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...

Full description

Bibliographic Details
Main Authors: Baumhoer, Celia, Dietz, Andreas, Dirscherl, Mariel, Künzer, Claudia
Format: Conference Object
Language:unknown
Published: 2020
Subjects:
Online Access:https://elib.dlr.de/135505/
id ftdlr:oai:elib.dlr.de:135505
record_format openpolar
spelling 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
institution Open Polar
collection German Aerospace Center: elib - DLR electronic library
op_collection_id ftdlr
language unknown
topic Dynamik der Landoberfläche
spellingShingle 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.
_version_ 1799476022331047936