Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach
Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mas...
Published in: | Remote Sensing |
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Online Access: | https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/20373 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-203735 https://doi.org/10.3390/rs12071203 https://opus.bibliothek.uni-wuerzburg.de/files/20373/remotesensing-12-01203-v2.pdf |
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ftunivwuerz:oai:opus.bibliothek.uni-wuerzburg.de:20373 2023-09-05T13:11:41+02:00 Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach Dirscherl, Mariel Dietz, Andreas J. Kneisel, Christof Kuenzer, Claudia 2020 application/pdf https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/20373 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-203735 https://doi.org/10.3390/rs12071203 https://opus.bibliothek.uni-wuerzburg.de/files/20373/remotesensing-12-01203-v2.pdf eng eng https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/20373 urn:nbn:de:bvb:20-opus-203735 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-203735 https://doi.org/10.3390/rs12071203 https://opus.bibliothek.uni-wuerzburg.de/files/20373/remotesensing-12-01203-v2.pdf https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess ddc:526 ddc:550 article doc-type:article 2020 ftunivwuerz https://doi.org/10.3390/rs12071203 2023-08-13T22:34:21Z Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further ... Article in Journal/Newspaper Amery Ice Shelf Antarc* Antarctic Antarctica George VI Ice Shelf Ice Sheet Ice Shelf Ice Shelves West Antarctica Würzburg University: Online Publication Service Abbott ENVELOPE(-62.133,-62.133,-64.100,-64.100) Amery ENVELOPE(-94.063,-94.063,56.565,56.565) Amery Ice Shelf ENVELOPE(71.000,71.000,-69.750,-69.750) Antarctic George VI Ice Shelf ENVELOPE(-67.840,-67.840,-71.692,-71.692) The Antarctic West Antarctica Remote Sensing 12 7 1203 |
institution |
Open Polar |
collection |
Würzburg University: Online Publication Service |
op_collection_id |
ftunivwuerz |
language |
English |
topic |
ddc:526 ddc:550 |
spellingShingle |
ddc:526 ddc:550 Dirscherl, Mariel Dietz, Andreas J. Kneisel, Christof Kuenzer, Claudia Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach |
topic_facet |
ddc:526 ddc:550 |
description |
Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further ... |
format |
Article in Journal/Newspaper |
author |
Dirscherl, Mariel Dietz, Andreas J. Kneisel, Christof Kuenzer, Claudia |
author_facet |
Dirscherl, Mariel Dietz, Andreas J. Kneisel, Christof Kuenzer, Claudia |
author_sort |
Dirscherl, Mariel |
title |
Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach |
title_short |
Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach |
title_full |
Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach |
title_fullStr |
Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach |
title_full_unstemmed |
Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach |
title_sort |
automated mapping of antarctic supraglacial lakes using a machine learning approach |
publishDate |
2020 |
url |
https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/20373 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-203735 https://doi.org/10.3390/rs12071203 https://opus.bibliothek.uni-wuerzburg.de/files/20373/remotesensing-12-01203-v2.pdf |
long_lat |
ENVELOPE(-62.133,-62.133,-64.100,-64.100) ENVELOPE(-94.063,-94.063,56.565,56.565) ENVELOPE(71.000,71.000,-69.750,-69.750) ENVELOPE(-67.840,-67.840,-71.692,-71.692) |
geographic |
Abbott Amery Amery Ice Shelf Antarctic George VI Ice Shelf The Antarctic West Antarctica |
geographic_facet |
Abbott Amery Amery Ice Shelf Antarctic George VI Ice Shelf The Antarctic West Antarctica |
genre |
Amery Ice Shelf Antarc* Antarctic Antarctica George VI Ice Shelf Ice Sheet Ice Shelf Ice Shelves West Antarctica |
genre_facet |
Amery Ice Shelf Antarc* Antarctic Antarctica George VI Ice Shelf Ice Sheet Ice Shelf Ice Shelves West Antarctica |
op_relation |
https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/20373 urn:nbn:de:bvb:20-opus-203735 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-203735 https://doi.org/10.3390/rs12071203 https://opus.bibliothek.uni-wuerzburg.de/files/20373/remotesensing-12-01203-v2.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs12071203 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
7 |
container_start_page |
1203 |
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1776196789640101888 |