AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini
Ice sheet marine margins via outlet glaciers are susceptible to climate change and are expected to respond through retreat, steepening, and acceleration, although with significant spatial heterogeneity. However, research on ice–ocean interactions has continued to rely on decentralized, manual mappin...
Published in: | The Cryosphere |
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Format: | Article in Journal/Newspaper |
Language: | English |
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Copernicus Publications
2023
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Online Access: | https://doi.org/10.5194/tc-17-3485-2023 https://noa.gwlb.de/receive/cop_mods_00068490 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066917/tc-17-3485-2023.pdf https://tc.copernicus.org/articles/17/3485/2023/tc-17-3485-2023.pdf |
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00068490 2023-09-26T15:18:08+02:00 AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini Zhang, Enze Catania, Ginny Trugman, Daniel T. 2023-08 electronic https://doi.org/10.5194/tc-17-3485-2023 https://noa.gwlb.de/receive/cop_mods_00068490 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066917/tc-17-3485-2023.pdf https://tc.copernicus.org/articles/17/3485/2023/tc-17-3485-2023.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-17-3485-2023 https://noa.gwlb.de/receive/cop_mods_00068490 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066917/tc-17-3485-2023.pdf https://tc.copernicus.org/articles/17/3485/2023/tc-17-3485-2023.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2023 ftnonlinearchiv https://doi.org/10.5194/tc-17-3485-2023 2023-08-27T23:20:34Z Ice sheet marine margins via outlet glaciers are susceptible to climate change and are expected to respond through retreat, steepening, and acceleration, although with significant spatial heterogeneity. However, research on ice–ocean interactions has continued to rely on decentralized, manual mapping of features at the ice–ocean interface, impeding progress in understanding the response of glaciers and ice sheets to climate change. The proliferation of remote-sensing images lays the foundation for a better understanding of ice–ocean interactions and also necessitates the automation of terminus delineation. While deep learning (DL) techniques have already been applied to automate the terminus delineation, none involve sufficient quality control and automation to enable DL applications to “big data” problems in glaciology. Here, we build on established methods to create a fully automated pipeline for terminus delineation that makes several advances over prior studies. First, we leverage existing manually picked terminus traces (16 440) as training data to significantly improve the generalization of the DL algorithm. Second, we employ a rigorous automated screening module to enhance the data product quality. Third, we perform a thoroughly automated uncertainty quantification on the resulting data. Finally, we automate several steps in the pipeline allowing data to be regularly delivered to public databases with increased frequency. The automation level of our method ensures the sustainability of terminus data production. Altogether, these improvements produce the most complete and high-quality record of terminus data that exists for the Greenland Ice Sheet (GrIS). Our pipeline has successfully picked 278 239 termini for 295 glaciers in Greenland from Landsat 5, 7, 8 and Sentinel-1 and Sentinel-2 images, spanning the period from 1984 to 2021. The pipeline has been tested on glaciers in Greenland with an error of 79 m. The high sampling frequency and the controlled quality of our terminus data will enable better ... Article in Journal/Newspaper glacier Greenland Ice Sheet The Cryosphere Niedersächsisches Online-Archiv NOA Greenland The Cryosphere 17 8 3485 3503 |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Zhang, Enze Catania, Ginny Trugman, Daniel T. AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini |
topic_facet |
article Verlagsveröffentlichung |
description |
Ice sheet marine margins via outlet glaciers are susceptible to climate change and are expected to respond through retreat, steepening, and acceleration, although with significant spatial heterogeneity. However, research on ice–ocean interactions has continued to rely on decentralized, manual mapping of features at the ice–ocean interface, impeding progress in understanding the response of glaciers and ice sheets to climate change. The proliferation of remote-sensing images lays the foundation for a better understanding of ice–ocean interactions and also necessitates the automation of terminus delineation. While deep learning (DL) techniques have already been applied to automate the terminus delineation, none involve sufficient quality control and automation to enable DL applications to “big data” problems in glaciology. Here, we build on established methods to create a fully automated pipeline for terminus delineation that makes several advances over prior studies. First, we leverage existing manually picked terminus traces (16 440) as training data to significantly improve the generalization of the DL algorithm. Second, we employ a rigorous automated screening module to enhance the data product quality. Third, we perform a thoroughly automated uncertainty quantification on the resulting data. Finally, we automate several steps in the pipeline allowing data to be regularly delivered to public databases with increased frequency. The automation level of our method ensures the sustainability of terminus data production. Altogether, these improvements produce the most complete and high-quality record of terminus data that exists for the Greenland Ice Sheet (GrIS). Our pipeline has successfully picked 278 239 termini for 295 glaciers in Greenland from Landsat 5, 7, 8 and Sentinel-1 and Sentinel-2 images, spanning the period from 1984 to 2021. The pipeline has been tested on glaciers in Greenland with an error of 79 m. The high sampling frequency and the controlled quality of our terminus data will enable better ... |
format |
Article in Journal/Newspaper |
author |
Zhang, Enze Catania, Ginny Trugman, Daniel T. |
author_facet |
Zhang, Enze Catania, Ginny Trugman, Daniel T. |
author_sort |
Zhang, Enze |
title |
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini |
title_short |
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini |
title_full |
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini |
title_fullStr |
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini |
title_full_unstemmed |
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini |
title_sort |
autoterm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of greenland glacier termini |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-3485-2023 https://noa.gwlb.de/receive/cop_mods_00068490 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066917/tc-17-3485-2023.pdf https://tc.copernicus.org/articles/17/3485/2023/tc-17-3485-2023.pdf |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
glacier Greenland Ice Sheet The Cryosphere |
genre_facet |
glacier Greenland Ice Sheet The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-17-3485-2023 https://noa.gwlb.de/receive/cop_mods_00068490 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066917/tc-17-3485-2023.pdf https://tc.copernicus.org/articles/17/3485/2023/tc-17-3485-2023.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/tc-17-3485-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
8 |
container_start_page |
3485 |
op_container_end_page |
3503 |
_version_ |
1778140133064704000 |