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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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DOI (Published version): 10.5194/tc-16-3215-2022

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
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URI: https://cronfa.swan.ac.uk/Record/cronfa61168
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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&#x2009;060 individual terminus traces for 278 glaciers with a mean of 136&#x2009;&#xB1;&#x2009;190 and median of 93 of traces per glacier. Across all glaciers, 32&#x2009;567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17&#x2009;%. We find a median error of &#x223C;&#x2009;100&#x2009;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.</abstract><type>Journal Article</type><journal>The Cryosphere</journal><volume>16</volume><journalNumber>8</journalNumber><paginationStart>3215</paginationStart><paginationEnd>3233</paginationEnd><publisher>Copernicus GmbH</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1994-0424</issnElectronic><keywords/><publishedDay>12</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-08-12</publishedDate><doi>10.5194/tc-16-3215-2022</doi><url/><notes/><college>COLLEGE NANME</college><department>Geography</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SGE</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>Sophie Goliber has been supported by the NASA Earth and Space Sciences fellowship (18-EARTH18F25323). 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spelling 2022-10-31T16:59:29.1914462 v2 61168 2022-09-09 TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications 758d19253522c8c306d4eea0e6e484f6 0000-0003-2649-2982 Suzanne Bevan Suzanne Bevan true false 008cb668b2671b653a88677f075799a9 0000-0002-9618-5905 Adrian Luckman Adrian Luckman true false 2022-09-09 SGE 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. Journal Article The Cryosphere 16 8 3215 3233 Copernicus GmbH 1994-0424 12 8 2022 2022-08-12 10.5194/tc-16-3215-2022 COLLEGE NANME Geography COLLEGE CODE SGE Swansea University 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. 2022-10-31T16:59:29.1914462 2022-09-09T14:32:25.3230372 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Sophie Goliber 1 Taryn Black 0000-0002-7836-3309 2 Ginny Catania 3 James M. Lea 0000-0003-1885-0858 4 Helene Olsen 5 Daniel Cheng 0000-0002-5247-7113 6 Suzanne Bevan 0000-0003-2649-2982 7 Anders Bjørk 0000-0002-4919-792x 8 Charlie Bunce 9 Stephen Brough 0000-0002-6581-6081 10 J. Rachel Carr 11 Tom Cowton 0000-0003-1668-7372 12 Alex Gardner 0000-0002-8394-8889 13 Dominik Fahrner 0000-0002-7895-1557 14 Emily Hill 0000-0003-3175-3163 15 Ian Joughin 16 Niels J. Korsgaard 0000-0002-8700-7023 17 Adrian Luckman 0000-0002-9618-5905 18 Twila Moon 19 Tavi Murray 20 Andrew Sole 0000-0001-5290-8967 21 Michael Wood 0000-0003-3074-7845 22 Enze Zhang 0000-0001-6431-2570 23 61168__25338__13d8f94318be4629afd7aa7f6fdc1dc1.pdf 61168_VoR.pdf 2022-10-06T15:54:16.4086722 Output 8522708 application/pdf Version of Record true © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
title TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
spellingShingle TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
Suzanne Bevan
Adrian Luckman
title_short TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
title_full TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
title_fullStr TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
title_full_unstemmed TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
title_sort TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
author_id_str_mv 758d19253522c8c306d4eea0e6e484f6
008cb668b2671b653a88677f075799a9
author_id_fullname_str_mv 758d19253522c8c306d4eea0e6e484f6_***_Suzanne Bevan
008cb668b2671b653a88677f075799a9_***_Adrian Luckman
author Suzanne Bevan
Adrian Luckman
author2 Sophie Goliber
Taryn Black
Ginny Catania
James M. Lea
Helene Olsen
Daniel Cheng
Suzanne Bevan
Anders Bjørk
Charlie Bunce
Stephen Brough
J. Rachel Carr
Tom Cowton
Alex Gardner
Dominik Fahrner
Emily Hill
Ian Joughin
Niels J. Korsgaard
Adrian Luckman
Twila Moon
Tavi Murray
Andrew Sole
Michael Wood
Enze Zhang
format Journal article
container_title The Cryosphere
container_volume 16
container_issue 8
container_start_page 3215
publishDate 2022
institution Swansea University
issn 1994-0424
doi_str_mv 10.5194/tc-16-3215-2022
publisher Copernicus GmbH
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
description 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.
published_date 2022-08-12T04:19:48Z
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