Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

Kurekin, Andrey A.; Miller, Peter I.; Woerd, H.J. van der

doi:10.1016/j.hal.2013.11.003

Cited by 68 publications

(65 citation statements)

References 24 publications

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“…In addition, satellite PG data were used to assess globally particulate organic carbon export (Mouw et al, 2016), for detection of regional HAB events (e.g., Kurekin et al, 2014), the estimation of recruitment of juvenile fish (Trzcinski et al, 2013) and for inferring globally oceanic emissions of volatile organic compounds (Arnold et al, 2009;Booge et al, 2016).…”

Section: Approachmentioning

confidence: 99%

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit

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Section: Approachmentioning

confidence: 99%

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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“…The detection of phytoplankton species causing harmful algal blooms (HABs) is a major concern for fisheries and shellfish farming management (Sourisseau et al, 2016), and recent algorithm developments have proved useful to provide early warnings (Davidson et al, 2009) or statistical estimation of HAB-related risks (Kurekin et al, 2014). Enhanced characterization of the composition of the particulate assemblage could also be used to improve satellite-derived aquaculture products.…”

Section: Shellfish Ecology From Space?mentioning

confidence: 99%

Shellfish Aquaculture from Space: Potential of Sentinel2 to Monitor Tide-Driven Changes in Turbidity, Chlorophyll Concentration and Oyster Physiological Response at the Scale of an Oyster Farm

2017

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The algorithms of Novoa et al. (2017) and Gons et al. (2005) were recalibrated and applied to Sentinel2 data to retrieve suspended particulate matter (SPM) and chlorophyll a (chl a) concentration in the environmentally and economically important intertidal zones. Sentinel2-derived chl a and SPM concentration distributions were analyzed at the scale of an oyster farm over a variety of tidal conditions. Sentinel2 imagery was then coupled with ecophysiological modeling to analyze the influence of tide-driven chl a and SPM dynamics on oyster clearance and chl consumption rates. Within the studied oyster farming site (Bourgneuf Bay along the French Atlantic coast), chl consumption rate mirrored the changes in chl a concentration during neap tides, whereas oyster clearance and chl consumption rates were both negatively impacted by high SPM concentration during spring tides.

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“…For future operational use of this or other models, remote identification of seed populations in real time will be required. report some success in developing a discrimination algorithm to identify K. mikimotoi from ocean colour data, with further development by Shutler et al (2012) and Kurekin et al (2014). Such remote-sensing based systems are necessary to allow the accurate initiation of operational numerical models.…”

mentioning

confidence: 99%

“…Holt and Proctor, 2008) and/or unstructured grid models (Aleynik et al, this issue) potentially offer just such necessary resolution. Combined with further refinement of the algal transport model and the K. mikimotoi-HAB classification algorithm Shutler et al, 2012;Kurekin et al, 2014), these developments offer the real possibility of an effective operational early warning system for UK shelf waters. Table 1.…”

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confidence: 99%

Individual-based modelling of the development and transport of a Karenia mikimotoi bloom on the North-west European continental shelf

et al. 2016

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Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

Cited by 68 publications

References 24 publications

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Shellfish Aquaculture from Space: Potential of Sentinel2 to Monitor Tide-Driven Changes in Turbidity, Chlorophyll Concentration and Oyster Physiological Response at the Scale of an Oyster Farm

Individual-based modelling of the development and transport of a Karenia mikimotoi bloom on the North-west European continental shelf

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