Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory

Osterloff J, Nilssen I, Jarnegren J, van Engeland T, Buhl-Mortensen P, Nattkemper TW (2019)
Scientific Reports.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Autor/in
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Abstract / Bemerkung
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 day time 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.
Stichworte
Machine learning; Deep learning; Data science; Computer Vision; Environmental Sciences; Marine Biology; Corals; Lophelia Pertusa
Erscheinungsjahr
2019
Zeitschriftentitel
Scientific Reports
eISSN
2045-2322
Page URI
https://pub.uni-bielefeld.de/record/2934146

Zitieren

Osterloff J, Nilssen I, Jarnegren J, van Engeland T, Buhl-Mortensen P, Nattkemper TW. Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory. Scientific Reports. 2019.
Osterloff, J., Nilssen, I., Jarnegren, J., van Engeland, T., Buhl-Mortensen, P., & Nattkemper, T. W. (2019). Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory. Scientific Reports
Osterloff, J., Nilssen, I., Jarnegren, J., van Engeland, T., Buhl-Mortensen, P., and Nattkemper, T. W. (2019). Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory. Scientific Reports.
Osterloff, J., et al., 2019. Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory. Scientific Reports.
J. Osterloff, et al., “Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory”, Scientific Reports, 2019.
Osterloff, J., Nilssen, I., Jarnegren, J., van Engeland, T., Buhl-Mortensen, P., Nattkemper, T.W.: Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory. Scientific Reports. (2019).
Osterloff, Jonas, Nilssen, Ingunn, Jarnegren, Johanna, van Engeland, Tom, Buhl-Mortensen, Pål, and Nattkemper, Tim Wilhelm. “Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory”. Scientific Reports (2019).
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