Articles | Volume 16, issue 2
https://doi.org/10.5194/essd-16-919-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-919-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
20 Feb 2024
Data description paper |
| 20 Feb 2024
| 20 Feb 2024
A high-resolution calving front data product for marine-terminating glaciers in Svalbard
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Konrad Heidler
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Lichao Mou
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Ádám Ignéczi
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Xiao Xiang Zhu
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Munich Center for Machine Learning, Technical University of Munich, Munich 80333, Germany
Jonathan L. Bamber
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
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Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
The Cryosphere, 17, 1003–1022, https://doi.org/10.5194/tc-17-1003-2023, https://doi.org/10.5194/tc-17-1003-2023, 2023
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Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
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Sam Royston, Rory J. Bingham, and Jonathan L. Bamber
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S. Zhao, S. Saha, and X. X. Zhu
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S. Saha, J. Gawlikowski, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 423–428, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-423-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-423-2022, 2022
T. Beker, H. Ansari, S. Montazeri, Q. Song, and X. X. Zhu
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K. R. Traoré, A. Camero, and X. X. Zhu
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Stephen J. Chuter, Andrew Zammit-Mangion, Jonathan Rougier, Geoffrey Dawson, and Jonathan L. Bamber
The Cryosphere, 16, 1349–1367, https://doi.org/10.5194/tc-16-1349-2022, https://doi.org/10.5194/tc-16-1349-2022, 2022
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Tom Mitcham, G. Hilmar Gudmundsson, and Jonathan L. Bamber
The Cryosphere, 16, 883–901, https://doi.org/10.5194/tc-16-883-2022, https://doi.org/10.5194/tc-16-883-2022, 2022
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Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
Earth Syst. Sci. Data, 14, 535–557, https://doi.org/10.5194/essd-14-535-2022, https://doi.org/10.5194/essd-14-535-2022, 2022
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Fanny Lehmann, Bramha Dutt Vishwakarma, and Jonathan Bamber
Hydrol. Earth Syst. Sci., 26, 35–54, https://doi.org/10.5194/hess-26-35-2022, https://doi.org/10.5194/hess-26-35-2022, 2022
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Y. Xie, K. Schindler, J. Tian, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, 2021
P. Ebel, S. Saha, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 243–249, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-243-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-243-2021, 2021
S. Saha, L. Kondmann, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 311–316, https://doi.org/10.5194/isprs-annals-V-3-2021-311-2021, https://doi.org/10.5194/isprs-annals-V-3-2021-311-2021, 2021
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
The Cryosphere, 14, 3629–3643, https://doi.org/10.5194/tc-14-3629-2020, https://doi.org/10.5194/tc-14-3629-2020, 2020
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Accurate knowledge of the Antarctic grounding zone is critical for the understanding of ice sheet instability and the evaluation of mass balance. We present a new, fully automated method to map the grounding zone from ICESat-2 laser altimetry. Our results of Larsen C Ice Shelf demonstrate the efficiency, density, and high spatial accuracy with which ICESat-2 can image complex grounding zones.
Cited articles
Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Automated extraction of antarctic glacier and ice shelf fronts from Sentinel-1 imagery using deep learning, Remote Sens., 11, 2529, https://doi.org/10.3390/rs11212529, 2019.
Baumhoer, C. A., Dietz, A. J., Heidler, K., and Kuenzer, C.: IceLines – A new data set of Antarctic ice shelf front positions, Sci. Data, 10, 138, https://doi.org/10.1038/s41597-023-02045-x, 2023.
Benn, D. I. and Åström, J. A.: Calving glaciers and ice shelves, Adv. Phys. X, 3, 1048–1076, https://doi.org/10.1080/23746149.2018.1513819, 2018.
Benn, D. I., Warren, C. R., and Mottram, R. H.: Calving processes and the dynamics of calving glaciers, Earth-Sci. Rev., 82, 143–179, https://doi.org/10.1016/J.EARSCIREV.2007.02.002, 2007.
Błaszczyk, M., Jacek, J., and Jon, O. H.: Tidewater Glaciers of Svalbard: Recent changes and estimates of calving fluxes, Polish Polar Res., 30, 85–142, https://opus.us.edu.pl/info/article/USL749ae2908280495bb99a7e046bb7cef1/ (last access: 9 August 2023), 2009.
Carr, J. R., Stokes, C. R., and Vieli, A.: Recent progress in understanding marine-terminating Arctic outlet glacier response to climatic and oceanic forcing: Twenty years of rapid change, Prog. Phys. Geogr., 37, 436–467, https://doi.org/10.1177/0309133313483163, 2013.
Carr, J. R., Stokes, C. R., and Vieli, A.: Threefold increase in marine-terminating outlet glacier retreat rates across the Atlantic Arctic: 1992–2010, Ann. Glaciol., 58, 72–91, https://doi.org/10.1017/AOG.2017.3, 2017.
Catania, G. A., Stearns, L. A., Moon, T. A., Enderlin, E. M., and Jackson, R. H.: Future Evolution of Greenland's Marine-Terminating Outlet Glaciers, J. Geophys. Res.-Earth, 125, e2018JF004873, https://doi.org/10.1029/2018JF004873, 2020.
Cheng, D., Hayes, W., Larour, E., Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019, The Cryosphere, 15, 1663–1675, https://doi.org/10.5194/tc-15-1663-2021, 2021.
Chollet, F.: Xception: Deep Learning with Depthwise Separable Convolutions, in: Proc. – 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, January 2017, 1800–1807, https://doi.org/10.1109/CVPR.2017.195, 2016.
Cook, A. J., Copland, L., Noël, B. P. Y., Stokes, C. R., Bentley, M. J., Sharp, M. J., Bingham, R. G., and van den Broeke, M. R.: Atmospheric forcing of rapid marine-terminating glacier retreat in the Canadian Arctic Archipelago, Sci. Adv., 5, eaau8507, https://doi.org/10.1126/SCIADV.AAU8507, 2019.
Cowton, T. R., Sole, A. J., Nienow, P. W., Slater, D. A., and Christoffersen, P.: Linear response of east Greenland's tidewater glaciers to ocean/atmosphere warming, P. Natl. Acad. Sci. USA, 115, 7907–7912, https://doi.org/10.1073/PNAS.1801769115, 2018.
Davies, T. M., Marshall, J. C., and Hazelton, M. L.: Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk, Stat. Med., 37, 1191–1221, https://doi.org/10.1002/sim.7577, 2018.
European Space Agency (ESA): Copernicus DEM – Global and European Digital Elevation Model (COP-DEM), ESA [data set], https://doi.org/10.5270/ESA-c5d3d65, 2021.
Geyman, E. C., van Pelt, W. J. J., Maloof, A. C., Aas, H. F., and Kohler, J.: Historical glacier change on Svalbard predicts doubling of mass loss by 2100, Nature, 601, 374–379, https://doi.org/10.1038/s41586-021-04314-4, 2022.
Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery, Earth Syst. Sci. Data, 14, 4287–4313, https://doi.org/10.5194/essd-14-4287-2022, 2022.
Heidler, K.: khdlr/COBRA: v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.8407566, 2023.
Heidler, K., Mou, L., Baumhoer, C., Dietz, A., and Zhu, X. X.: HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline, IEEE T. Geosci. Remote, 60, 4300514, https://doi.org/10.1109/TGRS.2021.3064606, 2022.
Heidler, K., Mou, L., Loebel, E., Scheinert, M., Lefèvre, S., and Zhu, X. X.: A Deep Active Contour Model for Delineating Glacier Calving Fronts, IEEE T. Geosci. Remote, 61, 5615912, https://doi.org/10.1109/TGRS.2023.3296539, 2023.
Holmes, F. A., Kirchner, N., Kuttenkeuler, J., Krützfeldt, J., and Noormets, R.: Relating ocean temperatures to frontal ablation rates at Svalbard tidewater glaciers: Insights from glacier proximal datasets, Sci. Rep., 9, 9442, https://doi.org/10.1038/s41598-019-45077-3, 2019.
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021.
Intergovernmental Panel on Climate Change: Climate Change 2021 – The Physical Science Basis, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2023.
Jiskoot, H., Murray, T., and Boyle, P.: Controls on the distribution of surge-type glaciers in Svalbard, J. Glaciol., 46, 412–422, https://doi.org/10.3189/172756500781833115, 2000.
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv preprint, https://doi.org/10.48550/arXiv.1412.6980, 2014.
Kochtitzky, W. and Copland, L.: Retreat of Northern Hemisphere Marine-Terminating Glaciers, 2000–2020, Geophys. Res. Lett., 49, e2021GL096501, https://doi.org/10.1029/2021GL096501, 2022.
Kochtitzky, W., Copland, L., Van Wychen, W., Hugonnet, R., Hock, R., Dowdeswell, J. A., Benham, T., Strozzi, T., Glazovsky, A., Lavrentiev, I., Rounce, D. R., Millan, R., Cook, A., Dalton, A., Jiskoot, H., Cooley, J., Jania, J., and Navarro, F.: The unquantified mass loss of Northern Hemisphere marine-terminating glaciers from 2000–2020, Nat. Commun., 13, 5835, https://doi.org/10.1038/s41467-022-33231-x, 2022.
Kochtitzky, W., Copland, L., Van Wychen, W., Hock, R., Rounce, D. R., Jiskoot, H., Scambos, T. A., Morlighem, M., King, M., Cha, L., Gould, L., Merrill, P. M., Glazovsky, A., Hugonnet, R., Strozzi, T., Noël, B., Navarro, F., Millan, R., Dowdeswell, J. A., Cook, A., Dalton, A., Khan, S., and Jania, J.: Progress toward globally complete frontal ablation estimates of marine-terminating glaciers, Ann. Glaciol., 63, 143–152, https://doi.org/10.1017/aog.2023.35, 2023.
Li, T., Heidler, K., Mou, L., Ignéczi, Á., Zhu, X. X., and Bamber, J.: Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985–2023, Zenodo [data set], https://doi.org/10.5281/zenodo.10407266, 2023.
Loebel, E., Scheinert, M., Horwath, M., Heidler, K., Christmann, J., Phan, L. D., Humbert, A., and Zhu, X. X.: Extracting glacier calving fronts by deep learning: the benefit of multi-spectral, topographic and textural input features, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2022.3208454, 2022.
Loebel, E., Scheinert, M., Horwath, M., Humbert, A., Sohn, J., Heidler, K., Liebezeit, C., and Zhu, X. X.: Calving front monitoring at sub-seasonal resolution: a deep learning application to Greenland glaciers, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2023-52, in review, 2023.
Luckman, A., Benn, D. I., Cottier, F., Bevan, S., Nilsen, F., and Inall, M.: Calving rates at tidewater glaciers vary strongly with ocean temperature, Nat. Commun., 6, 8566, https://doi.org/10.1038/ncomms9566, 2015.
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T., and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, 2019.
McNabb, R. W. and Hock, R.: Alaska tidewater glacier terminus positions, 1948–2012, J. Geophys. Res.-Earth, 119, 153–167, https://doi.org/10.1002/2013JF002915, 2014.
Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C., Ottersen, G., Pritchard, H., and Schuur, E. A. G.: Polar Regions, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, Cambridge University Press, 203–320, https://doi.org/10.1017/9781009157964.005, 2019.
Misund, O. A., Heggland, K., Skogseth, R., Falck, E., Gjøsæter, H., Sundet, J., Watne, J., and Lønne, O. J.: Norwegian fisheries in the Svalbard zone since 1980. Regulations, profitability and warming waters affect landings, Polar Sci., 10, 312–322, https://doi.org/10.1016/J.POLAR.2016.02.001, 2016.
Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Detection of glacier calving margins with convolutional neural networks: A case study, Remote Sens., 11, 74, https://doi.org/10.3390/rs11010074, 2019.
Moholdt, G., Maton, J., Majerska, M., and Kohler, J.: Annual frontlines of marine-terminating glaciers on Svalbard, https://data.npolar.no/dataset/d60a919a-9cc8-4048-9686-df81bfdc2338 (last access: 16 May 2023), 2022.
Müller, M.: Dynamic Time Warping, Inf. Retr. Music Motion, 69–84, https://doi.org/10.1007/978-3-540-74048-3_4, 2007.
Murray, T., Scharrer, K., Selmes, N., Booth, A. D., James, T. D., Bevan, S. L., Bradley, J., Cook, S., Llana, L. C., Drocourt, Y., Dyke, L., Goldsack, A., Hughes, A. L., Luckman, A. J., and McGovern, J.: Extensive Retreat of Greenland Tidewater Glaciers, 2000–2010, Arctic, Antarct. Alp. Res., 47, 427–447, https://doi.org/10.1657/AAAR0014-049, 2015.
Noël, B., Jakobs, C. L., van Pelt, W. J. J., Lhermitte, S., Wouters, B., Kohler, J., Hagen, J. O., Luks, B., Reijmer, C. H., van de Berg, W. J., and van den Broeke, M. R.: Low elevation of Svalbard glaciers drives high mass loss variability, Nat. Commun., 11, 4597, https://doi.org/10.1038/s41467-020-18356-1, 2020.
Nordli, Ø., Wyszyński, P., Gjelten, H. M., Isaksen, K., Łupikasza, E., Niedźwiedź, T., and Przybylak, R.: Revisiting the extended Svalbard Airport monthly temperature series, and the compiled corresponding daily series 1898–2018, Polar Res., 39, https://doi.org/10.33265/POLAR.V39.3614, 2020.
Nuth, C., Moholdt, G., Kohler, J., Hagen, J. O., and Kääb, A.: Svalbard glacier elevation changes and contribution to sea level rise, J. Geophys. Res., 115, F01008, https://doi.org/10.1029/2008JF001223, 2010.
Nuth, C., Kohler, J., König, M., von Deschwanden, A., Hagen, J. O., Kääb, A., Moholdt, G., and Pettersson, R.: Decadal changes from a multi-temporal glacier inventory of Svalbard, The Cryosphere, 7, 1603–1621, https://doi.org/10.5194/tc-7-1603-2013, 2013.
Nuth, C., Gilbert, A., Köhler, A., McNabb, R., Schellenberger, T., Sevestre, H., Weidle, C., Girod, L., Luckman, A., and Kääb, A.: Dynamic vulnerability revealed in the collapse of an Arctic tidewater glacier, Sci. Rep., 9, 5541, https://doi.org/10.1038/s41598-019-41117-0, 2019.
Oppenheimer, M., Glavovic, B. C., Hinkel, J., van de Wal, R. S. W., Magnan, A. K., Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Barrett, K., Seneviratne, S. I., and Macbean, N., Cambridge University Press, Cambridge, UK and New York, NY, USA, 321–445, 2019.
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen, J. O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B. H., Rich, J., Sharp, M. J., Andreassen, L. M., Bajracharya, S., Barrand, N. E., Beedle, M. J., Berthier, E., Bhambri, R., Brown, I., Burgess, D. O., Burgess, E. W., Cawkwell, F., Chinn, T., Copland, L., Cullen, N. J., Davies, B., De Angelis, H., Fountain, A. G., Frey, H., Giffen, B. A., Glasser, N. F., Gurney, S. D., Hagg, W., Hall, D. K., Haritashya, U. K., Hartmann, G., Herreid, S., Howat, I., Jiskoot, H., Khromova, T. E., Klein, A., Kohler, J., König, M., Kriegel, D., Kutuzov, S., Lavrentiev, I., Le Bris, R., Li, X., Manley, W. F., Mayer, C., Menounos, B., Mercer, A., Mool, P., Negrete, A., Nosenko, G., Nuth, C., Osmonov, A., Pettersson, R., Racoviteanu, A., Ranzi, R., Sarikaya, M. A., Schneider, C., Sigurdsson, O., Sirguey, P., Stokes, C. R., Wheate, R., Wolken, G. J., Wu, L. Z., and Wyatt, F. R.: The Randolph Glacier Inventory: a globally complete inventory of glaciers, J. Glaciol., 60, 537–552, https://doi.org/10.3189/2014JOG13J176, 2014.
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022.
RGI Consortium: Randolph Glacier Inventory (RGI) – A Dataset of Global Glacier Outlines: Version 6.0, NSIDC [data set], https://doi.org/10.7265/N5-RGI-60, 2017.
Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023.
Schuler, T. V., Kohler, J., Elagina, N., Hagen, J. O. M., Hodson, A. J., Jania, J. A., Kääb, A. M., Luks, B., Małecki, J., Moholdt, G., Pohjola, V. A., Sobota, I., and Van Pelt, W. J. J.: Reconciling Svalbard Glacier Mass Balance, Front. Earth Sci., 8, 156, https://doi.org/10.3389/feart.2020.00156, 2020.
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification: A research synthesis, Glob. Planet. Change, 77, 85–96, https://doi.org/10.1016/J.GLOPLACHA.2011.03.004, 2011.
Skogseth, R., Haugan, P. M., and Jakobsson, M.: Watermass transformations in Storfjorden, Cont. Shelf Res., 25, 667–695, https://doi.org/10.1016/J.CSR.2004.10.005, 2005.
Strozzi, T., Kääb, A., and Schellenberger, T.: Frontal destabilization of Stonebreen, Edgeøya, Svalbard, The Cryosphere, 11, 553–566, https://doi.org/10.5194/tc-11-553-2017, 2017.
van Pelt, W. J. J., Pohjola, V. A., Pettersson, R., Ehwald, L. E., Reijmer, C. H., Boot, W., and Jakobs, C. L.: Dynamic Response of a High Arctic Glacier to Melt and Runoff Variations, Geophys. Res. Lett., 45, 4917–4926, https://doi.org/10.1029/2018GL077252, 2018.
Zhang, E., Liu, L., and Huang, L.: Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal TerraSAR-X images: a deep learning approach, The Cryosphere, 13, 1729–1741, https://doi.org/10.5194/tc-13-1729-2019, 2019.
Zhang, E., Catania, G., and Trugman, D. T.: AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini, The Cryosphere, 17, 3485–3503, https://doi.org/10.5194/tc-17-3485-2023, 2023.
Short summary
Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.
Our study uses deep learning to produce a new high-resolution calving front dataset for 149...
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