Articles | Volume 19, issue 9
https://doi.org/10.5194/tc-19-3949-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-3949-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2025
Research article |
| 22 Sep 2025
| 22 Sep 2025
Comparing high-resolution snow mapping approaches in palsa mires: UAS lidar vs. modelling
Alexander Störmer
CORRESPONDING AUTHOR
Physical Geography and Landscape Ecology Section, Institute of Earth System Sciences, Leibniz University Hannover, Hanover 30167, Germany
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi 99490, Finland
Timo Kumpula
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu 80101, Finland
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi 99490, Finland
Miguel Villoslada
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu 80101, Finland
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi 99490, Finland
Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia
Pasi Korpelainen
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu 80101, Finland
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi 99490, Finland
Henning Schumacher
Physical Geography and Landscape Ecology Section, Institute of Earth System Sciences, Leibniz University Hannover, Hanover 30167, Germany
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi 99490, Finland
Benjamin Burkhard
Physical Geography and Landscape Ecology Section, Institute of Earth System Sciences, Leibniz University Hannover, Hanover 30167, Germany
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi 99490, Finland
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Cited articles
Autio, J. and Heikkinen, O.: The climate of northern Finland, Fennia, 180, 61–66, 2002. a
Barry, R. G.: The Role of Snow and Ice in the Global Climate System: A Review, Polar Geography, 26, 235–246, https://doi.org/10.1080/789610195, 2002. a
Bergamo, T. F., de Lima, R. S., Kull, T., Ward, R. D., Sepp, K., and Villoslada, M.: From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa, J. Environ. Manage., 117693, 336, https://doi.org/10.1016/j.jenvman.2023.117693, 2023. a
Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., Casalicchio, G., and Jones, Z. M.: mlr: Machine Learning in R, GitHub [code], https://github.com/mlr-org/mlr (last access: 16 April 2025), 2016. a
Böhner, J. and Selige, T.: Spatial Prediction of soil attributes using terrain analysis and climate regionalisation, Göttinger Geographische Abhandlungen, 115, 13–28, 2006. a
Broxton, P. D., van Leeuwen, W. J., and Biederman, J. A.: Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements, Water Resour. Res., 55, 3739–3757, https://doi.org/10.1029/2018WR024146, 2019. a
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016. a
Chai, T. and Draxler, R. R.: Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7, 1247–1250, https://doi.org/10.5194/gmd-7-1247-2014, 2014. a, b
Chen, L., Aalto, J., and Luoto, M.: Observed Decrease in Soil and Atmosphere Temperature Coupling in Recent Decades Over Northern Eurasia, Geophys. Res. Lett., 48(6), e2021GL092500, https://doi.org/10.1029/2021GL092500, 2021. a
Cho, E., Verfaillie, M., Jacobs, J. M., Hunsaker, A. G., Sullivan, F. B., Palace, M., and Wagner, C.: Characterizing Spatial Structures of Field-Scale Snowpack using Unpiloted Aerial System (UAS) Lidar and SfM Photogrammetry, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1530, 2024. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: A review, Journal of Glaciology, 59(215), 467–479, https://doi.org/10.3189/2013JoG12J154, 2013. a, b, c
De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., and Della Vedova, G.: Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation, The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, 2016. a
DeWalle, D. R. and Rango, A.: Principles of snow hydrology, Cambridge University Press, https://doi.org/10.1017/CBO9780511535673, 2008. a
EuroGeographics: EuroGlobalMap [data set], https://www.mapsforeurope.org/access-data (last access: 18 July 2024), 2024. a
Gerlitz, L., Conrad, O., and Böhner, J.: Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach, Earth Syst. Dynam., 6, 61–81, https://doi.org/10.5194/esd-6-61-2015, 2015. a, b
Gould, S. B., Glenn, N. F., Sankey, T. T., and Mcnamara, J. P.: Influence of a Dense, Low-height Shrub Species on the Accuracy of a Lidar-derived DEM, Photogramm. Eng. Rem. S., 79, 421–431, 2013. a
Guisan, A., Weiss, S. B., and Weiss, A. D.: GLM versus CCA spatial modeling of plant species distribution, Plant Ecol., 143, 107–122, 1999. a
Harder, P., Pomeroy, J. W., and Helgason, W. D.: Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques, The Cryosphere, 14, 1919–1935, https://doi.org/10.5194/tc-14-1919-2020, 2020. a, b
Hijmans, R. J., van Etten, J., Sumner, M., Cheng, J., Baston, D., Bevan, A., Bivand, R., Busetto, L., Canty, M., Fasoli, B., Forrest, D., Ghosh, A., Golicher, D., Gray, J., Greenberg, J. A., Hiemstra, P., Hingee, K., and Ilich, A.: Package “raster”: Geographic Data Analysis and Modeling, The R Foundation for Statistical Computing [code], https://doi.org/10.32614/CRAN.package.raster, 2023. a
Hu, J. M., Shean, D., and Bhushan, S.: Six Consecutive Seasons of High-Resolution Mountain Snow Depth Maps From Satellite Stereo Imagery, Geophys. Res. Lett., 50(24), e2023GL104871, https://doi.org/10.1029/2023GL104871, 2023. a
IPCC: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Lee, H., and Romero, J., IPCC, https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023. a
Jacobs, J. M., Hunsaker, A. G., Sullivan, F. B., Palace, M., Burakowski, E. A., Herrick, C., and Cho, E.: Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States, The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, 2021. a, b
Kauhanen, H. O.: Mountains of Kilpisjarvi Host An Abundance of Threatened Plants in Finnish Lapland, Botanica Pacifica, 2, 43–52, https://doi.org/10.17581/bp.2013.02105, 2013. a
Leppänen, L., Kontu, A., Hannula, H.-R., Sjöblom, H., and Pulliainen, J.: Sodankylä manual snow survey program, Geosci. Instrum. Method. Data Syst., 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, 2016. a
Leppiniemi, O., Karjalainen, O., Aalto, J., Luoto, M., and Hjort, J.: Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale, The Cryosphere, 17, 3157–3176, https://doi.org/10.5194/tc-17-3157-2023, 2023. a, b
Lépy, E. and Pasanen, L.: Observed regional climate variability during the last 50 years in reindeer herding cooperatives of finnish Fell Lapland, Climate, 5(4), 81, https://doi.org/10.3390/cli5040081, 2017. a
Luoto, M., Heikkinen, R. K., and Carter, T. R.: Loss of palsa mires in Europe and biological consequences, Environmental Conservation, 31, 30–37, Cambridge University Press, https://doi.org/10.1017/S0376892904001018, 2004. a
Madani, N., Parazoo, N. C., and Miller, C. E.: Climate change is enforcing physiological changes in Arctic Ecosystems, Environ. Res. Lett., 18(7), 074027, https://doi.org/10.1088/1748-9326/acde92, 2023. a
Mandlburger, G. and Jutzi, B.: On the feasibility of water surface mapping with single photon lidar, ISPRS Int. J. Geo-Inf., 8(4), 188, https://doi.org/10.3390/ijgi8040188, 2019. a
Markkula, I., Turunen, M., and Rasmus, S.: A review of climate change impacts on the ecosystem services in the Saami Homeland in Finland, Science of the Total Environment, 692, 1070–1085, https://doi.org/10.1016/j.scitotenv.2019.07.272, 2019. a
Marti, R., Gascoin, S., Berthier, E., de Pinel, M., Houet, T., and Laffly, D.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10, 1361–1380, https://doi.org/10.5194/tc-10-1361-2016, 2016. a, b
Martin, L. C. P., Nitzbon, J., Scheer, J., Aas, K. S., Eiken, T., Langer, M., Filhol, S., Etzelmüller, B., and Westermann, S.: Lateral thermokarst patterns in permafrost peat plateaus in northern Norway, The Cryosphere, 15, 3423–3442, https://doi.org/10.5194/tc-15-3423-2021, 2021. a
Meloche, J., Langlois, A., Rutter, N., McLennan, D., Royer, A., Billecocq, P., and Ponomarenko, S.: High-resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut, Hydrol. Process., 36(3), e14546, https://doi.org/10.1002/hyp.14546, 2022. a, b, c
Meriö, L.-J., Rauhala, A., Ala-aho, P., Kuzmin, A., Korpelainen, P., Kumpula, T., Kløve, B., and Marttila, H.: Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions, The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, 2023. a
Merkouriadi, I., Leppäranta, M., and Järvinen, O.: Interannual variability and trends in winter weather and snow conditions in Finnish Lapland, Est. J. Earth Sci., 66, 47–57, https://doi.org/10.3176/earth.2017.03, 2017. a
Nagelkerke, N. J. D.: A note on a general definition of the coefficient of determination, Biometrika, 78, 691–692, 1991. a
NOAA: March 2023 Global Snow and Ice Report, https://www.ncei.noaa.gov/access/monitoring/monthly-report/global-snow/202303 (last access: 25 June 2024), 2023. a
Olaya, V.: Basic land-surface parameters, vol. 33, Elsevier Ltd, 141–169, https://doi.org/10.1016/S0166-2481(08)00006-8, 2009. a
Olaya, V. and Conrad, O.: Geomorphometry in SAGA, vol. 33, Elsevier Ltd, 293–308, https://doi.org/10.1016/S0166-2481(08)00012-3, 2009. a, b, c
Olvmo, M., Holmer, B., Thorsson, S., Reese, H., and Lindberg, F.: Sub-arctic palsa degradation and the role of climatic drivers in the largest coherent palsa mire complex in Sweden (Vissátvuopmi), 1955–2016, Sci. Rep.-UK, 10, 8937, https://doi.org/10.1038/s41598-020-65719-1, 2020. a, b
Park, H., Fedorov, A. N., Zheleznyak, M. N., Konstantinov, P. Y., and Walsh, J. E.: Effect of snow cover on pan-Arctic permafrost thermal regimes, Clim. Dynam., 44, 2873–2895, https://doi.org/10.1007/s00382-014-2356-5, 2015. a
Peng, X., Frauenfeld, O. W., Huang, Y., Chen, G., Wei, G., Li, X., Tian, W., Yang, G., Zhao, Y., and Mu, C.: The thermal effect of snow cover on ground surface temperature in the Northern Hemisphere, Environ. Res. Lett., 19, 044015, https://doi.org/10.1088/1748-9326/ad30a5, 2024. a
Probst, P., Wright, M. N., and Boulesteix, A. L.: Hyperparameters and tuning strategies for random forest, Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 9, e1301, https://doi.org/10.1002/widm.1301, 2019. a
Quante, L., Willner, S. N., Middelanis, R., and Levermann, A.: Regions of intensification of extreme snowfall under future warming, Sci. Rep.-UK, 11, 16621, https://doi.org/10.1038/s41598-021-95979-4, 2021. a
Ran, Y., Li, X., Cheng, G., Che, J., Aalto, J., Karjalainen, O., Hjort, J., Luoto, M., Jin, H., Obu, J., Hori, M., Yu, Q., and Chang, X.: New high-resolution estimates of the permafrost thermal state and hydrothermal conditions over the Northern Hemisphere, Earth Syst. Sci. Data, 14, 865–884, https://doi.org/10.5194/essd-14-865-2022, 2022. a
Rauhala, A., Meriö, L.-J., Kuzmin, A., Korpelainen, P., Ala-aho, P., Kumpula, T., Kløve, B., and Marttila, H.: Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment, The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, 2023. a, b
Renette, C., Olvmo, M., Thorsson, S., Holmer, B., and Reese, H.: Multitemporal UAV lidar detects seasonal heave and subsidence on palsas, The Cryosphere, 18, 5465–5480, https://doi.org/10.5194/tc-18-5465-2024, 2024. a
Revuelto, J., Billecocq, P., Tuzet, F., Cluzet, B., Lamare, M., Larue, F., and Dumont, M.: Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas, Hydrol. Process., 34, 5384–5401, https://doi.org/10.1002/hyp.13951, 2020. a, b, c
Revuelto, J., Alonso-Gonzalez, E., Vidaller-Gayan, I., Lacroix, E., Izagirre, E., Rodríguez-López, G., and López-Moreno, J. I.: Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain, Cold Reg. Sci. Technol., 190, 103344, https://doi.org/10.1016/j.coldregions.2021.103344, 2021. a
Richiardi, C., Siniscalco, C., and Adamo, M.: Comparison of Three Different Random Forest Approaches to Retrieve Daily High- Resolution Snow Cover Maps from MODIS and Sentinel-2 in a Mountain Area, Gran Paradiso National Park (NW Alps), Remote Sens., 15, 343, https://doi.org/10.3390/rs15020343, 2023. a
Seppälä, M.: Snow Depth Controls Palsa Growth, Permafrost Periglac., 5, 283–288, 1994. a
Seppälä, M.: An experimental climate change study of the effect of increasing snow cover on active layer formation of a palsa, Finnish Lapland, in: Phillips, M., Springman, S. M., and Arenson, L. U. (Eds.), Proceedings of the 8th International Conference on Permafrost, Zürich, Switzerland, 21–25 July 2003, Vol. 2, A. A. Balkema, Lisse, pp. 1013–1016, ISBN 9058095827, 2003. a
Seppälä, M.: Palsa mires in Finland, The Finnish Environment, 23, 155–162, 2006. a
Thackeray, C. W. and Fletcher, C. G.: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions, Prog. Phys. Geog., 40, 392–408, https://doi.org/10.1177/0309133315620999, 2016. a
Verdonen, M., Villoslada, M., Kolari, T., Tahvanainen, T., Korpelainen, P., Tarolli, P., and Kumpula, T.: Spatial distribution of thaw depth in palsas estimated from Optical Unoccupied Aerial Systems data, Permafrost Periglac., 36, 22–36, 2024. a
Walker, B., Wilcox, E. J., and Marsh, P.: Accuracy assessment of late winter snow depth mapping for tundra environments using structure-from-motion photogrammetry, Arctic Science, 7, 588–604, https://doi.org/10.1139/as-2020-0006, 2021. a, b, c
Walser, H.: Statistik für Naturwissenschaftler, Haupt Verlag, Bern, Stuttgart, Wien, 330 pp., ISBN 978-3-8252-3541-3, 2011. a
Wang, C., Shirley, I., Wielandt, S., Lamb, J., Uhlemann, S., Breen, A., Busey, R. C., Bolton, W. R., Hubbard, S., and Dafflon, B.: Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region, Environ. Res. Lett., 19, 034030, https://doi.org/10.1088/1748-9326/ad27bb, 2024. a
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30, 79–82, 2005. a
Wilson, J. P. and Gallant, J. C.: Primary topographic attributes, 51–85, https://www.researchgate.net/publication/303543730 (last access: 15 August 2024), 2000. a
Wright, M. N. and Zigler, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R., J. Stat. Softw., 77, 1–17, https://doi.org/10.18637/jss.v077.i01, 2017. a
Zhang, K., Chen, S. C., Whitman, D., Shyu, M. L., Yan, J., and Zhang, C.: A progressive morphological filter for removing nonground measurements from airborne LIDAR data, IEEE T. Geosci. Remote, 41, 872–882, https://doi.org/10.1109/TGRS.2003.810682, 2003. a
Zuidhoff, F. S.: Recent decay of a single palsa in relation to weather conditions between 1996 and 2000 in Laivadalen, northern Sweden, Geogr. Ann. A, 84, 103–111, https://doi.org/10.1111/1468-0459.00164, 2002. a
Short summary
Snow has a major impact on palsa dynamics, yet our understanding of its distribution at the small scale remains limited. We used unoccupied aerial system (UAS) light detection and ranging (lidar) and ground truth data in combination with machine learning to model snow distribution at three palsa sites. We identified extremes in snow depth corresponding to palsa topography, providing insights into the influence of the distribution on their dynamics. The results demonstrate the usability of machine learning and UAS lidar for small-scale snow distribution mapping.
Snow has a major impact on palsa dynamics, yet our understanding of its distribution at the...
