Dergiler / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 4

Evaluation of UAV Flight Planning Strategies on the Accuracy of Glacier Mapping in Rugged Antarctic Terrains: A Case Study of Horseshoe Island

Mahmut Oğuz Selbesoğlu(İSTANBUL TEKNİK ÜNİVERSİTESİ)0000-0002-1132-3978
Mustafa Fahri Karabulut(ISTANBUL TECHNICAL UNIVERSITY)0000-0001-8513-3475
Esra Günaydın(ISTANBUL TECHNICAL UNIVERSITY)0000-0003-1868-0887
Oleg Vassilev(Bulgarian Antarctic Institute)0000-0002-8407-8416
Tolga Bakırman(YILDIZ TECHNICAL UNIVERSITY)0000-0001-7828-9666
Burak Akpınar(YILDIZ TECHNICAL UNIVERSITY)0000-0002-3076-1578
Özgün Oktar(TÜBİTAK Polar Research Institute)0000-0002-0188-6267
Burcu Özsoy(ISTANBUL TECHNICAL UNIVERSITY)0000-0003-4320-1796
Sayfa
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Yayın Tarihi
2026-10-16
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In terms of 3D positional accuracy, using a limited number of GCPs resulted in root mean square error (RMSE) values of approximately 1–2 cm in the horizontal and 2–3 cm in the vertical components; however, the RTK-supported flights exhibited higher errors in the Z component, with values of 12.18 cm for the fixed-height and 6.85 cm for the terrain-follow flights.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Changes in the Earth’s radiation balance have increased global average temperatures, accelerating glacier melt in Antarctica.
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Temel Bulgular & Veri:Therefore, obtaining accurate, reliable, and most importantly rapid, logistically feasible, and cost-effective measurements of glacier melt is critically important for regions with harsh conditions such as Antarctica.
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Sonuç & Katkı:In terms of 3D positional accuracy, using a limited number of GCPs resulted in root mean square error (RMSE) values of approximately 1–2 cm in the horizontal and 2–3 cm in the vertical components; however, the RTK-supported flights exhibited higher errors in the Z component, with values of 12.18 cm for the fixed-height and 6.85 cm for the terrain-follow flights.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Antarctica · UAV photogrammetry · Orthophoto

Abstract

Changes in the Earth’s radiation balance have increased global average temperatures, accelerating glacier melt in Antarctica. Monitoring this process has become of great importance for future modeling studies. Therefore, obtaining accurate, reliable, and most importantly rapid, logistically feasible, and cost-effective measurements of glacier melt is critically important for regions with harsh conditions such as Antarctica. The Ground Sampling Distance (GSD) and positional accuracy of photogrammetric products generated by unmanned aerial vehicle (UAV) photogrammetry are critical to accurately assessing snow and glacier dynamics. In this context, the study presents accuracy assessments and planning recommendations for various flight plan scenarios in a rugged area with slopes up to 40% and partial snow and glacial cover. The study was conducted on a test site on Horseshoe Island, and the UAV flight missions were performed under two operational modes: (i) terrain-follow and (ii) fixed-height. The flight missions were real-time kinematic (RTK)-supported; however, ground control points (GCPs) were also placed and measured prior to the flights. The images were evaluated to assess the positional accuracy achieved through RTK-supported processing and GCP-based georectification, with each flight mode analyzed separately. Fixed-height flight planning resulted in lower spatial accuracy than terrain-follow mode, indicating that maintaining a constant flight height over irregular topography reduces spatial resolution due to variable image scales. It was also observed that it performs better in terms of key points per image and bundle block adjustment (BBA)-Average reprojection error in terrain-follow scenarios. In terms of 3D positional accuracy, using a limited number of GCPs resulted in root mean square error (RMSE) values of approximately 1–2 cm in the horizontal and 2–3 cm in the vertical components; however, the RTK-supported flights exhibited higher errors in the Z component, with values of 12.18 cm for the fixed-height and 6.85 cm for the terrain-follow flights.

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Bu Makaleye Atıf Yap

Selbesoğlu, M. O., Karabulut, M. F., Günaydın, E., Vassilev, O., Bakırman, T., Akpınar, B., Oktar, Ö., Özsoy, B. (2026). Evaluation of UAV Flight Planning Strategies on the Accuracy of Glacier Mapping in Rugged Antarctic Terrains: A Case Study of Horseshoe Island. Turkish Journal of Engineering, Volume: 10 Issue: 4. https://doi.org/10.31127/tuje.1935836

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Kaynakça (55)

  1. Zhang, Y., Gao, T., Kang, S., Shangguan, D., & Luo, X. (2021). Albedo reduction as an important driver for glacier melting in Tibetan Plateau and its surrounding areas. Earth-Science Reviews, 220, 103735. https://doi.org/10.1016/j.earscirev.2021.103735
  2. Günaydın, E., Selbesoğlu, M. O., Karabulut, M. F., Erdoğdu, I., Kılıç, B. N., Çelik, B., Oktar, Ö., & Özsoy, B. (2025). Long-term aerosol optical depth analysis and prediction over the Antarctic Peninsula. Polar Science, 101212. https://doi.org/10.1016/j.polar.2025.101212
  3. Meloth, T., & Antony, R. (2023). Melting glaciers unlock hidden contaminants. Current Science, 124(8), 883–884.
  4. Li, Y., Qiao, G., Popov, S., Cui, X., Florinsky, I. V., Yuan, X., & Wang, L. (2023). Unmanned Aerial Vehicle Remote Sensing for Antarctic Research: A review of progress, current applications, and future use cases. IEEE Geoscience and Remote Sensing Magazine, 11(1), 73–93. https://doi.org/10.1109/MGRS.2022.3227056
  5. Gaffey, C. B., Bhardwaj, A., Frey, K. E., & Estes, L. (2022). Polar and Cryospheric Remote Sensing Using sUAS. In K. Konsoer, M. Leitner, & Q. Lewis (Eds), sUAS Applications in Geography (pp. 235–261). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-031-01976-0_9
  6. Lamsters, K., Karušs, J., Krievāns, M., & Ješkins, J. (2020). High-resolution orthophoto map and digital surface models of the largest Argentine Islands (the Antarctic) from unmanned aerial vehicle photogrammetry. Journal of Maps, 16(2), 335–347. https://doi.org/10.1080/17445647.2020.1748130
  7. Ahlstrøm, A. P., Andersen, S. B., Andersen, M. L., Machguth, H., Nick, F. M., Joughin, I., Reijmer, C. H., van de Wal, R. S. W., Merryman Boncori, J. P., Box, J. E., Citterio, M., van As, D., Fausto, R. S., & Hubbard, A. (2013). Seasonal velocities of eight major marine-terminating outlet glaciers of the Greenland ice sheet from continuous in situ GPS instruments. Earth System Science Data, 5(2), 277–287. https://doi.org/10.5194/essd-5-277-2013
  8. Tsutaki, S., Sugiyama, S., Sakakibara, D., & Sawagaki, T. (2016). Surface elevation changes during 2007–13 on Bowdoin and Tugto Glaciers, northwestern Greenland. Journal of Glaciology, 62(236), 1083–1092. https://doi.org/10.1017/jog.2016.106
  9. Selbesoğlu, M. O. (2023). Multi-GNSS reflectometry performance evaluation for coastal sea level monitoring: A case study in Antarctic Peninsula. Advances in Space Research, 71(7), 2990-2995. https://doi.org/10.1016/j.asr.2022.11.057
  10. Marangoz, A. M., Sefercik, U. G., & Yüce, D. (2020). Three-dimensional earth modelling performance analysis of Gokturk-2 satellite. Turkish Journal of Engineering, 4(3), 164–168. https://doi.org/10.31127/tuje.650989
  11. Winsvold, S. H., Kääb, A., Nuth, C., Andreassen, L. M., van Pelt, W. J. J., & Schellenberger, T. (2018). Using SAR satellite data time series for regional glacier mapping. The Cryosphere, 12(3), 867–890. https://doi.org/10.5194/tc-12-867-2018
  12. Yousuf, B., Shukla, A., Arora, M. K., & Jasrotia, A. S. (2019). Glacier facies characterization using optical satellite data: Impacts of radiometric resolution, seasonality, and surface morphology. Progress in Physical Geography: Earth and Environment, 43(4), 473–495. https://doi.org/10.1177/0309133319840770
  13. Selbesoğlu, M. O., Bakirman, T., Vassilev, O., & Ozsoy, B. (2023). Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto. Drones, 7(2), Article 2. https://doi.org/10.3390/drones7020072
  14. Surawy-Stepney, T., Hogg, A. E., Cornford, S. L., & Hogg, D. C. (2023). Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery. The Cryosphere, 17(10), 4421–4445. https://doi.org/10.5194/tc-17-4421-2023
  15. Fahnestock, M., Scambos, T., Moon, T., Gardner, A., Haran, T., & Klinger, M. (2016). Rapid large-area mapping of ice flow using Landsat 8. Remote Sensing of Environment, 185, 84–94. https://doi.org/10.1016/j.rse.2015.11.023
  16. Bhardwaj, A., Sam, L., Akanksha, Martín-Torres, F. J., & Kumar, R. (2016). UAVs as remote sensing platform in glaciology: Present applications and future prospects. Remote Sensing of Environment, 175, 196–204. https://doi.org/10.1016/j.rse.2015.12.029
  17. Fracastoro, G., Magli, E., Poggi, G., Scarpa, G., Valsesia, D., & Verdoliva, L. (2021). Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives. IEEE Geoscience and Remote Sensing Magazine, 9(2), 29–51. https://doi.org/10.1109/MGRS.2021.3070956
  18. Zhao, J., Liang, S., Li, X., Duan, Y., & Liang, L. (2022). Detection of Surface Crevasses over Antarctic Ice Shelves Using SAR Imagery and Deep Learning Method. Remote Sensing, 14(3), 487. https://doi.org/10.3390/rs14030487
  19. Akay, S. S., Ozcan, O., Balik Şanli, F., Bayram, B., & Görüm, T. (2021). Assessing the spatial accuracy of UAV-derived products based on variation of flight altitudes. Turkish Journal of Engineering, 5(1), 35–40. https://doi.org/10.31127/tuje.653631
  20. Ryan, J. C., Hubbard, A., Box, J. E., Brough, S., Cameron, K., Cook, J. M., Cooper, M., Doyle, S. H., Edwards, A., Holt, T., Irvine-Fynn, T., Jones, C., Pitcher, L. H., Rennermalm, A. K., Smith, L. C., Stibal, M., & Snooke, N. (2017). Derivation of High Spatial Resolution Albedo from UAV Digital Imagery: Application over the Greenland Ice Sheet. Frontiers in Earth Science, 5. https://doi.org/10.3389/feart.2017.00040
  21. Masný, M., Weis, K., & Biskupič, M. (2021). Application of Fixed-Wing UAV-Based Photogrammetry Data for Snow Depth Mapping in Alpine Conditions. Drones, 5(4), 114. https://doi.org/10.3390/drones5040114
  22. Fraser, R. H., Olthof, I., Lantz, T. C., & Schmitt, C. (2016). UAV photogrammetry for mapping vegetation in the low-Arctic. Arctic Science, 2(3), 79–102. https://doi.org/10.1139/as-2016-0008
  23. Korczak-Abshire, M., Zmarz, A., Rodzewicz, M., Kycko, M., Karsznia, I., & Chwedorzewska, K. J. (2019). Study of fauna population changes on Penguin Island and Turret Point Oasis (King George Island, Antarctica) using an unmanned aerial vehicle. Polar Biology, 42(1), 217–224. https://doi.org/10.1007/s00300-018-2379-1
  24. Chudley, T. R., Christoffersen, P., Doyle, S. H., Abellan, A., & Snooke, N. (2019). High-accuracy UAV photogrammetry of ice sheet dynamics with no ground control. The Cryosphere, 13(3), 955–968. https://doi.org/10.5194/tc-13-955-2019
  25. Jouvet, G., Weidmann, Y., van Dongen, E., Lüthi, M. P., Vieli, A., & Ryan, J. C. (2019). High-Endurance UAV for Monitoring Calving Glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland. Frontiers in Earth Science, 7. https://doi.org/10.3389/feart.2019.00206
  26. Selbesoğlu, M. O., Bakırman, T., Karabulut, M. F., Günaydın, E., Vassilev, O., Oktar, O., & Özsoy, B. (2024). Monitoring of Glaciers on Horseshoe Island, Antarctica Based on a Deep Learning Approach from High-Resolution Orthophotos (TAE-6 & TAE-7). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 321–327. https://doi.org/10.5194/isprs-archives-XLVIII-4-W9-2024-321-2024
  27. Selbesoğlu, M. O., Kulavuz, B., Hızal, C., Günaydın, E., Karabulut, M. F., Bakırman, T., Vassilev, O., Oktar, Ö., Özsoy, B., & Bayram, B. (2026). Deep learning–based water body extraction using high-resolution RGB-UAV imagery: a case study on Horseshoe Island, Antarctica. Environmental Monitoring and Assessment, 198(7), 680. https://doi.org/10.1007/s10661-026-15519-3
  28. Inoue, J., & Sato, K. (2022). Toward sustainable meteorological profiling in polar regions: Case studies using an inexpensive UAS on measuring lower boundary layers with quality of radiosondes. Environmental Research, 205, 112468. https://doi.org/10.1016/j.envres.2021.112468
  29. Zhao, H., Zhang, B., Hu, W., Liu, J., Li, D., Liu, Y., Yang, H., Pan, J., & Xu, L. (2021). Adaptable Flight Line Planning for Airborne Photogrammetry Using DEM. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6206–6218. https://doi.org/10.1109/JSTARS.2021.3088263
  30. Melita, C. D., Guastella, D. C., Cantelli, L., Di Marco, G., Minio, I., & Muscato, G. (2020). Low-Altitude Terrain-Following Flight Planning for Multirotors. Drones, 4(2), 26. https://doi.org/10.3390/drones4020026
  31. Singh, C. H., Mishra, V., & Jain, K. (2023). High-resolution mapping of forested hills using real-time UAV terrain following. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 665–671. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-665-2023
  32. Stuchlík, R., Stachoň, Z., Láska, K., & Kubíček, P. (2015). Unmanned Aerial Vehicle – Efficient mapping tool available for recent research in polar regions. Czech Polar Reports, 5(2), 210–221. https://doi.org/10.5817/CPR2015-2-18
  33. Bello, A. B., Navarro, F., Raposo, J., Miranda, M., Zazo, A., & Álvarez, M. (2022). Fixed-Wing UAV Flight Operation under Harsh Weather Conditions: A Case Study in Livingston Island Glaciers, Antarctica. Drones, 6(12), 384. https://doi.org/10.3390/drones6120384
  34. La Salandra, M., Miniello, G., Nicotri, S., Italiano, A., Donvito, G., Maggi, G., Dellino, P., & Capolongo, D. (2021). Generating UAV high-resolution topographic data within a FOSS photogrammetric workflow using high-performance computing clusters. International Journal of Applied Earth Observation and Geoinformation, 105, 102600. https://doi.org/10.1016/j.jag.2021.102600
  35. Gonçalves, J. A., & Henriques, R. (2015). UAV photogrammetry for topographic monitoring of coastal areas. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 101–111. https://doi.org/10.1016/j.isprsjprs.2015.02.009
  36. Varlık, A., Durduran, S. S., Mimiroğlu, İ. M., Doksanaltı, E. M., Karakök, C., & Sevmen, D. (2025). Use of UAV Photogrammetry in Archaeological Sites; The Example of the Ancient City of Lystra. International Journal of Engineering and Geosciences, 11(2), 432–441. https://doi.org/10.26833/ijeg.1705025
  37. Yakar, M., & Yılmaz, H. M. (2025). The Use of Unmanned Aerial Vehicles in Cultural Heritage Documentation. International Journal of Engineering and Geosciences, 11(2), 496–509. https://doi.org/10.26833/ijeg.1801614
  38. Ulvi, A. (2020). Importance of unmanned aerial vehicles (UAVs) in the documentation of cultural heritage. Turkish Journal of Engineering, 4(3), 104–112. https://doi.org/10.31127/tuje.637050
  39. Taş, İ. Ç., Bozdoğan, A. M., & Arıca, S. (2025). Application of convolutional neural networks for watermelon detection in UAV aerial images: A case study. Turkish Journal of Engineering, 9(1), 1–11. https://doi.org/10.31127/tuje.1481696
  40. Zeybek, M. (2021). Classification of UAV point clouds by random forest machine learning algorithm. Turkish Journal of Engineering, 5(2), 48–57. https://doi.org/10.31127/tuje.669566
  41. Jiménez-Jiménez, S. I., Ojeda-Bustamante, W., Marcial-Pablo, M. de J., & Enciso, J. (2021). Digital Terrain Models Generated with Low-Cost UAV Photogrammetry: Methodology and Accuracy. ISPRS International Journal of Geo-Information, 10(5), 285. https://doi.org/10.3390/ijgi10050285
  42. Abeho, D. R., Shoko, M., & Odera, P. A. (2024). Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products. International Journal of Engineering and Geosciences, 9(3), 314–323. https://doi.org/10.26833/ijeg.1422619
  43. Papadopoulou, E.-E., Vasilakos, C., Zouros, N., & Soulakellis, N. (2021). DEM-Based UAV Flight Planning for 3D Mapping of Geosites: The Case of Olympus Tectonic Window, Lesvos, Greece. ISPRS International Journal of Geo-Information, 10(8), 535. https://doi.org/10.3390/ijgi10080535
  44. Ekaso, D., Nex, F., & Kerle, N. (2020). Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing. Geo-Spatial Information Science, 23(2), 165–181. https://doi.org/10.1080/10095020.2019.1710437
  45. Özdemir, E. G., Deniz, E., & Hezer, M. (2025). Assessment of Positional Accuracy in Maps Derived Using UAV_PPK, UAV Network RTK, RTK-GNSS, and TUSAGA-Active Techniques. International Journal of Engineering and Geosciences, 11(1), 226–238. https://doi.org/10.26833/ijeg.1686266
  46. Günaydın, E., Selbesoğlu, M. O., Karabulut, M. F., Vassilev, O., Akpınar, B., Yanalak, M., Oktar, Ö., & Özsoy, B. (2025). PPK yöntemi ile İHA verilerinden üretilen ortofoto ve SYM ürünlerinin konumsal doğruluğunun araştırılması: Antarktika’daki Horseshoe Adası örneği. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14(1), 10–17. https://doi.org/10.28948/ngumuh.1500951
  47. Martínez-Carricondo, P., Agüera-Vega, F., & Carvajal-Ramírez, F. (2023). Accuracy assessment of RTK/PPK UAV-photogrammetry projects using differential corrections from multiple GNSS fixed base stations. Geocarto International, 38(1), 2197507. https://doi.org/10.1080/10106049.2023.2197507
  48. Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Skeletal graphs for efficient structure from motion. 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 1–8. https://doi.org/10.1109/CVPR.2008.4587678
  49. Ullman, S. (1979). The interpretation of visual motion. Massachusetts Inst of Technology Pr., Cambridge, MA. https://doi.org/10.7551/mitpress/3877.001.0001
  50. Deliry, S. I., & Avdan, U. (2021). Accuracy of Unmanned Aerial Systems Photogrammetry and Structure from Motion in Surveying and Mapping: A Review. Journal of the Indian Society of Remote Sensing, 49(8), 1997–2017. https://doi.org/10.1007/s12524-021-01366-x
  51. Deliry, S. I., & Avdan, U. (2024). Accuracy assessment of UAS photogrammetry and structure from motion in surveying and mapping. International Journal of Engineering and Geosciences, 9(2), 165–190. https://doi.org/10.26833/ijeg.1366146
  52. Yıldırım, C. (2020). Geomorphology of Horseshoe Island, Marguerite Bay, Antarctica. Journal of Maps, 16(2), 56–67. https://doi.org/10.1080/17445647.2019.1692700
  53. Smith, I. F. (1973). Gravity survey on Shoesmith Glacier, Horseshoe Island, Graham Land. British Antarctic Survey Bulletin, 33, 77–82.
  54. Selbesoğlu, M. O., Yalçın, G., Karabulut, M. F., Yavaşoğlu, H. H., Günaydın, E., & Gülal, V. E. (2026). Atmospheric Water Vapor Monitoring on Horseshoe Island, Antarctica: GNSS Observations at the Permanent TUR1 and TUR2 Stations Using a Regional Tm Calibration. Remote Sensing, 18(18), 3226. https://doi.org/10.3390/rs18183226
  55. Selbesoğlu, M. O., Yavaşoğlu, H. H., Karabulut, M. F., Oktar, Ö., Gülal, V. E., Karaman, H., & Kamaşak, M. E. (2023). Accuracy investigation of GNSS-reflectometry for sea level monitoring on Horseshoe Island, Antarctica: preliminary results of the Turkish permanent GNSS station (TUR1). Turkish Journal of Earth Sciences, 32(8), 1013-1021. https://doi.org/10.55730/1300-0985.1890