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.
Peer reviewed, Journal article
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Date
2019Metadata
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- Scientific publications [1437]
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10.1038/s41598-019-41275-1Abstract
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.