Tag Archives: Arctic Ocean

Science at Camp

Shining their own light

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Whether in solid or liquid or gaseous form, matter can convert radiative energy by the absorption process or it can transmit it with the possibility of a change in direction through the scattering process. When added up, these optical properties can tell you a lot about what a medium is made of.  Continue reading Shining their own light →

Science at Camp

Rapid, dramatic changes of surface sea ice

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Credit: Gauthier Verin

During the Arctic winter, a snow-pack forms over the sea ice. Typically 20-30 cm deep, it is thin relative to the thickness of the ice below. However, this snow layer plays a fundamental role in different domains. Snow is composed of small ice crystals surrounded by air. This very simple structure leads to two important physical properties: Continue reading Rapid, dramatic changes of surface sea ice →

Science at sea

Instrumental zoom: the LOKI system

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As part of the Green Edge 2016 campaign, LOKI – The Lightframe On-sight Keyspecies Investigation system – is being deployed from the CCGS Amundsen with the goal to study the coupling between phytoplankton and zooplankton.

LOKI is an underwater camera system for continuous, in-situ imaging of zooplankton (Fig. 1). During a LOKI deployment thousands of images of plankton ranging from 200 µm to ~3 cm are collected as well as environmental data from the onboard sensors (e.g. pressure, temperature, salinity, chl a). LOKI data has a vertical resolution of ~50 cm, therefore providing a much more detailed picture than can be drawn from traditional zooplankton net samples.

Figure 1. a) Schematic of LOKI showing its main components 1-4. b) The LOKI system on the right attached to a frame besides a traditional zooplankton net sampler, during a recent deployment in the Canadian Arctic. c) The LOKI camera, showing how plankton pass through the channel for imaging. a and c are adapted from Isitec GmbH. Photo credit for b): Jessy Barrette. Figure taken from Schmid et al. (2016).

Images collected by LOKI are high enough resolution to identify development stages of copepods or even mating copepods (Fig. 2).

Fig. 2: LOKI images of selected zooplankton taxa. Figure taken from Schmid et al. (2016).

Using the model developed in Schmid et al. (2016), we are now capable of automatically identifying LOKI images. In the case of copepods, identification can be as detailed as the stage level.

Moritz Schmid

Read Schmid et al. (2016) on researchgate.

To find out more about LOKI projects, follow: schmidscience.com

Reference: 

Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2016): The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography, http://dx.doi.org/10.1016/j.mio.2016.03.003.