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Dam Stability Monitoring with Satellite Geodesy II
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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: 31 December 2025 | Viewed by 5780

Special Issue Editors

Prof. Dr. Antonio Miguel Ruiz Armenteros

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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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Prof. Dr. Roberto Tomás

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Guest Editor
Department of Civil Engineering, University of Alicante, Campus de San Vicente del Raspeig s/n, 03080 Alicante, Spain
Interests: rock mechanics; landslides; infrastructures; remote sensing; geotechnical engineering
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Dr. Joaquim João Sousa

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Guest Editor
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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Prof. Dr. M. Clara de Lacy

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Guest Editor
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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Prof. Dr. Zhenhong Li

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Guest Editor
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China
Interests: synthetic aperture radar (SAR); interferometric SAR (InSAR); time series; precision agriculture
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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

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

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

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Related Special Issue

Published Papers (6 papers)

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Research

26 pages, 7238 KiB  
Article
Towards Operational Dam Monitoring with PS-InSAR and Electronic Corner Reflectors
by Jannik Jänichen, Jonas Ziemer, Marco Wolsza, Daniel Klöpper, Sebastian Weltmann, Carolin Wicker, Katja Last, Christiane Schmullius and Clémence Dubois
Remote Sens. 2025, 17(7), 1318; https://doi.org/10.3390/rs17071318 - 7 Apr 2025
Cited by 1 | Viewed by 489
Abstract
Dams are crucial for ensuring water and electricity supply, while also providing significant flood protection. Regular monitoring of dam deformations is of vital socio-economic and ecological significance. In Germany, dams must be constructed and operated according to generally accepted rules of engineering. The [...] Read more.
Dams are crucial for ensuring water and electricity supply, while also providing significant flood protection. Regular monitoring of dam deformations is of vital socio-economic and ecological significance. In Germany, dams must be constructed and operated according to generally accepted rules of engineering. The safety concept for dams based on these rules relies on structural safety, professional operation and maintenance, safety monitoring, and precautionary measures. Rather time-consuming in situ techniques have been employed for these measurements, which permit monitoring deformations with either high spatial or temporal resolution, but not both. As a means of measuring large-scale deformations in the millimeter range, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique of Persistent Scatterer Interferometry (PSI) is already being applied in various fields. However, when considering the operational monitoring of dams using PSI, specific characteristics need to be considered. For example, the geographical location of the dam in space, as well as its shape, size, and land cover. All these factors can affect the visibility of the structure for the use with PSI and, in certain cases, limit the applicability of SAR data. The visibility of dams for PSI monitoring is often limited, particularly in cases where observation is typically not feasible due to factors such as geographical and structural characteristics. While corner reflectors can improve visibility, their large size often makes them unsuitable for dam infrastructure and may raise concerns with heritage protection for listed dams. Addressing these challenges, electronic corner reflectors (ECRs) offer an effective alternative due to their small and compact size. In this study, we analyzed the strategic placement of ECRs on dam structures. We developed a new CR Index, which identifies areas where PSI alone is insufficient due to unfavorable geometric or land use conditions. This index categorizes visibility potential into three classes, presented in a ‘traffic light’ map, and is instrumental in selecting optimal installation sites. We furthermore investigated the signal stability of ECRs over an extended observation period, considering the Amplitude Dispersion Index (ADI). It showed values between 0.1 and 0.4 for many dam structures, which is comparable to normal corner reflectors (CRs), confirming the reliability of these signals for PSI analysis. This work underscores the feasibility of using ECRs to enhance monitoring capabilities at dam infrastructure. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
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20 pages, 1530 KiB  
Article
Assessing the Feasibility of Persistent Scatterer Data for Operational Dam Monitoring in Germany: A Case Study
by Jonas Ziemer, Jannik Jänichen, Carolin Wicker, Daniel Klöpper, Katja Last, Andre Kalia, Thomas Lege, Christiane Schmullius and Clémence Dubois
Remote Sens. 2025, 17(7), 1202; https://doi.org/10.3390/rs17071202 - 28 Mar 2025
Cited by 1 | Viewed by 375
Abstract
Multi-temporal synthetic aperture radar interferometry (MT-InSAR) has evolved from a niche research technique into a powerful global monitoring tool. With the launch of nationwide and continent-wide ground motion services (GMSs), freely available deformation data can now be analyzed on a large scale. However, [...] Read more.
Multi-temporal synthetic aperture radar interferometry (MT-InSAR) has evolved from a niche research technique into a powerful global monitoring tool. With the launch of nationwide and continent-wide ground motion services (GMSs), freely available deformation data can now be analyzed on a large scale. However, their applicability for monitoring critical infrastructure, such as dams, has not yet been thoroughly assessed, and several challenges have hindered the integration of MT-InSAR into existing monitoring frameworks. These challenges include technical limitations, difficulties in interpreting deformation results, and the rigidity of existing safety protocols, which often restrict the adoption of remote sensing techniques for operational dam monitoring. This study evaluates the effectiveness of persistent scatterer (PS) data from the German ground motion service (Bodenbewegungsdienst Deutschland, BBD) in complementing time-consuming in situ techniques. By analyzing a gravity dam in Germany, BBD time series were compared with in situ pendulum data. We propose a two-stage assessment procedure: First, we evaluate the dam’s suitability for PS analysis using the CR-Index to identify areas with good radar visibility. Second, we assess the interpretability of BBD data for radial deformations by introducing a novel index that quantifies the radial sensitivity of individual PS points on the dam. This index is universally applicable and can be transferred to other types of infrastructure. The results revealed a fair correlation between PS deformations and pendulum data for many PS points (up to R2 = 0.7). A priori feasibility assessments are essential, as factors such as topography, land cover, and dam type influence the applicability of the PS technique. The dam’s orientation relative to the look direction of the sensor emerged as a key criterion for interpreting radial deformations. For angle differences (ΔRAD) of up to 20° between the true north radial angle of a PS point and the satellite’s look direction, the line-of-sight (LOS) sensitivity accounts for approximately 50 to 70% of the true radial deformation, depending on the satellite’s incidence angle. This criterion is best fulfilled by dams aligned in a north–south direction. For the dam investigated in this study, the LOS sensitivity to radial deformations was low due to its east–west orientation, resulting in significantly higher errors (6 mm ≤RMSE≤43 mm) compared to in situ pendulum data. Eliminating PS points with an unfavorable alignment with the sensor should be considered before interpreting radial deformations. For implementation into operational monitoring programs, greater effort must be spent on near-real-time updates of BBD datasets. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
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19 pages, 1233 KiB  
Article
Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach
by Jonas Ziemer, Gideon Stein, Carolin Wicker, Jannik Jänichen, Daniel Klöpper, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh and Clémence Dubois
Remote Sens. 2025, 17(6), 1026; https://doi.org/10.3390/rs17061026 - 15 Mar 2025
Viewed by 581
Abstract
Deformation monitoring is a critical task for dam operators to guarantee safe operation. Given an increasing number of extreme weather events caused by climate change, the precise prediction of dam deformations has become increasingly important. Traditionally, multiple linear regression models have been employed, [...] Read more.
Deformation monitoring is a critical task for dam operators to guarantee safe operation. Given an increasing number of extreme weather events caused by climate change, the precise prediction of dam deformations has become increasingly important. Traditionally, multiple linear regression models have been employed, utilizing in situ data from pendulum systems or trigonometric measurements. These methods sometimes suffer from sparse data, which typically represent deformations only at specific points on the dam, if such data are available at all. Technical advances in multi-temporal synthetic aperture radar interferometry (MT-InSAR), particularly Persistent Scatterer Interferometry (PSI), address these limitations by enabling monitoring in high spatial and temporal resolution, capturing dam deformations with millimeter precision, and providing extensive spatial coverage. This study advances traditional methods of dam monitoring by employing data-driven techniques and integrating Sentinel-1 C-band Persistent Scatterer (PS) time series alongside in situ data. Through a comprehensive evaluation of advanced data-driven approaches, we demonstrated considerable improvements in predicting dam deformations and evaluating their drivers. The analysis provided evidence for the following insights: First, the accuracy of current modeling approaches can be greatly improved by utilizing advanced feature engineering and data-driven model selection. The prediction performance of the pendulum data was improved by utilizing data-driven algorithms, reducing the mean absolute error from 0.51 mm in the baseline model (R2 = 0.92) to as low as 0.05 mm using the full model search space (R2 = 0.99). Although the model accuracy for the PS datasets (MAEmax: 0.81 mm) was about one order of magnitude lower than that for pendulum data, the mean absolute errors could be reduced by up to 0.25 mm. Second, by incorporating freely available PS time series into deformation prediction, dams can be monitored in higher spatial resolution, making PSI a valuable tool for dam operators. This requires adequate dataset filtering to eliminate noisy PS points. Third, extended representations of water level and temperature, including interaction effects, can improve model accuracy and reduce prediction errors. With these insights, we recommend incorporating the proposed methodology into the monitoring program of gravity dams to enhance the accuracy in predicting their expected deformations. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
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17 pages, 6915 KiB  
Article
Dam Deformation Data Preprocessing with Optimized Variational Mode Decomposition and Kernel Density Estimation
by Siyu Chen, Chaoning Lin, Yanchang Gu, Jinbao Sheng and Mohammad Amin Hariri-Ardebili
Remote Sens. 2025, 17(4), 718; https://doi.org/10.3390/rs17040718 - 19 Feb 2025
Cited by 1 | Viewed by 478
Abstract
Deformation is one of the critical response quantities reflecting the structural safety of dams. To enhance outlier identification and denoising in dam deformation monitoring data, this study proposes a novel preprocessing method based on optimized Variational Mode Decomposition (VMD) and Kernel Density Estimation [...] Read more.
Deformation is one of the critical response quantities reflecting the structural safety of dams. To enhance outlier identification and denoising in dam deformation monitoring data, this study proposes a novel preprocessing method based on optimized Variational Mode Decomposition (VMD) and Kernel Density Estimation (KDE). The approach systematically processes data in three steps: First, VMD decomposes raw data into intrinsic mode functions without recursion. The parallel Jaya algorithm is used to adaptively optimize VMD parameters for improved decomposition. Second, the intrinsic mode functions containing outlier and noise characteristics are identified and separated using sample entropy and correlation coefficients. Finally, KDE thresholds are applied for outlier localization, while a data superposition method ensures effective denoising. Validation using simulated deformation data and Global Navigation Satellite Systems (GNSS)-based observed horizontal deformation from dam engineering demonstrates the method’s robustness in accurately identifying outliers and denoising data, achieving superior preprocessing performance. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
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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
Cited by 1 | Viewed by 1102
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
Cited by 3 | Viewed by 1789
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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