Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory
An array of sensors, including an HD camera mounted on a Fixed Underwater Observatory (FUO) were used to monitor a cold-water coral (Lophelia pertusa) reef in the Lofoten-Vesterålen area from April to November 2015. Image processing and deep learning enabled extraction of time series describing chan...
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ftninstnf:oai:brage.nina.no:11250/2596589 2023-05-15T17:08:15+02:00 Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory Osterloff, Jonas Nilssen, Ingunn Järnegren, Johanna Van Engeland, Tom Buhl-Mortensen, Pål Nattkemper, Tim W. Noreg, Norge, Norway, Nordland, Vesterålen, Lofoten 2019 application/pdf http://hdl.handle.net/11250/2596589 https://doi.org/10.1038/s41598-019-41275-1 eng eng urn:issn:2045-2322 http://hdl.handle.net/11250/2596589 https://doi.org/10.1038/s41598-019-41275-1 cristin:1695358 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © The Author(s) 2019 CC-BY 12 9 Scientific Reports VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Marinbiologi: 497 Peer reviewed 2019 ftninstnf https://doi.org/10.1038/s41598-019-41275-1 2021-12-23T07:17:02Z An array of sensors, including an HD camera mounted on a Fixed Underwater Observatory (FUO) were used to monitor a cold-water coral (Lophelia pertusa) reef in the Lofoten-Vesterålen area from April to November 2015. Image processing and deep learning enabled extraction of time series describing changes in coral colour and polyp activity (feeding). The image data was analysed together with data from the other sensors from the same period, to provide new insights into the short- and long-term dynamics in polyp features. The results indicate that diurnal variations and tidal current influenced polyp activity, by controlling the food supply. On a longer time-scale, the coral’s tissue colour changed from white in the spring to slightly red during the summer months, which can be explained by a seasonal change in food supply. Our work shows, that using an effective integrative computational approach, the image time series is a new and rich source of information to understand and monitor the dynamics in underwater environments due to the high temporal resolution and coverage enabled with FUOs. Text Lofoten Lophelia pertusa Nordland Nordland Vesterålen Nordland Norwegian Institute for Nature Research: Brage NINA Lofoten Norway Vesterålen ENVELOPE(14.939,14.939,68.754,68.754) Scientific Reports 9 1 |
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Norwegian Institute for Nature Research: Brage NINA |
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language |
English |
topic |
VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Marinbiologi: 497 |
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VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Marinbiologi: 497 Osterloff, Jonas Nilssen, Ingunn Järnegren, Johanna Van Engeland, Tom Buhl-Mortensen, Pål Nattkemper, Tim W. Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory |
topic_facet |
VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Marinbiologi: 497 |
description |
An array of sensors, including an HD camera mounted on a Fixed Underwater Observatory (FUO) were used to monitor a cold-water coral (Lophelia pertusa) reef in the Lofoten-Vesterålen area from April to November 2015. Image processing and deep learning enabled extraction of time series describing changes in coral colour and polyp activity (feeding). The image data was analysed together with data from the other sensors from the same period, to provide new insights into the short- and long-term dynamics in polyp features. The results indicate that diurnal variations and tidal current influenced polyp activity, by controlling the food supply. On a longer time-scale, the coral’s tissue colour changed from white in the spring to slightly red during the summer months, which can be explained by a seasonal change in food supply. Our work shows, that using an effective integrative computational approach, the image time series is a new and rich source of information to understand and monitor the dynamics in underwater environments due to the high temporal resolution and coverage enabled with FUOs. |
format |
Text |
author |
Osterloff, Jonas Nilssen, Ingunn Järnegren, Johanna Van Engeland, Tom Buhl-Mortensen, Pål Nattkemper, Tim W. |
author_facet |
Osterloff, Jonas Nilssen, Ingunn Järnegren, Johanna Van Engeland, Tom Buhl-Mortensen, Pål Nattkemper, Tim W. |
author_sort |
Osterloff, Jonas |
title |
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory |
title_short |
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory |
title_full |
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory |
title_fullStr |
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory |
title_full_unstemmed |
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory |
title_sort |
computer vision enables short- and long-term analysis of lophelia pertusa polyp behaviour and colour from an underwater observatory |
publishDate |
2019 |
url |
http://hdl.handle.net/11250/2596589 https://doi.org/10.1038/s41598-019-41275-1 |
op_coverage |
Noreg, Norge, Norway, Nordland, Vesterålen, Lofoten |
long_lat |
ENVELOPE(14.939,14.939,68.754,68.754) |
geographic |
Lofoten Norway Vesterålen |
geographic_facet |
Lofoten Norway Vesterålen |
genre |
Lofoten Lophelia pertusa Nordland Nordland Vesterålen Nordland |
genre_facet |
Lofoten Lophelia pertusa Nordland Nordland Vesterålen Nordland |
op_source |
12 9 Scientific Reports |
op_relation |
urn:issn:2045-2322 http://hdl.handle.net/11250/2596589 https://doi.org/10.1038/s41598-019-41275-1 cristin:1695358 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © The Author(s) 2019 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-019-41275-1 |
container_title |
Scientific Reports |
container_volume |
9 |
container_issue |
1 |
_version_ |
1766063965041852416 |