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High-Temporal Antarctic Glacier Terminus and Ice Shelf Front Mapping from Sentinel-1 – A Deep Learning Approach

Baumhoer, Celia and Dietz, Andreas and Kuenzer, Claudia (2019) High-Temporal Antarctic Glacier Terminus and Ice Shelf Front Mapping from Sentinel-1 – A Deep Learning Approach. IUGG 2019, Montreal.

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Abstract

Antarctic glacier termini and ice shelf fronts are sensitive indicators of glaciological and environmental change. Mapping Antarctic calving front change in a high-temporal and spatial resolution has been difficult due to the lack of suitable data and the time-consuming manual delineation of fronts. Since the launch of Sentinel-1 year-round SAR imagery over the Antarctic coastline exists with at least weekly revisit times. To exploit the abundance of data it is necessary to implement an automated extraction algorithm for glacier and ice shelf fronts. Novel improvements in deep learning offer great opportunities for scene classification in remote sensing data even when facing complex structures. Our developed approach uses a modified U-Net for semantic segmentation classifying Sentinel-1 scenes for glacier ice and ocean. Accurate front positions can be obtained also for glacier termini enclosed by icebergs and mélange. Nevertheless, surface melt can be challenging in some regions. To demonstrate the model’s performance, we present high-temporal time-series of calving front positions for fast moving glaciers (e.g. David Glacier). The frequent mapping of glacier termini reveals changes in front fluctuations in unprecedented detail and could be used as input data for ice dynamic modelling.

Item URL in elib:https://elib.dlr.de/128848/
Document Type:Conference or Workshop Item (Poster)
Title:High-Temporal Antarctic Glacier Terminus and Ice Shelf Front Mapping from Sentinel-1 – A Deep Learning Approach
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Baumhoer, CeliaCelia.Baumhoer (at) dlr.deUNSPECIFIED
Dietz, AndreasAndreas.Dietz (at) dlr.deUNSPECIFIED
Kuenzer, Claudiaclaudia.kuenzer (at) dlr.deUNSPECIFIED
Date:9 July 2019
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:calving front, deep learning, time-series, Antarctica, coastline, glacier front, ice shelf
Event Title:IUGG 2019
Event Location:Montreal
Event Type:international Conference
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 - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Baumhoer, Celia
Deposited On:03 Sep 2019 15:16
Last Modified:03 Sep 2019 15:16

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