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...
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ftmdpi:oai:mdpi.com:/2072-4292/12/7/1203/ 2023-08-20T03:59:30+02:00 Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach Mariel Dirscherl Andreas J. Dietz Christof Kneisel Claudia Kuenzer agris 2020-04-08 application/pdf https://doi.org/10.3390/rs12071203 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs12071203 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 7; Pages: 1203 Antarctica Antarctic ice sheet supraglacial lakes surface melt hydrology ice sheet dynamics sentinel-2 remote sensing random forest machine learning Text 2020 ftmdpi https://doi.org/10.3390/rs12071203 2023-07-31T23:20:51Z 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 km2). 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 methodological ... Text Amery Ice Shelf Antarc* Antarctic Antarctica George VI Ice Shelf Ice Sheet Ice Shelf Ice Shelves West Antarctica MDPI Open Access Publishing Antarctic The Antarctic West Antarctica Amery ENVELOPE(-94.063,-94.063,56.565,56.565) Amery Ice Shelf ENVELOPE(71.000,71.000,-69.750,-69.750) Abbott ENVELOPE(-62.133,-62.133,-64.100,-64.100) George VI Ice Shelf ENVELOPE(-67.840,-67.840,-71.692,-71.692) Remote Sensing 12 7 1203 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Antarctica Antarctic ice sheet supraglacial lakes surface melt hydrology ice sheet dynamics sentinel-2 remote sensing random forest machine learning |
spellingShingle |
Antarctica Antarctic ice sheet supraglacial lakes surface melt hydrology ice sheet dynamics sentinel-2 remote sensing random forest machine learning Mariel Dirscherl Andreas J. Dietz Christof Kneisel Claudia Kuenzer Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach |
topic_facet |
Antarctica Antarctic ice sheet supraglacial lakes surface melt hydrology ice sheet dynamics sentinel-2 remote sensing random forest machine learning |
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 km2). 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 methodological ... |
format |
Text |
author |
Mariel Dirscherl Andreas J. Dietz Christof Kneisel Claudia Kuenzer |
author_facet |
Mariel Dirscherl Andreas J. Dietz Christof Kneisel Claudia Kuenzer |
author_sort |
Mariel Dirscherl |
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 |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12071203 |
op_coverage |
agris |
long_lat |
ENVELOPE(-94.063,-94.063,56.565,56.565) ENVELOPE(71.000,71.000,-69.750,-69.750) ENVELOPE(-62.133,-62.133,-64.100,-64.100) ENVELOPE(-67.840,-67.840,-71.692,-71.692) |
geographic |
Antarctic The Antarctic West Antarctica Amery Amery Ice Shelf Abbott George VI Ice Shelf |
geographic_facet |
Antarctic The Antarctic West Antarctica Amery Amery Ice Shelf Abbott George VI Ice Shelf |
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_source |
Remote Sensing; Volume 12; Issue 7; Pages: 1203 |
op_relation |
https://dx.doi.org/10.3390/rs12071203 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
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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1774713320509538304 |