Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery
<p>Calving front positions of marine-terminating glaciers are an essential parameter for understanding dynamic glacier changes and constraining ice modelling. In particular, for the Antarctic Peninsula, where the current ice mass loss is driven by dynamic glacier changes, accurate and comprehe...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-01-01
|
| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/65/2025/essd-17-65-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841550091859525632 |
|---|---|
| author | E. Loebel E. Loebel C. A. Baumhoer A. Dietz M. Scheinert M. Horwath |
| author_facet | E. Loebel E. Loebel C. A. Baumhoer A. Dietz M. Scheinert M. Horwath |
| author_sort | E. Loebel |
| collection | DOAJ |
| description | <p>Calving front positions of marine-terminating glaciers are an essential parameter for understanding dynamic glacier changes and constraining ice modelling. In particular, for the Antarctic Peninsula, where the current ice mass loss is driven by dynamic glacier changes, accurate and comprehensive data products are of major importance. Current calving front data products are limited in coverage and temporal resolution because they rely on manual delineation, which is time-consuming and unfeasible for the increasing amount of satellite data. To simplify the mapping of calving fronts, we apply a deep-learning-based processing system designed to automatically delineate glacier fronts from multi-spectral Landsat imagery. The U-Net-based framework was initially trained on 869 Greenland glacier front positions. For this study, we extended the training data by 252 front positions of the Antarctic Peninsula. The data product presented here includes 4817 calving front locations of 42 key outlet glaciers from 2013 to 2023 and achieves a sub-seasonal temporal resolution. The mean difference between automated and manual extraction is estimated at <span class="inline-formula">59.3±5.9</span> <span class="inline-formula">m</span>. This dataset will help to better understand marine-terminating glacier dynamics on an intra-annual scale, study ice–ocean interactions in more detail and constrain glacier models. The data are publicly available at PANGAEA at <a href="https://doi.org/10.1594/PANGAEA.963725">https://doi.org/10.1594/PANGAEA.963725</a> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx42">Loebel et al.</a>, <a href="#bib1.bibx42">2024</a><a href="#bib1.bibx42">a</a>)</span>.</p> |
| format | Article |
| id | doaj-art-30a30088140e4a41bba485c78216dbd0 |
| institution | Kabale University |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj-art-30a30088140e4a41bba485c78216dbd02025-01-10T07:28:10ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-01-0117657810.5194/essd-17-65-2025Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imageryE. Loebel0E. Loebel1C. A. Baumhoer2A. Dietz3M. Scheinert4M. Horwath5Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, GermanyAlfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung, Sektion Glaziologie, Bremerhaven, GermanyGerman Aerospace Center, Earth Observation Center, Weßling, GermanyGerman Aerospace Center, Earth Observation Center, Weßling, GermanyInstitut für Planetare Geodäsie, Technische Universität Dresden, Dresden, GermanyInstitut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany<p>Calving front positions of marine-terminating glaciers are an essential parameter for understanding dynamic glacier changes and constraining ice modelling. In particular, for the Antarctic Peninsula, where the current ice mass loss is driven by dynamic glacier changes, accurate and comprehensive data products are of major importance. Current calving front data products are limited in coverage and temporal resolution because they rely on manual delineation, which is time-consuming and unfeasible for the increasing amount of satellite data. To simplify the mapping of calving fronts, we apply a deep-learning-based processing system designed to automatically delineate glacier fronts from multi-spectral Landsat imagery. The U-Net-based framework was initially trained on 869 Greenland glacier front positions. For this study, we extended the training data by 252 front positions of the Antarctic Peninsula. The data product presented here includes 4817 calving front locations of 42 key outlet glaciers from 2013 to 2023 and achieves a sub-seasonal temporal resolution. The mean difference between automated and manual extraction is estimated at <span class="inline-formula">59.3±5.9</span> <span class="inline-formula">m</span>. This dataset will help to better understand marine-terminating glacier dynamics on an intra-annual scale, study ice–ocean interactions in more detail and constrain glacier models. The data are publicly available at PANGAEA at <a href="https://doi.org/10.1594/PANGAEA.963725">https://doi.org/10.1594/PANGAEA.963725</a> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx42">Loebel et al.</a>, <a href="#bib1.bibx42">2024</a><a href="#bib1.bibx42">a</a>)</span>.</p>https://essd.copernicus.org/articles/17/65/2025/essd-17-65-2025.pdf |
| spellingShingle | E. Loebel E. Loebel C. A. Baumhoer A. Dietz M. Scheinert M. Horwath Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery Earth System Science Data |
| title | Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery |
| title_full | Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery |
| title_fullStr | Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery |
| title_full_unstemmed | Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery |
| title_short | Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery |
| title_sort | calving front positions for 42 key glaciers of the antarctic peninsula ice sheet a sub seasonal record from 2013 to 2023 based on deep learning application to landsat multi spectral imagery |
| url | https://essd.copernicus.org/articles/17/65/2025/essd-17-65-2025.pdf |
| work_keys_str_mv | AT eloebel calvingfrontpositionsfor42keyglaciersoftheantarcticpeninsulaicesheetasubseasonalrecordfrom2013to2023basedondeeplearningapplicationtolandsatmultispectralimagery AT eloebel calvingfrontpositionsfor42keyglaciersoftheantarcticpeninsulaicesheetasubseasonalrecordfrom2013to2023basedondeeplearningapplicationtolandsatmultispectralimagery AT cabaumhoer calvingfrontpositionsfor42keyglaciersoftheantarcticpeninsulaicesheetasubseasonalrecordfrom2013to2023basedondeeplearningapplicationtolandsatmultispectralimagery AT adietz calvingfrontpositionsfor42keyglaciersoftheantarcticpeninsulaicesheetasubseasonalrecordfrom2013to2023basedondeeplearningapplicationtolandsatmultispectralimagery AT mscheinert calvingfrontpositionsfor42keyglaciersoftheantarcticpeninsulaicesheetasubseasonalrecordfrom2013to2023basedondeeplearningapplicationtolandsatmultispectralimagery AT mhorwath calvingfrontpositionsfor42keyglaciersoftheantarcticpeninsulaicesheetasubseasonalrecordfrom2013to2023basedondeeplearningapplicationtolandsatmultispectralimagery |