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...

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Published in:ISPRS Journal of Photogrammetry and Remote Sensing
Main Authors: Barbat, Mauro M., Rackow, Thomas, Wesche, Christine, Hellmer, Hartmut H., Mata, Mauricio M.
Format: Article in Journal/Newspaper
Language:unknown
Published: 2021
Subjects:
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
id ftawi:oai:epic.awi.de:53589
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spelling 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
collection 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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