Delineation of debris-covered glaciers with multi-temporal UAV images (Gössnitzkees, Schober Group /Austria)

• Autorzy: Zandler Harald , Sulzer Wolfgang , Kaufmann Viktor+

Identyfikatory

DOI

10.24425/agg.2025.154150

• Języki publikacji: EN

Abstrakty

EN

The relative share of debris-covered glaciers strongly increased in recent decades due to climate change and amplified rock production, but despite their relevance as climate indicators, accurate demarcation of respective glaciers is challenging. Remote sensing is an important tool for glacier mapping, but most existing studies apply medium resolution sensors, which are not suitable for small, rock covered glaciers, or use semi-automatic approaches. We present a simple methodology to automatically derive debris-covered glacier areas by using multitemporal, high resolution UAV images. Thereby, standard products, such as elevation differences calculated from digital surface models and displacement rasters, combined with statistical or error thresholds, provide the basis to automatically delineate glacier areas. The comparison of the automatically derived debris-covered glacier area to the geodetically determined glacier snout showed lowest errors for the elevation-based method using yearly data (total error of 0.32 m or 8.6% of the yearly glacier retreat) and higher errors for four-year intervals (0.97 m, 34% of the yearly glacier retreat) or displacement-based methods (0.51 m, 13.6% of the yearly glacier retreat with yearly epochs). Visual evaluation also showed strong errors of the displacement-based method with many areas wrongly identified as debris-covered glacier area.We conclude that the elevation-based method allows for accurate delineation of debris-covered glaciers and pro-glacial areas, providing increased standardization of glacier monitoring using remote sensing.

Słowa kluczowe

EN

UAV; remote sensing; glacier boundary; Gössnitzkees; debris-covered glacier

PL

teledetekcja; lodowiec; UAV

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Advances in Geodesy and Geoinformation
• Rocznik: 2025
• Tom: Vol. 74, no. 1
• Strony: art. no. e66, 2025
• Opis fizyczny: Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Zandler Harald

• University of Graz, Graz, Austria

• https://orcid.org/0000-0001-5505-2455

autor

Sulzer Wolfgang

• University of Graz, Graz, Austria

• https://orcid.org/0000-0001-6040-2405

autor

Kaufmann Viktor+

• Graz University of Technology, Graz, Austria

• https://orcid.org/0000-0003-2074-1992

Bibliografia

• 1. Aati, S., Milliner, C., and Avouac, J.-P. (2022). A new approach for 2-D and 3-D precise measurements of ground deformation from optimized registration and correlation of optical images and ICA-based filtering of image geometry artifacts. Remote Sens. Environ., 277, 113038. DOI: 10.1016/j.rse.2022.113038.
• 2. Avian, M., Bauer, C., Schlögl, M. et al. (2020). The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales. Remote Sens., 12(8), 1251. DOI: 10.3390/rs12081251.
• 3. Azzoni, R.S., Fugazza, D., Zerboni, A. et al. (2018). Evaluating high-resolution remote sensing data for reconstructing the recent evolution of supra glacial debris: A study in the Central Alps (Stelvio Park, Italy). Prog. Phys. Geogr. Earth Environ., 42(1), 3–23. DOI: 10.1177/0309133317749434.
• 4. Barr, I.D., Dokukin, M.D., Kougkoulos, I. et al. (2018). Using ArcticDEM to Analyse the Dimensions and Dynamics of Debris-Covered Glaciers in Kamchatka, Russia. Geosci., 8(6), 216. DOI: 10.3390/geosciences8060216.
• 5. Bolch, T., Buchroithner, M. F., Kunert, A. et al. (2007). Automated delineation of debris-covered glaciers based on ASTER data. In M. A. Gomarasca (Ed.), GeoInformation in Europe, pp. 403–410.Netherlands: Millpress.
• 6. Borchers, H.W. (2023). pracma: Practical Numerical Math Functions. Retrieved November 10, 2024, from https://cran.r-project.org/web/packages/pracma/index.html. Accessed 4 September 2024.
• 7. Bremer. (2012). Module IMCORR – Feature Tracking / SAGA-GIS Module Library Documentation (v2.1.4). Retrieved September 4, 2024, from https://saga-gis.sourceforge.io/saga_tool_doc/2.1.4/grid_analysis_19.html.
• 8. Conrad, O., Bechtel, B., Bock, M. et al. (2015). System for Automated Geoscientific Analyses (SAGA) v.2.1.4. Geosci. Model Developemnt, 8(7), 1991–2007. DOI: 10.5194/gmd-8-1991-2015.
• 9. Di Rita, M., Fugazza, D., Belloni, V. et al. (2020). Glacier volume change monitoring from UAV observations: issues and potentials of state-of-the-art techniques. Int. Arch. Photogr. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1041–1048. DOI: 10.5194/isprs-archives-XLIII-B2-2020-1041-2020.
• 10. Ewertowski, M.W., Tomczyk, A.M., Evans, D.J.A. et al. (2019). Operational Framework for Rapid, Veryhigh Resolution Mapping of Glacial Geomorphology Using Low-cost Unmanned Aerial Vehicles and Structure-from-Motion Approach. Remote Sens., 11(1), 65. DOI: 10.3390/rs11010065.
• 11. Fahnestock, M.A., Scambos, T.A., and Bindschadler, R.A. (1992). Semi-automated ice velocity determination from satellite imagery. Eos, 73(493), 437–461.
• 12. Fleischer, F., Otto, J., Junker, R. R., & Hölbling, D. (2021). Evolution of debris cover on glaciers of the Eastern Alps, Austria, between 1996 and 2015. Earth Surface Processes and Landforms, 46(9), 1673–1691. DOI: 10.1002/esp.5065.
• 13. Foster, L.A., Brock, B.W., Cutler, M.E J. et al. (2012). A physically based method for estimating supraglacial debris thickness from thermal band remote-sensing data. J. Glaciol., 58(210), 677-691. DOI:10.3189/2012JoG11J194.
• 14. Fugazza, D., Senese, A., Azzoni, R.S. et al. (2015). High-resolution mapping of glacier surface features. The UAV survey of the Forni glacier (Stelvio National Park, Italy). Geografia Fisica e Dinamica Quaternaria, 38, 25-33. DOI: 10.4461/GFDQ.2015.38.03.
• 15. Hijmans, R.J., Bivand, R., Dyba, K. et al. (2024). Terra: Spatial Data Analysis.Retrieved September 4, 2024, from https://cran.r-project.org/web/packages/terra/index.html.
• 16. Huss, M., and Fischer, M. (2016). Sensitivity of Very Small Glaciers in the Swiss Alps to Future Climate Change. Fron. Earth Sci., 4. DOI: 10.3389/feart.2016.00034.
• 17. Immerzeel,W.W., Kraaijenbrink, P.D.A., Shea, J.M. et al. (2014). High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. Remote Sens. Environ., 150, 93–103. DOI:10.1016/j.rse.2014.04.025.
• 18. Jawak, S.D., Kumar, S., Luis, A.J. et al. (2018). Evaluation of Geospatial Tools for Generating Accurate Glacier Velocity Maps from Optical Remote Sensing Data. In The 2nd International Electronic Conference on Remote Sensing, p. 341. DOI: 10.3390/ecrs-2-05154.
• 19. Kaufmann, V., and Ladstädter, R. (2008a). Documentation of the Retreat of Gössnitzkees and Hornkees Glaciers (Hohe Tauern Range, Austria) for the Time Period 1997-2006 by Means of Aerial Photogrammetry. In Proceedings of the 6th ICA Mountain Cartography Workshop (Vol.6, pp. 115–123). Lenk, Switzerland: Proceedings of the ICA Mountain Cartography Workshop. https://mountaincartography.icaci.org/publications/papers/papers_lenk_08/kaufmann.pdf.
• 20. Kaufmann, V., and Ladstädter, R. (2008b). Application of terrestrial photogrammetry for glacier monitoring in alpine environments. Proceedings of the 21st Congress of ISPRS, 37(Part B8), 813–818.
• 21. Kaufmann, V., and Sulzer, W. (2019). Dokumentation des Gletscherrückgangs am Gössnitzkees für den Zeitraum 1982-2018 – eine Gletschergeschichte mit Ablaufdatum.
• 22. Kaufmann, V., Sulzer, W., Seier, G. et al. (2019). Panta Rhei: Movement Change of Tschadinhorn Rock Glacier (Hohe Tauern Range, Austria). Cartogr. Geoinf., 18(31), 4–24. DOI: 10.32909/kg.18.31.1.
• 23. Kaufmann, V. (2024). Website for access to geodetic measurement results of the Goessnitzkees (Carinthia, Austria). Retrieved October 18, 2024, from https://www.staff.tugraz.at/viktor.kaufmann/Goessnitzkees/index.html.
• 24. Kellerer-Pirklbauer, A. (2008). The Supraglacial Debris System at the Pasterze Glacier, Austria: Spatial Distribution, Characteristics and Transport of Debris. Zeitschrift für Geomorphologie, Supplementary Issues, 52(1), 3–25. DOI: 10.1127/0372-8854/2008/0052S1-0003.
• 25. Kellerer-Pirklbauer, A., Avian, M., Benn, D.I. et al. (2021). Buoyant calving and ice-contact lake evolution at Pasterze Glacier (Austria) in the period 1998–2019. The Cryosphere, 15(3), 1237–1258. IEEE Access, 8, 12725-12734. DOI: 10.5194/tc-15-1237-2021.
• 26. Khan, A.A., Jamil, A., Hussain, D. et al. (2020). Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study. IEEE Access, 8, 12725–12734. DOI: 10.1109/ACCESS.2020.2965768.
• 27. Lambrecht, A., and Kuhn, M. (2007). Glacier changes in the Austrian Alps during the last three decades, derived from the new Austrian glacier inventory. Ann. Glaciol., 46, 177–184. DOI:10.3189/172756407782871341.
• 28. Land Kärnten. (1997). Orthophoto – Federal State of Kärnten – KAGIS. CC-BY-4.0. Retrieved March 20, 2024, from https://kagis.ktn.gv.at
• 29. Land Kärnten. (2012). Airborne Laserscanning Data – Federal State of Kärnten – KAGIS. CC-BY-4.0. Retrieved March 19, 2024, from https://kagis.ktn.gv.at.
• 30. Land Kärnten. (2021). Orthophoto – Federal State of Kärnten – KAGIS. CC-BY-4.0. Retrieved March 20, 2024, from https://kagis.ktn.gv.at.
• 31. Lang, H., and Lieb, G.K. (1993). Die Gletschere Kärntens. Klagenfurt, Austria: Verlag des Naturwissenschaftlichen Vereins für Kärnten.
• 32. Leprince, S., Barbot, S., Ayoub, F. et al. (2007). Automatic and Precise Orthorectification, Coregistration, and Subpixel Correlation of Satellite Images, Application to Ground Deformation Measurements. IEEE Trans. Geosci. Remote Sens., 45(6), 1529–1558. DOI: 10.1109/TGRS.2006.888937.
• 33. Mayr, E., and Hagg, W. (2019). Debris-Covered Glaciers. In T. Heckmann & D. Morche (Eds.), Geomorphology of Proglacial Systems: Landform and Sediment Dynamics in Recently Deglaciated Alpine Landscapes, pp. 59–71. Cham: Springer International Publishing. DOI: 10.1007/978-3-319-94184-4_4.
• 34. Mölg, N., Bolch, T., Rastner, P. et al. (2018). A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges. Earth Sys. Sci. Data, 10(4), 1807–1827. DOI: 10.5194/essd-10-1807-2018.
• 35. Mueting, A., and Bookhagen, B. (2023). Tracking slow-moving landslides with PlanetScope data: new perspectives on the satellite’s perspective (preprint). EGUsphere [preprint]. DOI: 10.5194/egusphere-2023-1698.
• 36. Patzelt, G. (1980). The Austrian glacier inventory: status and first results. Bremerhaven: IAHS Publication. Retrieved September 13, 2024, from https://epic.awi.de/id/eprint/32383/1/Patzelt_1980.pdf.
• 37. Paul, F., Huggel, C., and Kääb, A. (2004). Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers. Remote Sens. Environ., 89(4), 510–518. DOI:10.1016/j.rse.2003.11.007.
• 38. Paul, F., Bolch, T., Briggs, K. et al. (2017). Error sources and guidelines for quality assessment of glacier area, elevation change, and velocity products derived from satellite data in the Glaciers_cci project. Remote Sens. Environ., 203, 256–275. DOI: 10.1016/j.rse.2017.08.038.
• 39. Pebesma, E. (2018). Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10(1), 439-446.
• 40. Pebesma, E., and Bivand, R. (2023). Spatial Data Science: With Applications in R. New York: Chapman and Hall/CRC. DOI: 10.1201/9780429459016.
• 41. Piermattei, L., Carturan, L., and Guarnieri, A. (2015). Use of terrestrial photogrammetry based on structurefrom-motion for mass balance estimation of a small glacier in the Italian alps. Earth Surf. Proc. Landforms, 40(13), 1791–1802. DOI: 10.1002/esp.3756.
• 42. Pronk, J.B., Bolch, T., King, O. et al. (2021). Contrasting surface velocities between lake- and landterminating glaciers in the Himalayan region. The Cryosphere, 15(12), 5577–5599. DOI: 10.5194/tc-15-5577-2021.
• 43. R Core Team. (2024). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
• 44. Robson, B.A., Hölbling, D., Nuth, C. et al. (2016). Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis. Remote Sens., 8(1), 67. DOI: 10.3390/rs8010067.
• 45. Sahu, R., and Gupta, R.D. (2018). Conceptual framework of the combined pixel and object-based method for the delineation of debris-covered glaciers. ISPRS Ann. Photogr. Remote Sens. Spatial Inf. Sci., IV–5, 173–180. DOI: 10.5194/isprs-annals-IV-5-173-2018.
• 46. Scambos, T.A., Dutkiewicz, M.J., Wilson, J.C. et al. (1992). Application of image cross-correlation to the measurement of glacier velocity using satellite image data. Remote Sens. Environ., 42(3), 177–186. DOI: 10.1016/0034-4257(92)90101-O.
• 47. Shukla, A., and Garg, P. K. (2019). Evolution of a debris-covered glacier in the western Himalaya during the last four decades (1971–2016): A multiparametric assessment using remote sensing and field observations. Geomorphology, 341, 1–14. DOI: 10.1016/j.geomorph.2019.05.009.
• 48. Sledz, S., Ewertowski, M.W., and Piekarczyk, J. (2021). Applications of unmanned aerial vehicle (UAV) surveys and Structure from Motion photogrammetry in glacial and periglacial geomorphology. Geomorphology, 378, 107620. DOI: 10.1016/j.geomorph.2021.107620.
• 49. Strimas-Mackey, M. (2023). smoothr: Smooth and Tidy Spatial Features. Retrieved September 4, 2024, from https://cran.r-project.org/web/packages/smoothr/index.html.
• 50. Thomas, D. J., Robson, B. A.,&Racoviteanu, A. (2023). An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers. Frontiers in Remote Sensing, 4, 1161530. DOI: 10.3389/frsen.2023.1161530.
• 51. Tielidze, L.G., Iacob, G., and Holobâca, I.H. (2024). Mapping of Supra-Glacial Debris Cover in the Greater Caucasus: A Semi-Automated Multi-Sensor Approach. Geosci., 14(7), 178. DOI: 10.3390/geosciences14070178.
• 52. Wecht, M. (2021). Standardized structure from motion – multi view stereo photogrammetry workflow to gain high quality digital elevation models for geomorphic research. Master Thesis TU Graz. DOI:10.3217/2s19x-n9x81.
• 53. Westoby, M.J., Brasington, J., Glasser, N.F. et al. (2012). ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314. DOI:10.1016/j.geomorph.2012.08.021.
• 54. Xie, Z., Haritashya, U.K., Asari, V.K. et al. (2020). GlacierNet: A Deep-Learning Approach for Debris-Covered Glacier Mapping. IEEE Access, 8, 83495–83510. DOI: 10.1109/ACCESS.2020.2991187.
• 55. Yang, X., Xie, F., Liu, S. et al. (2024). Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms. Remote Sens., 16(12), 2062. DOI: 10.3390/rs16122062.
• 56. Yurtseven, H. (2019). Comparison of GNSS-, TLS- and Different Altitude UAV-Generated Datasets on the Basis of Spatial Differences. ISPRS Int. J. Geo-Inf., 8(4), 175. DOI: 10.3390/ijgi8040175.
• 57. Zandler, H., Haag, I., and Samimi, C. (2019). Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions. Sci. Rep., 9(1), 15118. DOI:10.1038/s41598-019-51666-z.
• 58. Zhou, W., Xu, M., and Han, H. (2024). Spatial Distribution and Variation in Debris Cover and Flow Velocities of Glaciers during 1989–2022 in Tomur Peak Region, Tianshan Mountains. Remote Sens., 16(14), 2587. DOI: 10.3390/rs16142587.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)