technology to quantify ground movement with millimetric accuracy and with no direct environmental footprint. The ability to process images both ex-post and ex-ante represents a significant asset for monitoring past, current, and future surface deformation. InSAR becomes even more indispensable when surface deformation measurements are sought at large scales, playing a key role in globally protecting and preserving our planet and its environment.

This Special Issue aims to collect original research and review papers focusing on the application of InSAR for monitoring surface deformation in the energy transition process. We invite contributions that leverage the advances in InSAR technology, processing, and analysis to foster new developments in this domain. We welcome submissions in the following categories or any topic using InSAR, including but not limited to: surface subsidence or uplift caused by underground extraction or storage; exploitation of new energies including hydrogen, solar and wind; and the development of new tools and deployment of advanced algorithms to large datasets for wide-area monitoring (e.g., country scale), operational safety, risk and hazard management in the energy sector.

Dr. Damien Dhont
Dr. Marine Larrey
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
  • InSAR
  • energy
  • monitoring
  • exploration
  • storage
  • CCS
  • surface deformation
  • subsidence
  • environment

Published Papers (3 papers)

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Research

18 pages, 24661 KiB  
Article
Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR
by Sichun Long, Maoqi Liu, Chaohui Xiong, Tao Li, Wenhao Wu, Hongjun Ding, Liya Zhang, Chuanguang Zhu and Shide Lu
Remote Sens. 2023, 15(23), 5546; https://doi.org/10.3390/rs15235546 - 28 Nov 2023
Viewed by 639
Abstract
The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to [...] Read more.
The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data. Full article
(This article belongs to the Special Issue Applications of InSAR for Monitoring Surface Deformation in the Energy Transition)
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18 pages, 5527 KiB  
Article
Subsidence of a Coal Ash Landfill in a Power Plant Observed by Applying PSInSAR to Sentinel-1 SAR Data
by Youngnam Shin and Hoonyol Lee
Remote Sens. 2023, 15(17), 4127; https://doi.org/10.3390/rs15174127 - 23 Aug 2023
Viewed by 853
Abstract
We analyzed ground subsidence at the coal ash disposal sites of Stanton Energy Center, a power plant located in Orlando, Florida, USA, by applying 157 Sentinel-1 SAR images obtained between May 2017 and December 2022 in ascending orbit to the PSInSAR technique. A [...] Read more.
We analyzed ground subsidence at the coal ash disposal sites of Stanton Energy Center, a power plant located in Orlando, Florida, USA, by applying 157 Sentinel-1 SAR images obtained between May 2017 and December 2022 in ascending orbit to the PSInSAR technique. A LiDAR DEM with 1 m posting was used for the DInSAR and StaMPS processing for PSInSAR. The results showed significant ground subsidence on the area where solar panels were installed on top of the coal ash landfill. The coal ash landfill was divided into three sites (A, B, and C) according to the landfill sequence. The spatially averaged PSInSAR showed subsidence rates of 7.3 mm/year, 6.2 mm/year, and 8.8 mm/year in sites A, B, and C, respectively. In particular, relatively newly deposited sites A and B showed a decreasing trend in subsidence rate with higher quadratic components in regression function, indicating a stabilization of the subsidence. On the other hand, the oldest site C exhibited the highest (and a relatively constant) subsidence rate, suggesting that the settlement occurred earlier and is now at a constant rate. It is also suspected that new dumping activity near C might have caused a higher subsidence rate than in sites A and B. No subsidence occurred at other solar panel installations on the ground outside the landfill, suggesting that the subsidence was caused by the gravitational compaction of the landfill materials rather than by the instability of the solar facilities. Comparison of PSInSAR results with lower resolution DEMs, such as SRTM and Copernicus DEM, showed range errors of the PS positions proportional to the height deviation from LiDAR DEM, highlighting the importance of accurate DEMs for the time-series analysis of SAR data. Full article
(This article belongs to the Special Issue Applications of InSAR for Monitoring Surface Deformation in the Energy Transition)
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24 pages, 19561 KiB  
Article
Integrating SBAS-InSAR and AT-LSTM for Time-Series Analysis and Prediction Method of Ground Subsidence in Mining Areas
by Yahong Liu and Jin Zhang
Remote Sens. 2023, 15(13), 3409; https://doi.org/10.3390/rs15133409 - 5 Jul 2023
Cited by 3 | Viewed by 1737
Abstract
Ground subsidence is a significant safety concern in mining regions, making large-scale subsidence forecasting vital for mine site environmental management. This study proposes a deep learning-based prediction approach to address the challenges posed by the existing prediction methods, such as complicated model parameters [...] Read more.
Ground subsidence is a significant safety concern in mining regions, making large-scale subsidence forecasting vital for mine site environmental management. This study proposes a deep learning-based prediction approach to address the challenges posed by the existing prediction methods, such as complicated model parameters or large data requirements. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was utilized to collect spatiotemporal ground subsidence data at the Pingshuo mining area from 2019 to 2022, which was then analyzed using the long-short term memory (LSTM) neural network algorithm. Additionally, an attention mechanism was introduced to incorporate temporal dependencies and improve prediction accuracy, leading to the development of the AT-LSTM model. The results demonstrate that the Pingshuo mine area had subsidence rates ranging from −205.89 to −59.70 mm/yr from 2019 to 2022, with subsidence areas mainly located around Jinggong-1 (JG-1) and the three open-pit mines, strongly linked to mining activities, and the subsidence range continuously expanding. The spatial distribution of the AT-LSTM prediction results is basically consistent with the real situation, and the correlation coefficient is more than 0.97. Compared with the LSTM, the AT-LSTM method better captured the fluctuation changes of the time series for fitting, while the model was more sensitive to the mining method of the mine, and had different expressiveness in open-pit and shaft mines. Furthermore, in comparison to existing time-series forecasting methods, the AT-LSTM is effective and practical. Full article
(This article belongs to the Special Issue Applications of InSAR for Monitoring Surface Deformation in the Energy Transition)
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