Assessment of orthometric height determination utilizing network of multi-baselines of GNSS Continuously Operating Reference Stations
-
Chaiyaporn Kitpracha
Abstract
Global Navigation Satellite Systems (GNSS) offer a modern, efficient alternative to traditional leveling methods, with advantages in accuracy, cost-effectiveness, and adaptability to diverse terrains. This study examines GNSS leveling techniques using Thailand’s Continuously Operating Reference Station (CORS) network. A multi-baseline network approach is proposed to enhance redundancy, enabling weighted least squares adjustment for both absolute and relative methods. The performance of the multi-baseline approach is compared to the single-baseline method to assess its benefits. Results indicate that the multi-baseline absolute method achieves an accuracy of approximately 4 cm, surpassing the single-baseline absolute method and demonstrating a robustness to orthometric height errors caused by reference station anomalies. On the other hand, the relative method shows degraded performance for both single and multi-baseline approaches due to biases in antenna eccentricity, which necessitate reducing the ellipsoidal height at the marker point to the Permanent Bench Mark (PBM) level.
Funding source: Chula Engineering's promoting research grant
Award Identifier / Grant number: 2210042000
Acknowledgments
We would like to thank our colleagues in the Department of Survey Engineering, Chulalongkorn University Bangkok, Thailand, who helped us in fieldwork to collect GNSS observations. We would also like to thank anonymous reviewers for their comments, which helped to improve the paper.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. CK did most of the data analysis and writing of the manuscript. PB participated in the design of the experiment and collecting the data. CS contributed to discussion of the results and improving the manuscript. All the authors read and approved the final manuscript.
-
Use of Large Language Models, AI and Machine Learning Tools: This manuscript only used AI to improve language.
-
Conflict of interest: The author states no conflict of interest.
-
Research funding: This article is financially supported by Chula Engineering's promoting research grant (grant No. 2210042000) from the Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
-
Data availability: Not applicable.
References
1. Hofmann-Wellenhof, B, Moritz, H. Physical geodesy. Vienna: Springer Science & Business Media; 2006.Suche in Google Scholar
2. Savchuk, S, Fedorchuk, A. Methodology for local correction of the heights of global geoid models to improve the accuracy of GNSS leveling. Geodesy Geodyn 2024;15:42–9. https://doi.org/10.1016/j.geog.2023.02.005.Suche in Google Scholar
3. Dumrongchai, P, Srimanee, C, Duangdee, N, Bairaksa, J. The determination of Thailand geoid model 2017 (TGM2017) from airborne and terrestrial gravimetry. Terr Atmos Ocean Sci 2021;32:8–872. https://doi.org/10.3319/tao.2021.08.23.01.Suche in Google Scholar
4. Zilkoski, DB. Establishing orthometric heights using GNSS — part 1. GPS World; 2015. Available from: https://www.gpsworld.com/establishing-orthometric-heights-using-gnss-part-1/.Suche in Google Scholar
5. Li, YS, Ning, FS. Research into GNSS levelling using network RTK in Taiwan. Surv Rev 2019;51:17–25. https://doi.org/10.1080/00396265.2017.1340130.Suche in Google Scholar
6. Zilkoski, D, Carlson, E, Smith, C. Guidelines for establishing GPS-derived orthometric heights (standards: 2 cm and 5 cm) version 1.4. Nat Geodetic Surv 2005;58:45–6.Suche in Google Scholar
7. Kubodera, T, Okazawa, H, Hosokawa, Y, Kawana, F, Matsuo, E, Mihara, M. Effects of surveying methods between GNSS and direct leveling on elevation values over long distance in mountainous area. Int J Environ Rural Dev 2016;7:62–9.Suche in Google Scholar
8. Jekeli, C, Montenbruck, O. Time and reference systems. Cham: Springer International Publishing; 2017:25–58 pp.10.1007/978-3-319-42928-1_2Suche in Google Scholar
9. Torge, W, Müller, J, Pail, R. Geodesy. Berlin, Boston: De Gruyter Oldenbourg; 2023.10.1515/9783110723304Suche in Google Scholar
10. Verhagen, S, Teunissen, PJG. Least-squares estimation and Kalman filtering. Cham: Springer International Publishing; 2017:639–60 pp.10.1007/978-3-319-42928-1_22Suche in Google Scholar
11. Tenzer, R, Chen, W, Rathnayake, S, Pitoňák, M. The effect of anomalous global lateral topographic density on the geoid-to-quasigeoid separation. J Geod 2021;95:12. https://doi.org/10.1007/S00190-020-01457-6.Suche in Google Scholar
12. Dach, R, Lutz, S, Walser, P, Fridez P Bernese GNSS software version 5.2. User manual. Bern: Astronomical Institute, University of Bern, Bern Open Publishing; 2015.Suche in Google Scholar
13. Johnston, G, Riddell, A, Hausler, G. The international GNSS service. In: Teunissen, P, Montenbruck, O, editors. Springer handbook of global navigation satellite systems. Cham: Springer Handbooks, Springer; 2017.10.1007/978-3-319-42928-1_33Suche in Google Scholar
14. Altamimi, Z, Rebischung, P, Collilieux, X, Métivier, L, Chanard, K. ITRF2020: an augmented reference frame refining the modeling of nonlinear station motions. J Geod 2023;97:47. https://doi.org/10.1007/s00190-023-01738-w.Suche in Google Scholar
15. Dach, R, Schaer, S, Arnold, D, Brockmann, E, Kalarus, M, Lasser, M, et al.. CODE final product series for the IGS [Dataset]. Bern: Astronomical Institute, University of Bern; 2024.Suche in Google Scholar
16. Petit, G, Luzum, B. IERS conventions (2010); 2010. Available from: http://www.iers.org/TN36/.Suche in Google Scholar
17. Landskron, D, Böhm, J. VMF3/GPT3: refined discrete and empirical troposphere mapping functions. J Geod 2018;92:349–60. https://doi.org/10.1007/s00190-017-1066-2.Suche in Google Scholar PubMed PubMed Central
18. Lyard, F, Lefevre, F, Letellier, T, Francis, O. Modelling the global ocean tides: modern insights from FES2004. Ocean Dyn 2006;56:394–415. https://doi.org/10.1007/s10236-006-0086-x.Suche in Google Scholar
19. Ray, RD, Ponte, RM. Barometric tides from ECMWF operational analyses. Ann Geophys 2003;21:1897–910. https://doi.org/10.5194/angeo-21-1897-2003.Suche in Google Scholar
20. Desai, SD, Sibois, AE. Evaluating predicted diurnal and semidiurnal tidal variations in polar motion with GPS-based observations. J Geophys Res Solid Earth 2016;121:5237–56. https://doi.org/10.1002/2016jb013125.Suche in Google Scholar
21. Odijk, D, Wanninger, L. Differential positioning. Cham: Springer International Publishing; 2017:753–80 pp.10.1007/978-3-319-42928-1_26Suche in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
