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Dam Stability Monitoring with Satellite Geodesy II

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2478

Special Issue Editors


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Departamento de Ingeniería Cartográfica, Geodésica y Fotogrametría, Universidad de Jaén, Edificio de Ingeniería y Tecnología A3, Campus Las Lagunillas s/n, 23071 Jaén, Spain
Interests: deformation monitoring; InSAR; MT-InSAR; GNSS; geodesy; remote sensing
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Engineering Department, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: UAV; image processing algorithms (RGB, NIR, multi- and hyperspectral, thermal and LiDAR sensors); InSAR; precision agriculture; precision forestry
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Department of Mathematics and Data Science. University CEU San Pablo Julián Romea, 23, 28003 Madrid, Spain
Interests: GNSS (global navigation satellite system); Galileo; geodesy; deformation monitoring; geoid
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Special Issue Information

Dear Colleagues,

Monitoring the structural integrity of dams is critical to ensuring their safe condition and maintaining their operational functions. Dam failures can lead to significant social, economic, and environmental consequences, posing significant risk to people, communities, infrastructures, and nature. Therefore, it is imperative to maintain ongoing surveillance and safety programs to identify critical situations that could result in catastrophic infrastructure damage and loss of life. Occasionally, failures may not lead to complete dam collapse, but they can still jeopardize operational conditions, causing substantial economic losses. This can occur, for example, due to interruptions in energy production or related activities like hydraulic regulation and water storage.

Due to the complexity of dams, the use of multiple sensors is required for their monitoring. Each sensor is designed and installed to focus on specific areas of the dam, the slopes surrounding the reservoir, or the structures related to public services. Monitoring serves not only to provide early warnings of potential collapses but also valuable data for verifying design parameters, investigating the reasons behind deformation processes, and learning essential lessons for implementation in future projects.

Although the deformation monitoring of this man-made infrastructure is mandatory and undeniably accurate and reliable, it is usually time-consuming and expensive. Monitoring measurements involve the establishment of classical geodetic networks (triangulations/trilaterations and leveling), GNSS networks for monitoring the structure and surrounding areas, the inclusion of geotechnical/structural sensors to measure local deformations and other physical quantities, and the application of other remote sensing techniques using ground-based and satellite platforms, such as terrestrial laser scanning (TLS), ground-based synthetic aperture radar (GBSAR), or spaceborne SAR interferometry (InSAR).

Currently, integrated monitoring systems combine information from several sources to monitor different processes that may impact structural stability or to cross-validate different results. Space geodetic techniques offer significant advantages over conventional geodetic techniques, making them efficient monitoring methods in terms of time and cost. In particular, the use of GNSS and InSAR techniques, together with the high availability of medium- and high-spatial-resolution images from the latest generation of SAR constellations with shorter revisit times and the continuous development of algorithms for time series analysis, aims to accelerate the collection of results and their reliability.

In summary, advancements in measuring instruments, computer science, and global Earth observation systems have improved the methods of analysis and computation for stability monitoring in civil engineering. The goal of this Special Issue is to promote satellite geodesy as a tool for monitoring dams by collecting success cases in which these monitoring techniques, either alone or in combination with other techniques, allow deformations in this type of structure to be detected.

We look forward to receiving your contribution to this Special Issue on “Dam Stability Monitoring with Satellite Geodesy”.

Prof. Dr. Antonio Miguel Ruiz Armenteros
Prof. Dr. Roberto Tomás
Dr. Joaquim João Sousa
Prof. Dr. M. Clara de Lacy
Prof. Dr. Zhenhong Li
Guest Editors

Manuscript Submission Information

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Keywords

  • dam
  • satellite geodesy
  • remote sensing
  • GNSS
  • radar interferometry
  • InSAR
  • deformation monitoring
  • geodetic measurements
  • geotechnical measurements
  • infrastructure
  • earth observation
  • sensors

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Published Papers (2 papers)

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Research

19 pages, 8158 KiB  
Article
A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network
by Xiwen Sun, Tieding Lu, Shunqiang Hu, Haicheng Wang, Ziyu Wang, Xiaoxing He, Hongqiang Ding and Yuntao Zhang
Remote Sens. 2024, 16(21), 3978; https://doi.org/10.3390/rs16213978 - 26 Oct 2024
Viewed by 585
Abstract
To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory [...] Read more.
To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
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19 pages, 9836 KiB  
Article
Measuring Dam Deformation of Long-Distance Water Transfer Using Multi-Temporal Synthetic Aperture Radar Interferometry: A Case Study in South-to-North Water Diversion Project, China
by Ruya Xiao, Xiaoyuan Gao, Xun Wang, Shanshui Yuan, Zhou Wu and Xiufeng He
Remote Sens. 2024, 16(2), 365; https://doi.org/10.3390/rs16020365 - 16 Jan 2024
Viewed by 1317
Abstract
Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional [...] Read more.
Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam’s substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
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