Stéphane Couturier

Universidad Nacional Autónoma de México, Instituto de Geografía, Faculty Member

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Stéphane Couturier received the B.Sc. degree from the College for Aeronautics and Space Engineering (SupAero), Toulouse, France, in 1996, the M.Sc. degree from the University of Paris VI, Paris, France, the Ph.D. degree in Remote Sensing of the
Biosphere from the University of Toulouse III (Paul Sabatier University), Toulouse, and the Ph.D. degree in Geography from Universidad Nacional
Autónoma de México (UNAM).
He is currently with the Laboratorio de Análisis Geoespacial, Instituto de Geografía, Universidad Nacional Autónoma de México. He worked in Singapore, in Indonesia and in Mexico, focusing on remote sensing applications to highly biodiverse and highly dynamic environments.
These include robust tropical deforestation assessment, fire risk mapping, water quality assessment in watersheds undergoing rapid deforestation, urban expansion assessment, and the accuracy assessment of land-use/land-cover maps in dynamic environments. He also developed techniques in community geography and takes part in local collaborative planning with an incidence on land uses in Mexico. His major interest is to use geomatics and contribute to visually enhanced geospatial representations of global change, as well as to drive local changes for a better livelihood in urban and rural environments in the global South.

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Stephane Couturier

Pierre Couteron

Jose Manuel Morales Delgado

Joaquin Solana-Gutierrez

Gregory Asner

Carnegie Institution of Washington

Cristina Gómez

raphael pelissier

Institut de Recherche pour le Développement (IRD - France)

Damiano Gianelle

Bachari Nour el islam

Université des Sciences et Technologie Houari Boumediene (USTHB)

J. Gastellu-etchegorry

Université Paul Sabatier de Toulouse

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Papers by Stéphane Couturier

A model-based performance test for forest classifiers on remote-sensing imagery

Forest Ecology and Management, 2009

Ambiguity between forest types on remote-sensing imagery is a major cause of errors found in accu... more Ambiguity between forest types on remote-sensing imagery is a major cause of errors found in accuracy assessments of forest inventory maps. This paper presents a methodology, based on forest plot inventory, ground measurements and simulated imagery, for systematically quantifying these ambiguities in the sense of the minimum distance (MD), maximum likelihood (ML), and frequency-based (FB) classifiers. The method is tested with multi-spectral IKONOS images acquired on areas containing six major communities (oak, pine, fir, primary and secondary high tropical forests, and avocado plantation) of the National Forest Inventory (NFI) map in Mexico. A structural record of the canopy and optical measurements (leaf area index and soil reflectance) were performed on one plot of each class. Intra-class signal variation was modelled using the Discrete Anisotropic Radiative Transfer (DART) simulator of remote-sensing images. Atmospheric conditions were inferred from ground measurements on reference surfaces and leaf optical properties of each forest type were derived from the IKONOS forest signal. Next, all forest types were simulated, using a common environmental configuration, in order to quantify similarity among all forest types, according to MD, ML and FB classifiers. Classes were considered ambiguous when their dissimilarity was smaller than intra-class signal variation. DART proved useful in approximating the pixel value distribution and the ambiguity pattern measured on real forest imagery. In the case study, the oak forest and the secondary tropical forest were both distinguishable from all other classes using an MD classifier in a 25 m window size, whereas pine and primary tropical forests were ambiguous with three other classes using MD. By contrast, only two pairs of classes were found ambiguous for the ML classifier and only one for the FB classifier in that same window size. The avocado plantation was confounded with the primary tropical forest for all classifiers, presumably because the reflectance of both types of forest is governed by a deep canopy and a similar shadow area. We confronted the results of this study with the confusion matrix from the accuracy assessment of the NFI map. An asset of this model-based method is its applicability to a variety of sensor types, eco-zones and class definitions.

Stéphane Couturier

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