Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study
Drifting icebergs represent a significant hazard for polar navigation and are able to impact the ocean environment around them. Freshwater flux and the associated cooling from melting icebergs can locally decrease salinity and temperature and thus affect ocean circulation, biological activity, sea i...
Published in: | ISPRS Journal of Photogrammetry and Remote Sensing |
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Online Access: | https://epic.awi.de/id/eprint/53589/ https://doi.org/10.1016/j.isprsjprs.2020.12.006 https://hdl.handle.net/10013/epic.cd2231c1-8cb9-46f7-8759-b926bdeaaf0a |
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ftawi:oai:epic.awi.de:53589 2024-09-15T17:42:04+00:00 Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study Barbat, Mauro M. Rackow, Thomas Wesche, Christine Hellmer, Hartmut H. Mata, Mauricio M. 2021-02 https://epic.awi.de/id/eprint/53589/ https://doi.org/10.1016/j.isprsjprs.2020.12.006 https://hdl.handle.net/10013/epic.cd2231c1-8cb9-46f7-8759-b926bdeaaf0a unknown Barbat, M. M. , Rackow, T. orcid:0000-0002-5468-575X , Wesche, C. orcid:0000-0002-9786-4010 , Hellmer, H. H. orcid:0000-0002-9357-9853 and Mata, M. M. (2021) Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study , ISPRS Journal of Photogrammetry and Remote Sensing, 172 , pp. 189-206 . doi:10.1016/j.isprsjprs.2020.12.006 <https://doi.org/10.1016/j.isprsjprs.2020.12.006> , hdl:10013/epic.cd2231c1-8cb9-46f7-8759-b926bdeaaf0a EPIC3ISPRS Journal of Photogrammetry and Remote Sensing, 172, pp. 189-206, ISSN: 09242716 Article isiRev 2021 ftawi https://doi.org/10.1016/j.isprsjprs.2020.12.006 2024-06-24T04:26:11Z Drifting icebergs represent a significant hazard for polar navigation and are able to impact the ocean environment around them. Freshwater flux and the associated cooling from melting icebergs can locally decrease salinity and temperature and thus affect ocean circulation, biological activity, sea ice, and –on larger spatial scales– the whole climate system. However, despite their potential impact, the large-scale operational monitoring of drifting icebergs in sea ice-covered regions is as of today typically restricted to giant icebergs, larger than 18.5 km in length. This is due to difficulties in accurately identifying and following the motion of much smaller features in the polar ocean from space. So far, tracking of smaller icebergs from satellite imagery thus has been limited to open-ocean regions not covered by sea ice. In this study, a novel automated iceberg tracking method, based on a machine learning-approach for automatic iceberg detection, is presented. To demonstrate the applicability of the method, a case study was performed for the Weddell Sea region, Antarctica, using 1213 Advanced Synthetic Aperture Radar (ASAR) satellite images acquired between 2002 and 2011. Overall, a subset of 414 icebergs (3134 re-detections in total) with surface areas between 3.4 km² and 3612 km² were investigated with respect to their prevalent drift patterns, size variability, and average disintegration. The majority of the tracked icebergs drifted between 1.3 km and 2679.2 km westward around the Antarctic continent, following the Antarctic Coastal Current (ACoC) and the Weddell Gyre, at an average drift speed of 3.6 ± 7.4 km day⁻¹. The method also allowed us to estimate an average daily disintegration (i.e. iceberg area decrease) rate of ~0.13% (~37% year⁻¹) for all icebergs. Using the sum of all detected individual surface area reductions, we estimate a total iceberg mass decrease of ~683 Gt year⁻¹, which can be freshwater input and/or new ‘child’ icebergs calved from larger icebergs. The extension to an automated ... Article in Journal/Newspaper Antarc* Antarctic Antarctica Iceberg* Sea ice Weddell Sea Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) ISPRS Journal of Photogrammetry and Remote Sensing 172 189 206 |
institution |
Open Polar |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
op_collection_id |
ftawi |
language |
unknown |
description |
Drifting icebergs represent a significant hazard for polar navigation and are able to impact the ocean environment around them. Freshwater flux and the associated cooling from melting icebergs can locally decrease salinity and temperature and thus affect ocean circulation, biological activity, sea ice, and –on larger spatial scales– the whole climate system. However, despite their potential impact, the large-scale operational monitoring of drifting icebergs in sea ice-covered regions is as of today typically restricted to giant icebergs, larger than 18.5 km in length. This is due to difficulties in accurately identifying and following the motion of much smaller features in the polar ocean from space. So far, tracking of smaller icebergs from satellite imagery thus has been limited to open-ocean regions not covered by sea ice. In this study, a novel automated iceberg tracking method, based on a machine learning-approach for automatic iceberg detection, is presented. To demonstrate the applicability of the method, a case study was performed for the Weddell Sea region, Antarctica, using 1213 Advanced Synthetic Aperture Radar (ASAR) satellite images acquired between 2002 and 2011. Overall, a subset of 414 icebergs (3134 re-detections in total) with surface areas between 3.4 km² and 3612 km² were investigated with respect to their prevalent drift patterns, size variability, and average disintegration. The majority of the tracked icebergs drifted between 1.3 km and 2679.2 km westward around the Antarctic continent, following the Antarctic Coastal Current (ACoC) and the Weddell Gyre, at an average drift speed of 3.6 ± 7.4 km day⁻¹. The method also allowed us to estimate an average daily disintegration (i.e. iceberg area decrease) rate of ~0.13% (~37% year⁻¹) for all icebergs. Using the sum of all detected individual surface area reductions, we estimate a total iceberg mass decrease of ~683 Gt year⁻¹, which can be freshwater input and/or new ‘child’ icebergs calved from larger icebergs. The extension to an automated ... |
format |
Article in Journal/Newspaper |
author |
Barbat, Mauro M. Rackow, Thomas Wesche, Christine Hellmer, Hartmut H. Mata, Mauricio M. |
spellingShingle |
Barbat, Mauro M. Rackow, Thomas Wesche, Christine Hellmer, Hartmut H. Mata, Mauricio M. Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study |
author_facet |
Barbat, Mauro M. Rackow, Thomas Wesche, Christine Hellmer, Hartmut H. Mata, Mauricio M. |
author_sort |
Barbat, Mauro M. |
title |
Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study |
title_short |
Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study |
title_full |
Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study |
title_fullStr |
Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study |
title_full_unstemmed |
Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study |
title_sort |
automated iceberg tracking with a machine learning approach applied to sar imagery: a weddell sea case study |
publishDate |
2021 |
url |
https://epic.awi.de/id/eprint/53589/ https://doi.org/10.1016/j.isprsjprs.2020.12.006 https://hdl.handle.net/10013/epic.cd2231c1-8cb9-46f7-8759-b926bdeaaf0a |
genre |
Antarc* Antarctic Antarctica Iceberg* Sea ice Weddell Sea |
genre_facet |
Antarc* Antarctic Antarctica Iceberg* Sea ice Weddell Sea |
op_source |
EPIC3ISPRS Journal of Photogrammetry and Remote Sensing, 172, pp. 189-206, ISSN: 09242716 |
op_relation |
Barbat, M. M. , Rackow, T. orcid:0000-0002-5468-575X , Wesche, C. orcid:0000-0002-9786-4010 , Hellmer, H. H. orcid:0000-0002-9357-9853 and Mata, M. M. (2021) Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study , ISPRS Journal of Photogrammetry and Remote Sensing, 172 , pp. 189-206 . doi:10.1016/j.isprsjprs.2020.12.006 <https://doi.org/10.1016/j.isprsjprs.2020.12.006> , hdl:10013/epic.cd2231c1-8cb9-46f7-8759-b926bdeaaf0a |
op_doi |
https://doi.org/10.1016/j.isprsjprs.2020.12.006 |
container_title |
ISPRS Journal of Photogrammetry and Remote Sensing |
container_volume |
172 |
container_start_page |
189 |
op_container_end_page |
206 |
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1810488445832790016 |