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Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour[...]
Material and Field Applications
The study area.
Equipment and sensors.
Figure 2 An image recorded at the LoVe observatory the 4th of April 2015 05:59.
Table 1 The temporal resolution of the different sensors ft used in the multivariate data analysis.
Methods
Coral colour time series computation.
Figure 3 One image (see (A)) per hour is uploaded from the LoVe observatory (see schematic graph (B), image drawn by author TWN) to the web portal.
Polyp activity time series computation.
Multivariate data analysis.
Figure 4 Measurements of selected sensors are plotted together with estimated polyp activity γt and coral colour ξt (a-coordinate of the coral colour ).
Results
Figure 5 The daily averages for temperature () are plotted against daily averages for the polyp activity ().
Figure 6 The wavelet power spectrum (see left panels in the subplots) of the polyp activity () (see a)) northward bottom current velocity () (see b)) and the corresponding scale-averaged global power spectra (right panels in the subplots).
Discussion
Long term dynamics.
Figure 7 Left panel: Squared wavelet coherence (colour) and phase (arrows) between polyp activity and the northward component of the bottom current velocity .
Short term patterns.
Conclusions and future perspectives.
Acknowledgements
Journal Article
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory.
Place and Date of Creation
2019
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