Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019
Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving f...
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ftdoajarticles:oai:doaj.org/article:f1abd6c58823403e80f8ffe543f8ead8 2023-05-15T16:27:17+02:00 Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 D. Cheng W. Hayes E. Larour Y. Mohajerani M. Wood I. Velicogna E. Rignot 2021-04-01T00:00:00Z https://doi.org/10.5194/tc-15-1663-2021 https://doaj.org/article/f1abd6c58823403e80f8ffe543f8ead8 EN eng Copernicus Publications https://tc.copernicus.org/articles/15/1663/2021/tc-15-1663-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-1663-2021 1994-0416 1994-0424 https://doaj.org/article/f1abd6c58823403e80f8ffe543f8ead8 The Cryosphere, Vol 15, Pp 1663-1675 (2021) Environmental sciences GE1-350 Geology QE1-996.5 article 2021 ftdoajarticles https://doi.org/10.5194/tc-15-1663-2021 2022-12-31T07:28:12Z Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving fronts is time consuming, which limits the availability of these data across a wide spatial and temporal range. Automated methods face challenges that include the handling of clouds, illumination differences, sea ice mélange, and Landsat 7 scan line corrector errors. To address these needs, we develop the Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers, using neural networks. The results are often indistinguishable from manually curated fronts, deviating by on average 86.76 ± 1.43 m from the measured front. Landsat imagery from 1972 to 2019 is used to generate 22 678 calving front lines across 66 Greenlandic glaciers. This improves on the state of the art in terms of the spatiotemporal coverage and accuracy of its outputs and is validated through a comprehensive intercomparison with existing studies. The current implementation offers a new opportunity to explore subseasonal and regional trends on the extent of Greenland's margins and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise. Article in Journal/Newspaper Greenland greenlandic Ice Sheet Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Greenland The Cryosphere 15 3 1663 1675 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
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Environmental sciences GE1-350 Geology QE1-996.5 D. Cheng W. Hayes E. Larour Y. Mohajerani M. Wood I. Velicogna E. Rignot Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving fronts is time consuming, which limits the availability of these data across a wide spatial and temporal range. Automated methods face challenges that include the handling of clouds, illumination differences, sea ice mélange, and Landsat 7 scan line corrector errors. To address these needs, we develop the Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers, using neural networks. The results are often indistinguishable from manually curated fronts, deviating by on average 86.76 ± 1.43 m from the measured front. Landsat imagery from 1972 to 2019 is used to generate 22 678 calving front lines across 66 Greenlandic glaciers. This improves on the state of the art in terms of the spatiotemporal coverage and accuracy of its outputs and is validated through a comprehensive intercomparison with existing studies. The current implementation offers a new opportunity to explore subseasonal and regional trends on the extent of Greenland's margins and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise. |
format |
Article in Journal/Newspaper |
author |
D. Cheng W. Hayes E. Larour Y. Mohajerani M. Wood I. Velicogna E. Rignot |
author_facet |
D. Cheng W. Hayes E. Larour Y. Mohajerani M. Wood I. Velicogna E. Rignot |
author_sort |
D. Cheng |
title |
Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 |
title_short |
Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 |
title_full |
Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 |
title_fullStr |
Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 |
title_full_unstemmed |
Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 |
title_sort |
calving front machine (calfin): glacial termini dataset and automated deep learning extraction method for greenland, 1972–2019 |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-15-1663-2021 https://doaj.org/article/f1abd6c58823403e80f8ffe543f8ead8 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland greenlandic Ice Sheet Sea ice The Cryosphere |
genre_facet |
Greenland greenlandic Ice Sheet Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 15, Pp 1663-1675 (2021) |
op_relation |
https://tc.copernicus.org/articles/15/1663/2021/tc-15-1663-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-1663-2021 1994-0416 1994-0424 https://doaj.org/article/f1abd6c58823403e80f8ffe543f8ead8 |
op_doi |
https://doi.org/10.5194/tc-15-1663-2021 |
container_title |
The Cryosphere |
container_volume |
15 |
container_issue |
3 |
container_start_page |
1663 |
op_container_end_page |
1675 |
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1766016423033831424 |