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Remote Sensing
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Advances in GNSS for Time Series Analysis
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Advances in GNSS for Time Series Analysis

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 3273

Special Issue Editors

Prof. Dr. Junping Chen

E-Mail Website
Guest Editor
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Interests: GNSS data analysis including POD; clock analysis; SBAS; PPP; PPP-RTK; LEO augmentation; reference frame and Geodynamics
Dr. Alfonso Vitti

E-Mail Website
Guest Editor
Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38122 Trento, Italy
Interests: methods for spatial and temporal filtering of remote sensed data and digital surface models; GNSS for water resources; SAR and optical remote sensing for river and lake science
Special Issues, Collections and Topics in MDPI journals
Dr. Weijie Tan

E-Mail Website
Guest Editor
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Interests: tectonics; geodynamics; crustal deformation mechanism; GNSS timeseries analysis
Dr. Nannan Guo

E-Mail Website
Guest Editor
The First Monitoring and Application Center, China Earthquake Administration, China
Interests: crustal deformation characteristics; InSAR precision data processing; geodetic data fusion method; 3D crustal deformation field

Special Issue Information

Dear Colleagues,

Recently, GNSS has preliminarily determined the basic characteristics of continental crustal movements. GNSS station coordinates time series which usually contains linear and nonlinear signals caused by tectonic movements, annual variations caused by mass loads, co-seismic and post-seismic deformation, the inflation and eruption of active volcanoes, glacial-isostatic adjustment and its effects on relative sea level, as well as uneliminated systematic errors in GNSS data processing. However, real-time earth deformation monitoring at different temporal and spatial scales requires higher GNSS precision. How to distinguish the above signals from other geophysical processes, especially for transient and weak signals related to tectonic deformation, and obtain high precision crustal movement information, is an urgent problem in the current coordinate time series analysis.

This Special Issue aims to cover different uses of GNSS in geophysics and geodesy. Topics may cover anything from GNSS time series analysis methods, signals observed by GNSS, noise analysis in GNSS timeseries, etc. Hence, multisource data integration, multiscale approaches, or studies focused on GNSS timeseries analysis, among other issues, are welcome. Articles may address, but are not limited to, the following topics:

  • GNSS coordinate timeseries analysis;
  • Detection and analysis of signals derived from geodetic time series data;
  • Active crustal deformation processes observed by GNSS;
  • Multivariate statistical techniques application on GNSS;
  • Noise in GPS position time-series.

Prof. Dr. Junping Chen
Dr. Alfonso Vitti
Dr. Weijie Tan
Dr. Nannan Guo
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

  • non-linear crustal deformation
  • common mode error
  • independent component analysis
  • noise analysis
  • mass loadings

Published Papers (3 papers)

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Research

21 pages, 9404 KiB  
Article
Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model
by Yilin Xie, Xiaolin Meng, Jun Wang, Haiyang Li, Xun Lu, Jinfeng Ding, Yushan Jia and Yin Yang
Remote Sens. 2024, 16(10), 1767; https://doi.org/10.3390/rs16101767 - 16 May 2024
Viewed by 191
Abstract
Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory [...] Read more.
Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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18 pages, 6594 KiB  
Article
Python Software Tool for Diagnostics of the Global Navigation Satellite System Station (PS-NETM)–Reviewing the New Global Navigation Satellite System Time Series Analysis Tool
by Stepan Savchuk, Petro Dvulit, Vladyslav Kerker, Daniel Michalski and Anna Michalska
Remote Sens. 2024, 16(5), 757; https://doi.org/10.3390/rs16050757 - 21 Feb 2024
Viewed by 654
Abstract
The time series of GNSS coordinates contain signals caused by the age-related movement of tectonic plates, the deformation of the Earth’s surface, as well as errors at different time scales from sub-daily tidal deformation to the long-term deformation of the surface load. Depending [...] Read more.
The time series of GNSS coordinates contain signals caused by the age-related movement of tectonic plates, the deformation of the Earth’s surface, as well as errors at different time scales from sub-daily tidal deformation to the long-term deformation of the surface load. Depending on the nature of the signal, specific approaches are used for both the visual interpretation and pre-processing of time series and their statistical analysis. However, none of the present software analyzes the nature of the residual errors but assumes their random nature and obedience to the classical normal distribution. One of the methods for analyzing the time series of coordinates with residual, unaccounted-for systematic errors is the non-classical error theory of measurements. The result of this work is a developed software solution for analyzing the time series of GNSS coordinates to test their normality, or in other words, to test whether a particular GNSS station is subject to the influence of small, unaccounted-for errors. Conclusions: After testing our software on four reference stations in Europe, we concluded that none of the chosen stations followed the normal law of distribution; thus, it is vital to perform such tests before conducting any experiments on the time series from reference stations. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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22 pages, 7826 KiB  
Article
An Improved VMD-LSTM Model for Time-Varying GNSS Time Series Prediction with Temporally Correlated Noise
by Hongkang Chen, Tieding Lu, Jiahui Huang, Xiaoxing He, Kegen Yu, Xiwen Sun, Xiaping Ma and Zhengkai Huang
Remote Sens. 2023, 15(14), 3694; https://doi.org/10.3390/rs15143694 - 24 Jul 2023
Cited by 4 | Viewed by 1886
Abstract
GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and the maintenance of the global coordinate framework. Long short-term memory (LSTM) is a deep learning model that has been widely applied in the field of high-precision time [...] Read more.
GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and the maintenance of the global coordinate framework. Long short-term memory (LSTM) is a deep learning model that has been widely applied in the field of high-precision time series prediction and is often combined with Variational Mode Decomposition (VMD) to form the VMD-LSTM hybrid model. To further improve the prediction accuracy of the VMD-LSTM model, this paper proposes a dual variational modal decomposition long short-term memory (DVMD-LSTM) model to effectively handle noise in GNSS time series prediction. This model extracts fluctuation features from the residual terms obtained after VMD decomposition to reduce the prediction errors associated with residual terms in the VMD-LSTM model. Daily E, N, and U coordinate data recorded at multiple GNSS stations between 2000 and 2022 were used to validate the performance of the proposed DVMD-LSTM model. The experimental results demonstrate that, compared to the VMD-LSTM model, the DVMD-LSTM model achieves significant improvements in prediction performance across all measurement stations. The average RMSE is reduced by 9.86% and the average MAE is reduced by 9.44%; moreover, the average R2 increased by 17.97%. Furthermore, the average accuracy of the optimal noise model for the predicted results is improved by 36.50%, and the average velocity accuracy of the predicted results is enhanced by 33.02%. These findings collectively attest to the superior predictive capabilities of the DVMD-LSTM model, thereby demonstrating the reliability of the predicted results. Full article
(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)
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