Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
Delineating the grounding line of marine-terminating glaciers—where ice starts to become afloat in ocean waters—is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mos...
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ftpubmed:oai:pubmedcentral.nih.gov:7925556 2023-05-15T13:48:19+02:00 Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning Mohajerani, Yara Jeong, Seongsu Scheuchl, Bernd Velicogna, Isabella Rignot, Eric Milillo, Pietro 2021-03-02 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925556/ https://doi.org/10.1038/s41598-021-84309-3 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925556/ http://dx.doi.org/10.1038/s41598-021-84309-3 © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. CC-BY Sci Rep Article Text 2021 ftpubmed https://doi.org/10.1038/s41598-021-84309-3 2021-03-07T02:31:27Z Delineating the grounding line of marine-terminating glaciers—where ice starts to become afloat in ocean waters—is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mostly-manual digitizations of differential interferometric synthetic-aperture radar interferograms by human experts. This approach is no longer viable with a fast-growing set of satellite observations and the need to establish time series over entire continents with quantified uncertainties. We present a fully-convolutional neural network with parallel atrous convolutional layers and asymmetric encoder/decoder components that automatically delineates grounding lines at a large scale, efficiently, and accompanied by uncertainty estimates. Our procedure detects grounding lines within 232 m in 100-m posting interferograms, which is comparable to the performance achieved by human experts. We also find value in the machine learning approach in situations that even challenge human experts. We use this approach to map the tidal-induced variability in grounding line position around Antarctica in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West Antarctica, we demonstrate that grounding zones are one order magnitude (13.3 ± 3.9) wider than expected from hydrostatic equilibrium, which justifies the need to map grounding lines repeatedly and comprehensively to inform numerical models. Text Antarc* Antarctica Getz Ice Shelf Ice Sheet Ice Shelf West Antarctica PubMed Central (PMC) Getz ENVELOPE(-145.217,-145.217,-76.550,-76.550) Getz Ice Shelf ENVELOPE(-126.500,-126.500,-74.250,-74.250) West Antarctica Scientific Reports 11 1 |
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Article Mohajerani, Yara Jeong, Seongsu Scheuchl, Bernd Velicogna, Isabella Rignot, Eric Milillo, Pietro Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning |
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Article |
description |
Delineating the grounding line of marine-terminating glaciers—where ice starts to become afloat in ocean waters—is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mostly-manual digitizations of differential interferometric synthetic-aperture radar interferograms by human experts. This approach is no longer viable with a fast-growing set of satellite observations and the need to establish time series over entire continents with quantified uncertainties. We present a fully-convolutional neural network with parallel atrous convolutional layers and asymmetric encoder/decoder components that automatically delineates grounding lines at a large scale, efficiently, and accompanied by uncertainty estimates. Our procedure detects grounding lines within 232 m in 100-m posting interferograms, which is comparable to the performance achieved by human experts. We also find value in the machine learning approach in situations that even challenge human experts. We use this approach to map the tidal-induced variability in grounding line position around Antarctica in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West Antarctica, we demonstrate that grounding zones are one order magnitude (13.3 ± 3.9) wider than expected from hydrostatic equilibrium, which justifies the need to map grounding lines repeatedly and comprehensively to inform numerical models. |
format |
Text |
author |
Mohajerani, Yara Jeong, Seongsu Scheuchl, Bernd Velicogna, Isabella Rignot, Eric Milillo, Pietro |
author_facet |
Mohajerani, Yara Jeong, Seongsu Scheuchl, Bernd Velicogna, Isabella Rignot, Eric Milillo, Pietro |
author_sort |
Mohajerani, Yara |
title |
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning |
title_short |
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning |
title_full |
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning |
title_fullStr |
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning |
title_full_unstemmed |
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning |
title_sort |
automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning |
publisher |
Nature Publishing Group UK |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925556/ https://doi.org/10.1038/s41598-021-84309-3 |
long_lat |
ENVELOPE(-145.217,-145.217,-76.550,-76.550) ENVELOPE(-126.500,-126.500,-74.250,-74.250) |
geographic |
Getz Getz Ice Shelf West Antarctica |
geographic_facet |
Getz Getz Ice Shelf West Antarctica |
genre |
Antarc* Antarctica Getz Ice Shelf Ice Sheet Ice Shelf West Antarctica |
genre_facet |
Antarc* Antarctica Getz Ice Shelf Ice Sheet Ice Shelf West Antarctica |
op_source |
Sci Rep |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925556/ http://dx.doi.org/10.1038/s41598-021-84309-3 |
op_rights |
© The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-021-84309-3 |
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Scientific Reports |
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