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Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations

Baumhoer, Celia and Dietz, Andreas and Dirscherl, Mariel and Künzer, Claudia (2020) Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations. EGU 2020, 4.-8. Mai 2020, Wien.

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Abstract

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.

Item URL in elib:https://elib.dlr.de/135505/
Document Type:Conference or Workshop Item (Poster)
Title:Deep learning reveals seasonal patterns of Antarctic ice shelf front fluctuations
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Baumhoer, CeliaCelia.Baumhoer (at) dlr.deUNSPECIFIED
Dietz, AndreasAndreas.Dietz (at) dlr.deUNSPECIFIED
Dirscherl, MarielMariel.Dirscherl (at) dlr.deUNSPECIFIED
Künzer, ClaudiaClaudia.Kuenzer (at) dlr.deUNSPECIFIED
Date:2020
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:p. 1
Status:Published
Keywords:calving front, Antarctica, glacier, deep learning, U-Net
Event Title:EGU 2020
Event Location:Wien
Event Type:international Conference
Event Dates:4.-8. Mai 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Baumhoer, Celia
Deposited On:21 Jul 2020 11:20
Last Modified:21 Jul 2020 11:20

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