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Journals / International Journal of Engineering and Geosciences / 2025 / Volume: 10 Issue: 3

Comparison of UAV-based digital elevation model with multi beam bathymetry for shallow water

Tuğba Kılıç(ISTANBUL TECHNICAL UNIVERSITY)0009-0001-1812-6619
Onur Akyol(TUBITAK Marmara Research Center)0000-0003-3956-1449
Reha Metin Alkan(ISTANBUL TECHNICAL UNIVERSITY)0000-0002-1981-9783
Pages
—
Publication Date
2025-09-17
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicated that an accuracy of 1.2 meters (RMSE) can be achieved in relatively shallow water areas up to a depth of 5 meters, while an accuracy of 2.0 meters (RMSE) is achievable at depths of around 15 meters.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurate bathymetric data is essential for marine and coastal applications, particularly in shallow water regions.
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Key Findings & Data:However, the UAV-based method is influenced by refraction effects, which result in underwater objects being perceived as shallower than their actual depth, leading to a decrease in the accuracy of the bathymetric model.
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Conclusion & Impact:The results indicated that an accuracy of 1.2 meters (RMSE) can be achieved in relatively shallow water areas up to a depth of 5 meters, while an accuracy of 2.0 meters (RMSE) is achievable at depths of around 15 meters.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Unmanned Aerial Vehicle · Bathymetry · Multibeam Echosounder

Abstract

Accurate bathymetric data is essential for marine and coastal applications, particularly in shallow water regions. Unmanned Aerial Vehicle (UAV)-based systems are recognized for their cost-effectiveness and flexibility, making them a promising alternative in shallow-water environments where Multi Beam Echosounder (MBES) systems often face limitations due to high operational costs or logistical challenges. However, the UAV-based method is influenced by refraction effects, which result in underwater objects being perceived as shallower than their actual depth, leading to a decrease in the accuracy of the bathymetric model. To address this issue, the underestimation of water depth in submerged areas caused by refraction was evaluated using a correction algorithm. This study aims to assess the accuracy and usability of the UAV-based Digital Elevation Model (DEM) for seafloor mapping. For this assessment, the UAV-based DEM from the water body was compared with high-resolution three-dimensional (3D) seafloor topography obtained from a multi beam acoustic survey conducted in the same water area. The results indicated that an accuracy of 1.2 meters (RMSE) can be achieved in relatively shallow water areas up to a depth of 5 meters, while an accuracy of 2.0 meters (RMSE) is achievable at depths of around 15 meters. The study also highlighted the direct correlation between UAV-based DEM accuracy and depth, as well as the impact of sun glint on measurement accuracy. These findings underscore the potential of UAV technology to enhance bathymetric surveying capabilities, particularly in regions where MBES is either impractical or cost-prohibitive, thereby offering a valuable tool for comprehensive mapping and coastal studies

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Cite This Article

Kılıç, T., Akyol, O., Alkan, R. M. (2025). Comparison of UAV-based digital elevation model with multi beam bathymetry for shallow water. International Journal of Engineering and Geosciences, Volume: 10 Issue: 3. https://doi.org/10.26833/ijeg.1582508

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References (37)

  1. Snellen, M., Siemes, K., & Simons, D. G. (2011). Model-based sediment classification using single-beam echosounder signals. The Journal of the Acoustical Society of America, 129(5), 2878-2888.
  2. Landero Figueroa, M. M., Parsons, M. J., Saunders, B. J., Radford, B., Salgado‐Kent, C., & Parnum, I. M. (2021). The use of singlebeam echo‐sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo‐sounder depth. Ecology and Evolution, 11(24), 17873-17884.
  3. Peeri, S., Gardner, J. V., Ward, L. G., & Morrison, J. R. (2010). The seafloor: A key factor in LiDAR bottom detection. IEEE Transactions on Geoscience and Remote Sensing, 49(3), 1150-1157.
  4. Hell, B. (2011). Mapping bathymetry: From measurement to applications. (Doctoral dissertation). Stockholm University, Department of Geological Sciences, Stockholm.
  5. Kim, M., Danielson, J., Storlazzi, C., & Park, S. (2024). Physics-Based Satellite-Derived Bathymetry (SDB) Using Landsat OLI Images. Remote Sensing, 16(5), 843.
  6. Serwa, A. (2016). Development of Software Application for Digital Photogrammetric Systems (ADPS): Basic Level. International Conference on Civil and Architecture Engineering, (pp. 1-15). Military Technical College, April 19-21.
  7. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13, 693-712.
  8. Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148.
  9. Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), 1110.
  10. Selim, S., Demir, N., & Şahin, S. O. (2022). Automatic detection of forest trees from digital surface models derived by aerial images. International Journal of Engineering and Geosciences, 7(3), 208-213.
  11. Şasi, A., & Yakar, M. (2018). Photogrammetric modelling of Hasbey Dar’ülhuffaz (Masjid) using an unmanned aerial vehicle. International Journal of Engineering and Geosciences, 3(1), 6-11. https://doi.org/10.26833/ijeg.328919.
  12. Kanun, E., Alptekin, A., & Yakar, M. (2021). Cultural heritage modelling using UAV photogrammetric methods: a case study of Kanlıdivane archeological site. Advanced UAV, 1(1), 24-33.
  13. İlhan, S., & Aydar, U. (2023). Flood analysis of Çan (Kocabaş) stream with UAV images. Advanced UAV, 3(1), 25-34.
  14. Ağca, M., Gültekin, N., & Kaya, E. (2020). İnsansız hava aracından elde edilen veriler ile kaya düşme potansiyelinin değerlendirilmesi: Adam Kayalar örneği, Mersin. Geomatik, 5(2), 134-145.
  15. Woodget, A. S., Carbonneau, P. E., Visser, F., & Maddock, I. P. (2015). Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surface Processes and Landforms, 40(1), 47-64.
  16. Westaway, R. M., Lane, S. N., & Hicks, D. M. (2000). The development of an automated correction procedure for digital photogrammetry for the study of wide, shallow, gravel‐bed rivers. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 25(2), 209-226.
  17. Chirayath, V., & Li, A. (2019). Next-generation optical sensing technologies for exploring ocean worlds—NASA FluidCam, MiDAR, and NeMO-Net. Frontiers in Marine Science, 6, 521.
  18. Dietrich, J. T. (2017). Bathymetric structure‐from‐motion: Extracting shallow stream bathymetry from multi‐view stereo photogrammetry. Earth Surface Processes and Landforms, 42(2), 355-364.
  19. Skarlatos, D., & Agrafiotis, P. (2018). A novel iterative water refraction correction algorithm for use in structure from motion photogrammetric pipeline. Journal of Marine Science and Engineering, 6(3), 77.
  20. Partama, I. G. Y., Kanno, A., Ueda, M., Akamatsu, Y., Inui, R., Sekine, M., Yamamoto, K., Imai, T., & Higuchi, T. (2018). Removal of water‐surface reflection effects with a temporal minimum filter for UAV‐based shallow‐water photogrammetry. Earth Surface Processes and Landforms, 43(12), 2673-2682.
  21. Bussières, S., Kinnard, C., Clermont, M., Campeau, S., Dubé-Richard, D., Bordeleau, P. A., & Roy, A. (2022). Monitoring Water Turbidity in a Temperate Floodplain Using UAV: Potential and Challenges. Canadian Journal of Remote Sensing, 48(4), 565-574.
  22. Westaway, R. M., Lane, S. N., & Hicks, D. M. (2001). Remote sensing of clear-water, shallow, gravel-bed rivers using digital photogrammetry. Photogrammetric Engineering and Remote Sensing, 67(11), 1271-1282.
  23. Cao, B., Fang, Y., Jiang, Z., Gao, L., & Hu, H. (2019). Shallow water bathymetry from WorldView-2 stereo imagery using two-media photogrammetry. European Journal of Remote Sensing, 52(1), 506-521.
  24. Jerlov, N. G. (1976). Marine optics. Elsevier.
  25. Partama, I. G. Y., Kanno, A., Akamatsu, Y., Inui, R., Goto, M., & Sekine, M. (2017). A simple and empirical refraction correction method for UAV-based shallow-water photogrammetry. International Journal of Geological and Environmental Engineering, 11(4), 295-302.
  26. Serwa, A., & El-Semary, H. H. (2016). Integration of soft computational simulator and strapdown inertial navigation system for aerial surveying project planning. Spatial Information Research, 24, 279-290.
  27. Hedley, J. D., Harborne, A. R., & Mumby, P. J. (2005). Simple and robust removal of sun glint for mapping shallow‐water benthos. International Journal of Remote Sensing, 26(10), 2107-2112.
  28. Tiškus, E., Bučas, M., Vaičiūtė, D., Gintauskas, J., & Babrauskienė, I. (2023). An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies. Drones, 7(9), 546.
  29. DJI (2024). Support Phantom 4 RTK, Specs.
  30. General Directorate of Land Registry and Cadastre (2024). TUSAGA-Aktif System.
  31. WASSP (2024). WMB-3250 Manuals.
  32. Eltner, A., Kaiser, A., Castillo, C., Rock, G., Neugirg, F., & Abellán, A. (2016). Image-based surface reconstruction in geomorphometry–merits, limits and developments. Earth Surface Dynamics, 4(2), 359-389.
  33. Meinen, B. U., & Robinson, D. T. (2020). Mapping erosion and deposition in an agricultural landscape: Optimization of UAV image acquisition schemes for SfM-MVS. Remote Sensing of Environment, 239, 111666.
  34. Zeybek, M. (2021). Classification of UAV point clouds by random forest machine learning algorithm. Turkish Journal of Engineering, 5(2), 48-57.
  35. TUDES (2024). Sea Level Observations.
  36. Bagheri, O., Ghodsian, M., & Saadatseresht, M. (2015). Reach scale application of UAV+ SfM method in shallow rivers hyperspatial bathymetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 77-81.
  37. Kim, H. D., Aoki, S. I., Kim, K. H., Kim, J., Shin, B. S., & Lee, K. (2020). Bathymetric survey for seabed topography using multibeam echo sounder in wando, korea. Journal of Coastal Research, 95(SI), 527-531