Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning
Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. Th...
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Online Access: | https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/19315 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-193150 https://doi.org/10.3390/rs11212529 https://opus.bibliothek.uni-wuerzburg.de/files/19315/remotesensing-11-02529.pdf |
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ftunivwuerz:oai:opus.bibliothek.uni-wuerzburg.de:19315 2023-09-05T13:13:45+02:00 Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning Baumhoer, Celia A. Dietz, Andreas J. Kneisel, C. Kuenzer, C. 2019 application/pdf https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/19315 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-193150 https://doi.org/10.3390/rs11212529 https://opus.bibliothek.uni-wuerzburg.de/files/19315/remotesensing-11-02529.pdf eng eng https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/19315 urn:nbn:de:bvb:20-opus-193150 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-193150 https://doi.org/10.3390/rs11212529 https://opus.bibliothek.uni-wuerzburg.de/files/19315/remotesensing-11-02529.pdf https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess ddc:526 article doc-type:article 2019 ftunivwuerz https://doi.org/10.3390/rs11212529 2023-08-13T22:34:10Z Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr. Article in Journal/Newspaper Antarc* Antarctic DeVicq Glacier Getz Ice Shelf Ice Sheet Ice Shelf Sea ice Würzburg University: Online Publication Service Antarctic DeVicq Glacier ENVELOPE(-131.000,-131.000,-75.000,-75.000) Getz ENVELOPE(-145.217,-145.217,-76.550,-76.550) Getz Ice Shelf ENVELOPE(-126.500,-126.500,-74.250,-74.250) The Antarctic Remote Sensing 11 21 2529 |
institution |
Open Polar |
collection |
Würzburg University: Online Publication Service |
op_collection_id |
ftunivwuerz |
language |
English |
topic |
ddc:526 |
spellingShingle |
ddc:526 Baumhoer, Celia A. Dietz, Andreas J. Kneisel, C. Kuenzer, C. Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning |
topic_facet |
ddc:526 |
description |
Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr. |
format |
Article in Journal/Newspaper |
author |
Baumhoer, Celia A. Dietz, Andreas J. Kneisel, C. Kuenzer, C. |
author_facet |
Baumhoer, Celia A. Dietz, Andreas J. Kneisel, C. Kuenzer, C. |
author_sort |
Baumhoer, Celia A. |
title |
Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning |
title_short |
Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning |
title_full |
Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning |
title_fullStr |
Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning |
title_full_unstemmed |
Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning |
title_sort |
automated extraction of antarctic glacier and ice shelf fronts from sentinel-1 imagery using deep learning |
publishDate |
2019 |
url |
https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/19315 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-193150 https://doi.org/10.3390/rs11212529 https://opus.bibliothek.uni-wuerzburg.de/files/19315/remotesensing-11-02529.pdf |
long_lat |
ENVELOPE(-131.000,-131.000,-75.000,-75.000) ENVELOPE(-145.217,-145.217,-76.550,-76.550) ENVELOPE(-126.500,-126.500,-74.250,-74.250) |
geographic |
Antarctic DeVicq Glacier Getz Getz Ice Shelf The Antarctic |
geographic_facet |
Antarctic DeVicq Glacier Getz Getz Ice Shelf The Antarctic |
genre |
Antarc* Antarctic DeVicq Glacier Getz Ice Shelf Ice Sheet Ice Shelf Sea ice |
genre_facet |
Antarc* Antarctic DeVicq Glacier Getz Ice Shelf Ice Sheet Ice Shelf Sea ice |
op_relation |
https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/19315 urn:nbn:de:bvb:20-opus-193150 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-193150 https://doi.org/10.3390/rs11212529 https://opus.bibliothek.uni-wuerzburg.de/files/19315/remotesensing-11-02529.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs11212529 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
21 |
container_start_page |
2529 |
_version_ |
1776204920331960320 |