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De Boissieu F., Menkes Christophe, Dupouy Cécile, Rodier Martine, Bonnet Sophie, Mangeas Morgan, Frouin R. (2014). Phytoplankton global mapping from space with a support vector machine algorithm. In : Frouin R.J. (ed.), Pan D. (ed.), Murakami H. (ed.) Ocean remote sensing and monitoring from space. Bellingham WA : SPIE, (9261), 92611R [14 p. en ligne] (Proceedings of SPIE ; 9261). SPIE Asia-Pacific Remote Sensing, Pékin (CHN), 2014/10/13-16. ISSN 0277-786X

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Lien direct chez l'éditeur doi:10.1117/12.2083730

Titre
Phytoplankton global mapping from space with a support vector machine algorithm
Année de publication2014
Type de documentArticle référencé dans le Web of Science WOS:000348837900041
AuteursDe Boissieu F., Menkes Christophe, Dupouy Cécile, Rodier Martine, Bonnet Sophie, Mangeas Morgan, Frouin R.
InFrouin R.J. (ed.), Pan D. (ed.), Murakami H. (ed.) Ocean remote sensing and monitoring from space
SourceBellingham WA : SPIE, 2014, 92611R [14 p. en ligne] (Proceedings of SPIE ; 9261). ISSN 0277-786X
ColloqueSPIE Asia-Pacific Remote Sensing, Pékin (CHN), 2014/10/13-16
RésuméIn recent years great progress has been made in global mapping of phytoplankton from space. Two main trends have emerged, the recognition of phytoplankton functional types (PFT) based on reflectance normalized to chlorophyll-a concentration, and the recognition of phytoplankton size class (PSC) based on the relationship between cell size and chlorophyll-a concentration. However, PFTs and PSCs are not decorrelated, and one approach can complement the other in a recognition task. In this paper, we explore the recognition of several dominant PFTs by combining reflectance anomalies, chlorophyll-a concentration and other environmental parameters, such as sea surface temperature and wind speed. Remote sensing pixels are labeled thanks to coincident in-situ pigment data from GeP&CO, NOMAD and MAREDAT datasets, covering various oceanographic environments. The recognition is made with a supervised Support Vector Machine classifier trained on the labeled pixels. This algorithm enables a non-linear separation of the classes in the input space and is especially adapted for small training datasets as available here. Moreover, it provides a class probability estimate, allowing one to enhance the robustness of the classification results through the choice of a minimum probability threshold. A greedy feature selection associated to a 10-fold cross-validation procedure is applied to select the most discriminative input features and evaluate the classification performance. The best classifiers are finally applied on daily remote sensing datasets (SeaWIFS, MODISA) and the resulting dominant PFT maps are compared with other studies. Several conclusions are drawn: (1) the feature selection highlights the weight of temperature, chlorophyll-a and wind speed variables in phytoplankton recognition; (2) the classifiers show good results and dominant PFT maps in agreement with phytoplankton distribution knowledge; (3) classification on MODISA data seems to perform better than on SeaWIFS data, (4) the probability threshold screens correctly the areas of smallest confidence such as the interclass regions.
Plan de classementOcéanographie [126TELAPP05] ; Phytoplancton [034BIOVEG02]
DescripteursTELEDETECTION SPATIALE ; DONNEES SATELLITE ; PHYTOPLANCTON ; CLASSIFICATION ; ALGORITHME ; COULEUR DE L'OCEAN
LocalisationFonds IRD [F B010063840]
Identifiant IRDfdi:010063840
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010063840

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