Identifying four phytoplankton functional types from space: An ecological approach

Raitsos, Dionysios E., Lavender, Samantha J., Maravelias, Christos D., Haralabous, John, Richardson, Anthony and Reid, Philip C. (2008) Identifying four phytoplankton functional types from space: An ecological approach. Limnology and Oceanography, 53 2: 605-613. doi:10.4319/lo.2008.53.2.0605

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Author Raitsos, Dionysios E.
Lavender, Samantha J.
Maravelias, Christos D.
Haralabous, John
Richardson, Anthony
Reid, Philip C.
Title Identifying four phytoplankton functional types from space: An ecological approach
Journal name Limnology and Oceanography   Check publisher's open access policy
ISSN 0024-3590
1939-5590
Publication date 2008-03-31
Year available 2008
Sub-type Article (original research)
DOI 10.4319/lo.2008.53.2.0605
Open Access Status File (Publisher version)
Volume 53
Issue 2
Start page 605
End page 613
Total pages 9
Place of publication Waco, TX, United States
Publisher American Society of Limnology and Oceanography
Collection year 2009
Language eng
Subject C1
960305 Ecosystem Adaptation to Climate Change
050101 Ecological Impacts of Climate Change
Abstract Deriving maps of phytoplankton taxa based on remote sensing data using bio-optical properties of phytoplankton alone is challenging. A more holistic approach was developed using artificial neural networks, incorporating ecological and geographical knowledge together with ocean color, bio-optical characteristics, and remotely sensed physical parameters. Results show that the combined remote sensing approach could discriminate four major phytoplankton functional types (diatoms, dinoflagellates, coccolithophores, and silicoflagellates) with an accuracy of more than 70%. Models indicate that the most important information for phytoplankton functional type discrimination is spatio-temporal information and sea surface temperature. This approach can supply data for large-scale maps of predicted phytoplankton functional types, and an example is shown.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2009 Higher Education Research Data Collection
School of Mathematics and Physics
Ecology Centre Publications
 
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Created: Fri, 28 Nov 2008, 13:42:16 EST by Marie Grove on behalf of School of Mathematics & Physics