Науковий вісник НГУ

Large-scale topographic mapping of vegetation areas based on UAV and GNSS technology

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №1 2026

Last Updated on 27 February 2026

Published on 30 November -0001

Hits: 2340

Authors:

Thuy Thi Hoang, orcid.org/0009-0005-5181-9379, Hanoi University of Mining and Geology, Faculty of Geomatics and Land Administration, Hanoi, Socialist Republic of Vietnam

Anh Tuan Luu*, orcid.org/0009-0001-7738-9718, Hanoi University of Mining and Geology, Faculty of Geomatics and Land Administration, Hanoi, Socialist Republic of Vietnam, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (1): 120 - 129

https://doi.org/10.33271/nvngu/2026-1/120

Abstract:

Purpose. To develop a method for generating accurate large-scale topographic maps in densely vegetated areas using UAV (Unmanned Aerial Vehicle) and GNSS (Global Navigation Satellite System) technologies. The focus is on correcting elevation data from UAV imagery through a polynomial model based on CORS checkpoints.

Methodology. The research was conducted in Mai Pha commune, Lang Son province, Vietnam. A DJI Phantom 3 Pro UAV was used to capture aerial images, and GNSS-RTK (CORS) technology was employed to collect ground control points. A polynomial surface fitting method (1st to 3rd degree) was applied to model vegetation thickness and correct the digital surface model (DSM) to obtain a digital elevation model (DEM). The DSM was smoothed using various radii (0; 20; 50; 70; 100 m), and the accuracy was assessed using different numbers of checkpoints (150; 350; 1,000). A software module was developed to automate the correction process.

Findings. The accuracy of the DEM improved with an increased number of checkpoints and an appropriate smoothing radius. The best results were achieved with 1,000 checkpoints and a smoothing radius of 50 m. The corrected DEM achieved elevation accuracy within 1–2 meters, meeting regulatory standards for large-scale topographic maps (1:2,000–1:5,000). The method proved effective even in areas with dense vegetation, where traditional mapping methods face limitations.

Originality. This study introduces a novel approach that integrates UAV photogrammetry with GNSS-RTK data and polynomial surface modeling to correct elevation data in vegetated areas. The developed software module automates the correction process, enhancing efficiency and consistency.

Practical value. The proposed method enables the creation of accurate, large-scale digital topographic maps in challenging environments with dense vegetation. It offers a cost-effective and efficient alternative to traditional surveying methods, with potential applications in forestry, land management, and infrastructure planning.

Keywords: UAV, GNSS technology, vegetation areas, large-scale topographic map

References.

1. Ismael, R. Q., & Henari, Q. Z. (2019). Accuracy assessment of UAV photogrammetry for large scale topographic mapping. 2019 International Engineering Conference (IEC), 1-5. https://doi.org/10.1109/IEC47844.2019.8950607

2. Rovira-Más, F. (2011). Global 3D terrain maps for agricultural applications. Advances in Theory and Applications of Stereo Vision, 227-242. https://doi.org/10.5772/13003

3. Yu, M., Huang, Y., Xu, Q., Guo, P., & Dai, Z. (2016). Application of virtual earth in 3D terrain modeling to visual analysis of large-scale geological disasters in mountainous areas. Environmental earth sciences, 75, 1-7. https://doi.org/10.1007/s12665-015-5161-5

4. Ahmed, R., & Mahmud, K. H. (2022). Potentiality of high-resolution topographic survey using unmanned aerial vehicle in Bangladesh. Remote Sensing Applications: Society and Environment, 26, 100729. https://doi.org/10.1016/j.rsase.2022.100729

5. Latif, Z. A., Aman, S. N. A., & Pradhan, B. (2015). Landslide susceptibility assessment using high resolution Lidar-derived parameters and probabilistic frequency ratio model. International Symposium on Multi-Hazard and Risk, 72-84. Retrieved from https://www.researchgate.net/publication/330439928_MJRSGIS_vol_4_num_2

6. Olsen, M. J., Johnstone, E., Driscoll, N., Ashford, S. A., & Kuester, F. (2009). Terrestrial laser scanning of extended cliff sections in dynamic environments: Parameter analysis. Journal of Surveying Engineering, 135(4), 161-169. https://doi.org/10.1061/(ASCE)0733-9453(2009)135:4(161)

7. Darwin, N., Ahmad, A., & Akib, W. A. A. W. M. (2014). The potential of low altitude aerial data for large scale mapping. Jurnal Teknologi (Sciences & Engineering), 70(5). https://doi.org/10.11113/jt.v70.3523

8. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of photogrammetry and remote sensing, 92, 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013

9. Yazid, A., Wahid, R., Nazrin, K., Ahmad, A., Nasruddin, A., Rozilawati, D., & Razak, M. (2019). Terrain mapping from unmanned aerial vehicles. Journal of Advanced Manufacturing Technology, 13(1), 1-16. Retrieved from https://jamt.utem.edu.my/jamt/article/view/5228

10.      Karantanellis, E., Marinos, V., Vassilakis, E., & Christaras, B. (2020). Object-based analysis using unmanned aerial vehicles (UAVs) for site-specific landslide assessment. Remote Sensing, 12(11), 1711. https://doi.org/10.3390/rs12111711

11.      Aleshin, M., Gavrilova, L., & Melnikov, A. (2019). Use of unmanned aerial vehicles on example of Phantom 4 (standard) for creating digital terrain models. Engineering for Rural Development, 22, 1686-1692. https://doi.org/10.22616/ERDev2019.18.N488

12.      Taddia, Y., Stecchi, F., & Pellegrinelli, A. (2019). Using DJI Phantom 4 RTK drone for topographic mapping of coastal areas. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 625-630. https://doi.org/10.5194/isprs-archives-XLII-2-W13-625-2019

13.      Ahmad, M., Ahmad, A., & Kanniah, K. (2018). Large scale topographic mapping based on unmanned aerial vehicle and aerial photogrammetric technique. IOP Conference Series: Earth and Environmental Science, 169(1), 012007. https://doi.org/10.1088/1757-899X/305/1/012007

14.      Anguiano-Morales, M., Corral-Martínez, L., Trujillo-Schiaffino, G., Salas-Peimbert, D. P., & García-Guevara, A. E. (2018). Topographic investigation from a low altitude unmanned aerial vehicle. Optics and Lasers in Engineering, 110, 63-71. https://doi.org/10.1016/j.optlaseng.2018.05.015

15.      Nwilag, B. D., Eyoh, A. E., & Ndehedehe, C. E. (2023). Digital topographic mapping and modelling using low altitude unmanned aerial vehicle. Modeling Earth Systems and Environment, 9(2), 1463-1476. https://doi.org/10.1007/s40808-022-01677-z

16.      Lee, S., & Choi, Y. (2015). Topographic survey at small-scale open-pit mines using a popular rotary-wing unmanned aerial vehicle (drone). Tunnel and underground space, 25(5), 462-469. https://doi.org/10.7474/TUS.2015.25.5.462

17.      Annis, A., Nardi, F., Petroselli, A., Apollonio, C., Arcangeletti, E., Tauro, F., & Grimaldi, S. (2020). UAV-DEMs for small-scale flood hazard mapping. Water, 12(6), 1717. https://doi.org/10.3390/w12061717

18.      Watanabe, Y., & Kawahara, Y. (2016). UAV photogrammetry for monitoring changes in river topography and vegetation. Procedia Engineering, 154, 317-325. https://doi.org/10.1016/j.proeng.2016.07.482

19.      Nazirova, A. B., Dubovenko, Y I., Abdoldina, F. N., & Kuzminets, M. P. (2021). Optimization of GIS modules for processing data of gravity monitoring of subsoil. Geoinformatics, 1, 1-6. https://doi.org/10.3997/2214-4609.20215521136

20.      Dubovenko, Y. I., Nazirova, A. B., & Abdoldina, F. N. (2022). Data-driven preprocessing of gravity data in oilfield GIS monitoring system. International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, 1, 1-4. https://doi.org/10.3997/2214-4609.2022580267

21.      Meng, X., Shang, N., Zhang, X., Li, C., Zhao, K., Qiu, X., & Weeks, E. (2017). Photogrammetric UAV mapping of terrain under dense coastal vegetation: An object-oriented classification ensemble algorithm for classification and terrain correction. Remote Sensing, 9(11), 1187. https://doi.org/10.3390/rs9111187

22.      Choi, S. K., Ramirez, R. A., & Kwon, T. H. (2023). Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration. Heliyon, 9(9), e20225. https://doi.org/10.1016/j.heliyon.2023.e20225

• < Prev
• Next >