Presentation Open Access

LoVe Observatory

Nilssen, Ingunn; Hermansen, Anders

ATLAS work package 2 presentation at ATLAS 3rd General Assembly

The Lofoten-Vesterålen Ocean Observatory (LoVe) is located in a hotspot area for cold-water corals and is also hosting the most important spawning area for North-Atlantic cod (Gadus morhua). The heterogenic topography creates complex current and eddy systems, basis for the rich ecosystem present in the area. Our intention is to gain knowledge about the natural dynamics through technology testing and development, and with development of methodology retracting the inherent knowledge available in the provided data of high temporal resolution and coverage.
The observatory was cabled from shore in 2013. After more than one year of downtime, a considerably upgraded system was redeployed in November 2017. To find optimal locations for the cameras, facing a Lophelia pertusa reef and bubblegum corals (Paragorgia arborea), respectively, the location of the observatory has been slightly changed (within a range of 100 meters).
Presently, LoVe consist of three sensor platforms: 1) the X-frame measuring currents and biomasses present in the water column, 2) Sat1 equipped with a HD camera and sensors measuring a variety of physical/chemical parameters and 3) equipped with a stereo camera. Data is public available and can be downloaded from www.love.statoil.com.
In addition to hardware development, Statoil has till present mainly been working with images and supported development of algorithms to detect various organisms and behaviour (Möller et al., 2016; Möller et al., 2017; Osterloff et al., 2016a; Osterloff et al., 2016b; Osterloff et al., 2016c). Lately we have also investigated biological behaviour and change with physical/chemical measurements from the observatory (In prep.). Establishment of these methodologies, enables us to take the next step; use of the new knowledge in environmental monitoring. When the most important natural contributors to specimen behaviour and/or biochemical change are identified, the aim is to develop statistical models to predict expected biological responses.

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