Articles | Volume 17, issue 1
https://doi.org/10.5194/tc-17-15-2023
https://doi.org/10.5194/tc-17-15-2023
Research article
 | 
09 Jan 2023
Research article |  | 09 Jan 2023

Automated ArcticDEM iceberg detection tool: insights into area and volume distributions, and their potential application to satellite imagery and modelling of glacier–iceberg–ocean systems

Connor J. Shiggins, James M. Lea, and Stephen Brough

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Cited articles

Alstott, J., Bullmore, E., and Plenz, D.: powerlaw: a Python package for analysis of heavy-tailed distributions, PloS one, 9, e85777, https://doi.org/10.1371/journal.pone.0095816, 2014. 
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Bartholomaus, T. C., Stearns, L. A., Sutherland, D. A., Shroyer, E. L., Nash, J. D., Walker, R. T., Catania, G., Felikson, D., Carroll, D., Fried, M. J., and Noël, B. P.: Contrasts in the response of adjacent fjords and glaciers to ice-sheet surface melt in West Greenland, Ann. Glaciol., 57, 25–38, https://doi.org/10.1017/aog.2016.19, 2016. 
Bigg, G. R.: Icebergs: their science and links to global change, Cambridge University Press, https://doi.org/10.1017/CBO978110758927, 2015. 
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Short summary
Iceberg detection is spatially and temporally limited around the Greenland Ice Sheet. This study presents a new, accessible workflow to automatically detect icebergs from timestamped ArcticDEM strip data. The workflow successfully produces comparable output to manual digitisation, with results revealing new iceberg area-to-volume conversion equations that can be widely applied to datasets where only iceberg outlines can be extracted (e.g. optical and SAR imagery).