Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard
Calving is an important process in glacier systems terminating in the ocean, and more observations are needed to improve our understanding of the undergoing processes and parameterize calving in larger-scale models. Time-lapse cameras are good tools for monitoring calving fronts of glaciers and they...
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00002810 2023-05-15T16:22:17+02:00 Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard Vallot, Dorothée Adinugroho, Sigit Strand, Robin How, Penelope Pettersson, Rickard Benn, Douglas I. Hulton, Nicholas R. J. 2019-03 electronic https://doi.org/10.5194/gi-8-113-2019 https://noa.gwlb.de/receive/cop_mods_00002810 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00002768/gi-8-113-2019.pdf https://gi.copernicus.org/articles/8/113/2019/gi-8-113-2019.pdf eng eng Copernicus Publications Geoscientific Instrumentation, Methods and Data Systems -- Geosci. Instrum. Meth. Data Syst. -- http://www.geoscientific-instrumentation-methods-and-data-systems.net/home.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2690575 -- 2193-0864 https://doi.org/10.5194/gi-8-113-2019 https://noa.gwlb.de/receive/cop_mods_00002810 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00002768/gi-8-113-2019.pdf https://gi.copernicus.org/articles/8/113/2019/gi-8-113-2019.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2019 ftnonlinearchiv https://doi.org/10.5194/gi-8-113-2019 2022-02-08T23:00:53Z Calving is an important process in glacier systems terminating in the ocean, and more observations are needed to improve our understanding of the undergoing processes and parameterize calving in larger-scale models. Time-lapse cameras are good tools for monitoring calving fronts of glaciers and they have been used widely where conditions are favourable. However, automatic image analysis to detect and calculate the size of calving events has not been developed so far. Here, we present a method that fills this gap using image analysis tools. First, the calving front is segmented. Second, changes between two images are detected and a mask is produced to delimit the calving event. Third, we calculate the area given the front and camera positions as well as camera characteristics. To illustrate our method, we analyse two image time series from two cameras placed at different locations in 2014 and 2015 and compare the automatic detection results to a manual detection. We find a good match when the weather is favourable, but the method fails with dense fog or high illumination conditions. Furthermore, results show that calving events are more likely to occur (i) close to where subglacial meltwater plumes have been observed to rise at the front and (ii) close to one another. Article in Journal/Newspaper glacier Svalbard Niedersächsisches Online-Archiv NOA Svalbard Tunabreen ENVELOPE(17.387,17.387,78.461,78.461) Geoscientific Instrumentation, Methods and Data Systems 8 1 113 127 |
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Open Polar |
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Niedersächsisches Online-Archiv NOA |
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ftnonlinearchiv |
language |
English |
topic |
article Verlagsveröffentlichung |
spellingShingle |
article Verlagsveröffentlichung Vallot, Dorothée Adinugroho, Sigit Strand, Robin How, Penelope Pettersson, Rickard Benn, Douglas I. Hulton, Nicholas R. J. Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard |
topic_facet |
article Verlagsveröffentlichung |
description |
Calving is an important process in glacier systems terminating in the ocean, and more observations are needed to improve our understanding of the undergoing processes and parameterize calving in larger-scale models. Time-lapse cameras are good tools for monitoring calving fronts of glaciers and they have been used widely where conditions are favourable. However, automatic image analysis to detect and calculate the size of calving events has not been developed so far. Here, we present a method that fills this gap using image analysis tools. First, the calving front is segmented. Second, changes between two images are detected and a mask is produced to delimit the calving event. Third, we calculate the area given the front and camera positions as well as camera characteristics. To illustrate our method, we analyse two image time series from two cameras placed at different locations in 2014 and 2015 and compare the automatic detection results to a manual detection. We find a good match when the weather is favourable, but the method fails with dense fog or high illumination conditions. Furthermore, results show that calving events are more likely to occur (i) close to where subglacial meltwater plumes have been observed to rise at the front and (ii) close to one another. |
format |
Article in Journal/Newspaper |
author |
Vallot, Dorothée Adinugroho, Sigit Strand, Robin How, Penelope Pettersson, Rickard Benn, Douglas I. Hulton, Nicholas R. J. |
author_facet |
Vallot, Dorothée Adinugroho, Sigit Strand, Robin How, Penelope Pettersson, Rickard Benn, Douglas I. Hulton, Nicholas R. J. |
author_sort |
Vallot, Dorothée |
title |
Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard |
title_short |
Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard |
title_full |
Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard |
title_fullStr |
Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard |
title_full_unstemmed |
Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard |
title_sort |
automatic detection of calving events from time-lapse imagery at tunabreen, svalbard |
publisher |
Copernicus Publications |
publishDate |
2019 |
url |
https://doi.org/10.5194/gi-8-113-2019 https://noa.gwlb.de/receive/cop_mods_00002810 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00002768/gi-8-113-2019.pdf https://gi.copernicus.org/articles/8/113/2019/gi-8-113-2019.pdf |
long_lat |
ENVELOPE(17.387,17.387,78.461,78.461) |
geographic |
Svalbard Tunabreen |
geographic_facet |
Svalbard Tunabreen |
genre |
glacier Svalbard |
genre_facet |
glacier Svalbard |
op_relation |
Geoscientific Instrumentation, Methods and Data Systems -- Geosci. Instrum. Meth. Data Syst. -- http://www.geoscientific-instrumentation-methods-and-data-systems.net/home.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2690575 -- 2193-0864 https://doi.org/10.5194/gi-8-113-2019 https://noa.gwlb.de/receive/cop_mods_00002810 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00002768/gi-8-113-2019.pdf https://gi.copernicus.org/articles/8/113/2019/gi-8-113-2019.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/gi-8-113-2019 |
container_title |
Geoscientific Instrumentation, Methods and Data Systems |
container_volume |
8 |
container_issue |
1 |
container_start_page |
113 |
op_container_end_page |
127 |
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1766010244820893696 |