4.6 Article

Automated ArcticDEM iceberg detection tool: insights into area and volumedistributions, and their potential application to satellite imagery andmodelling of glacier-iceberg-ocean systems

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CRYOSPHERE
卷 17, 期 1, 页码 15-32

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COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/tc-17-15-2023

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Iceberg calving is a significant factor contributing to mass loss from the Greenland Ice Sheet. A highly automated workflow using high spatial resolution ArcticDEM data was developed to detect icebergs and append their associated metadata within Google Earth Engine. The study analyzed three glaciers and successfully detected a large number of icebergs, contributing to insights into iceberg distributions and their impact on glacier mass loss and fjord freshwater fluxes. The workflow offers the potential to interrogate iceberg data on a pan-Arctic scale.
Iceberg calving accounts for up to half of mass loss from the Greenland Ice Sheet (GrIS), with their size distributions providing insights into glacier calving dynamics and impacting fjord environments through their melting and subsequent freshwater release. Iceberg area and volume data for the GrIS are currently limited to a handful of fjord locations, while existing approaches to iceberg detection are of -ten time-consuming and are not always suited for long time series analysis over large spatial scales. This study presents a highly automated workflow that detects icebergs and appends their associated metadata within Google Earth Engine using high spatial resolution timestamped ArcticDEM (Arc-tic Digital Elevation Model) strip data. This is applied to three glaciers that exhibit a range of different iceberg concentrations and size distributions: Sermeq Kujalleq (Jakobshavn Isbr ae), Umiammakku Isbr ae and Kangiata Nunaata Sermia. A total of 39 ArcticDEM scenes are analysed, detecting a total of 163 738 icebergs with execution times of 6 min to 2 h for each glacier depending on the number of DEMs avail-able and total area analysed, comparing well with the map-ping of manually digitised outlines. Results reveal two distinct iceberg distributions at Sermeq Kujalleq and Kangiata Nunaata Sermia where iceberg density is high, and one dis-tribution at Umiammakku Isbr ae where iceberg density is low. Small icebergs (< 1000 m(2)) are found to account for over 80 % of each glacier's icebergs; however, they only con-tribute to 10 %-37 % of total iceberg volume suggesting that large icebergs are proportionally more important for glacier mass loss and as fjord freshwater reservoirs. The overall dataset is used to construct new area-to-volume conversions (with associated uncertainties) that can be applied elsewhere to two-dimensional iceberg outlines derived from optical or synthetic aperture radar imagery. When data are expressed in terms of total iceberg count and volume, insight is provided into iceberg distributions that have potential applicability to observations and modelling of iceberg calving behaviour and fjord freshwater fluxes. Due to the speed and automated nature of our approach, this workflow offers the potential to interrogate iceberg data on a pan-Arctic scale where Arctic -DEM strip data coverage allows.

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