Dr. Morgan Mangeas Profile

Dr. Morgan Mangeas

at Institut de Recherche pour le Développement

SPIE Involvement:

Author

Proceedings Article | 10 December 2014 Paper

A statistical algorithm for estimating chlorophyll concentration from MODIS data

Guillaume Wattelez, Cécile Dupouy, Morgan Mangeas, Jérôme Lèfevre, T. Touraivane, Robert Frouin

Proceedings Volume 9261, 92611S (2014) https://doi.org/10.1117/12.2086297

KEYWORDS: Data modeling, Statistical modeling, MODIS, Databases, Reflectivity, Satellites, Statistical analysis, Sensors, Error analysis, Algorithm development

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Proceedings Article | 10 December 2014 Paper

Phytoplankton global mapping from space with a support vector machine algorithm

Florian de Boissieu, Christophe Menkes, Cécile Dupouy, Martin Rodier, Sophie Bonnet, Morgan Mangeas, Robert Frouin

Proceedings Volume 9261, 92611R (2014) https://doi.org/10.1117/12.2083730

KEYWORDS: Pulmonary function tests, Stanford Linear Collider, Remote sensing, Detection and tracking algorithms, Feature selection, Reflectivity, Algorithm development, Satellites, Ecosystems, In situ remote sensing

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In 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.

Proceedings Article | 4 November 2010 Paper

Monitoring optical properties of the southwest tropical Pacific

Cécile Dupouy, Tatiana Savranski, Jérôme Lefevre, Marc Despinoy, Morgan Mangeas, Rosalie Fuchs, Vincent Faure, Sylvain Ouillon, Michel Petit

Proceedings Volume 7858, 78580J (2010) https://doi.org/10.1117/12.869888

KEYWORDS: MODIS, Absorption, Reflectivity, Backscatter, Satellites, In situ metrology, Particles, Remote sensing, Optical properties, Water

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Proceedings Article | 11 December 2008 Paper

A case study on an improved method for very high spatial resolution satellite image classification: watersheds across the complex environment of high Pacific islands

Marc Despinoy, Maelle Aubert, Mickael Barotin, Morgan Mangeas

Proceedings Volume 7149, 71490O (2008) https://doi.org/10.1117/12.804805

KEYWORDS: Image segmentation, Clouds, Satellites, Image classification, Roads, Image processing, Spatial resolution, Earth observing sensors, Environmental sensing, Binary data

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Conference Committee Involvement (1)

SPIE Asia-Pacific Remote Sensing

17 November 2008 | Noumea, New Caledonia

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