ArticleScientific reports2019
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- New interactive machine learning tool for marine image analysis.Royal Society open science · 2024Article
- A data science approach for multi-sensor marine observatory data monitoring cold water corals (Paragorgia arborea) in two campaigns.PloS one · 2023Article
- Exploring time series of hyperspectral images for cold water coral stress response analysis.PloS one · 2022Article
- Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic-The Glider Project.Sensors (Basel, Switzerland) · 2021Article
- An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms ofSensors (Basel, Switzerland) · 2020Article
- A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring.Sensors (Basel, Switzerland) · 2020Article
- Broad Thermal Tolerance in the Cold-Water CoralFrontiers in physiology · 2019Article
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Authors and funding
6 authors.
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Abstract
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.
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