Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations
The Antarctic ice sheet drains ice through its peripheral ice shelves and glaciers making them an important factor for ice sheet mass balance. The extent of ice shelves and their calving front position influences ice sheet discharge and can yield valuable information on ice dynamics. Moreover, glaci...
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ftdlr:oai:elib.dlr.de:135505 2024-05-19T07:28:49+00:00 Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations Baumhoer, Celia Dietz, Andreas Dirscherl, Mariel Künzer, Claudia 2020 https://elib.dlr.de/135505/ unknown Baumhoer, Celia und Dietz, Andreas und Dirscherl, Mariel und Künzer, Claudia (2020) Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations. EGU 2020, 2020-05-04 - 2020-05-08, Wien. Dynamik der Landoberfläche Konferenzbeitrag NonPeerReviewed 2020 ftdlr 2024-04-25T00:53:45Z The Antarctic ice sheet drains ice through its peripheral ice shelves and glaciers making them an important factor for ice sheet mass balance. The extent of ice shelves and their calving front position influences ice sheet discharge and can yield valuable information on ice dynamics. Moreover, glacier fronts can have strong seasonal variations of retreat and advance. Yet, little is known about the seasonal pattern of Antarctic calving front fluctuations and their effect on ice sheet dynamics. The current developments in remote sensing and big data processing allow accurate monitoring of the Antarctic coastline. But to derive monthly calving front positions, the traditional approach of manual delineation is too time-consuming to cope with the temporal and spatial abundance of contemporary satellite missions. To create an up to date monitoring of changes in the Antarctic coastline a fully-automated approach is necessary. Automation of ice front delineation is a very challenging task as conventional edge detection methods fail due to the very low contrast between shelf ice and sea ice. Therefore, to exploit the abundance of available Sentinel-1 imagery over Antarctica, we created an automated workflow to extract monthly ice shelf front positions from Sentinel-1 imagery. The core of our processing chain is the deep learning architecture U-Net trained with about 44.000 image tiles covering parts of the Antarctic coastline during various seasons. Post-processing allows generating shapefiles of front positions and creating time series of seasonal ice shelf front fluctuations. We demonstrate our proposed method on selected ice shelves along the West and East Antarctic coastline and present intra-annual changes of calving front positions. This allows us to investigate seasonal change patterns of Antarctic ice shelves between 2014 and 2019 (depending on Sentinel-1 data availability) and to obtain a better picture on current Antarctic ice shelf front dynamics. Conference Object Antarc* Antarctic Antarctica Ice Sheet Ice Shelf Ice Shelves Sea ice German Aerospace Center: elib - DLR electronic library |
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German Aerospace Center: elib - DLR electronic library |
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Dynamik der Landoberfläche |
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Dynamik der Landoberfläche Baumhoer, Celia Dietz, Andreas Dirscherl, Mariel Künzer, Claudia Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations |
topic_facet |
Dynamik der Landoberfläche |
description |
The Antarctic ice sheet drains ice through its peripheral ice shelves and glaciers making them an important factor for ice sheet mass balance. The extent of ice shelves and their calving front position influences ice sheet discharge and can yield valuable information on ice dynamics. Moreover, glacier fronts can have strong seasonal variations of retreat and advance. Yet, little is known about the seasonal pattern of Antarctic calving front fluctuations and their effect on ice sheet dynamics. The current developments in remote sensing and big data processing allow accurate monitoring of the Antarctic coastline. But to derive monthly calving front positions, the traditional approach of manual delineation is too time-consuming to cope with the temporal and spatial abundance of contemporary satellite missions. To create an up to date monitoring of changes in the Antarctic coastline a fully-automated approach is necessary. Automation of ice front delineation is a very challenging task as conventional edge detection methods fail due to the very low contrast between shelf ice and sea ice. Therefore, to exploit the abundance of available Sentinel-1 imagery over Antarctica, we created an automated workflow to extract monthly ice shelf front positions from Sentinel-1 imagery. The core of our processing chain is the deep learning architecture U-Net trained with about 44.000 image tiles covering parts of the Antarctic coastline during various seasons. Post-processing allows generating shapefiles of front positions and creating time series of seasonal ice shelf front fluctuations. We demonstrate our proposed method on selected ice shelves along the West and East Antarctic coastline and present intra-annual changes of calving front positions. This allows us to investigate seasonal change patterns of Antarctic ice shelves between 2014 and 2019 (depending on Sentinel-1 data availability) and to obtain a better picture on current Antarctic ice shelf front dynamics. |
format |
Conference Object |
author |
Baumhoer, Celia Dietz, Andreas Dirscherl, Mariel Künzer, Claudia |
author_facet |
Baumhoer, Celia Dietz, Andreas Dirscherl, Mariel Künzer, Claudia |
author_sort |
Baumhoer, Celia |
title |
Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations |
title_short |
Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations |
title_full |
Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations |
title_fullStr |
Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations |
title_full_unstemmed |
Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations |
title_sort |
deep learning reveals seasonal patterns of antarctic ice shelf front fluctuations |
publishDate |
2020 |
url |
https://elib.dlr.de/135505/ |
genre |
Antarc* Antarctic Antarctica Ice Sheet Ice Shelf Ice Shelves Sea ice |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet Ice Shelf Ice Shelves Sea ice |
op_relation |
Baumhoer, Celia und Dietz, Andreas und Dirscherl, Mariel und Künzer, Claudia (2020) Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations. EGU 2020, 2020-05-04 - 2020-05-08, Wien. |
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1799476022331047936 |