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TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications

Sophie Goliber, Taryn Black Orcid Logo, Ginny Catania, James M. Lea Orcid Logo, Helene Olsen, Daniel Cheng Orcid Logo, Suzanne Bevan Orcid Logo, Anders Bjørk Orcid Logo, Charlie Bunce, Stephen Brough Orcid Logo, J. Rachel Carr, Tom Cowton Orcid Logo, Alex Gardner Orcid Logo, Dominik Fahrner Orcid Logo, Emily Hill Orcid Logo, Ian Joughin, Niels J. Korsgaard Orcid Logo, Adrian Luckman Orcid Logo, Twila Moon, Tavi Murray, Andrew Sole Orcid Logo, Michael Wood Orcid Logo, Enze Zhang Orcid Logo

The Cryosphere, Volume: 16, Issue: 8, Pages: 3215 - 3233

Swansea University Authors: Suzanne Bevan Orcid Logo, Adrian Luckman Orcid Logo

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Abstract

Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor in...

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Published in: The Cryosphere
ISSN: 1994-0424
Published: Copernicus GmbH 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa61168
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Abstract: Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet.
College: Faculty of Science and Engineering
Funders: Sophie Goliber has been supported by the NASA Earth and Space Sciences fellowship (18-EARTH18F25323). Michael Wood was supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Technology, administered by the Universities Space Research Association under contract with NASA. James M. Lea is supported by a UKRI Future Leaders Fellowship (grant no. MR/S017232/1). Dominik Fahrner acknowledges support for this study through the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments (grant no. EP/L015927/1). Tavi Murray is funded by the Leverhulme Trust Research Leadership scheme F/00391/J and the UK NERC NE/G010366/1.
Issue: 8
Start Page: 3215
End Page: 3233