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Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones / C. Gomez in Remote sensing of environment, vol 114 n° 11 (15/11/2010)  

Public [article]  inRemote sensing of environment > vol 114 n° 11 (15/11/2010) . - pp 2731 - 2744 Titre : Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones Type de document : Article/Communication Auteurs : C. Gomez, Auteur ; M. Mangeas, Auteur ; Marcel Petit, Auteur ; Christina Corbane, Auteur ; et al., Auteur Année de publication : 2010 Article en page(s) : pp 2731 - 2744 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] carte thématique [Termes IGN] classification dirigée [Termes IGN] classification par arbre de décision [Termes IGN] classification par réseau neuronal [Termes IGN] Coffea (genre) [Termes IGN] couvert forestier [Termes IGN] image à haute résolution [Termes IGN] image panchromatique [Termes IGN] image Quickbird [Termes IGN] modèle numérique de surface [Termes IGN] Nouvelle-Calédonie [Termes IGN] ombre [Termes IGN] pansharpening (fusion d'images) [Termes IGN] prédiction Résumé : (Auteur) In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity. Numéro de notice : A2010-402 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2010.06.007 En ligne : https://doi.org/10.1016/j.rse.2010.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30595 [article]

N-FindR method versus independent component analysis for lithological identification in hyperspectral imagery / C. Gomez in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°23-24 (December 2007) . - pp 5315 - 5338 Titre : N-FindR method versus independent component analysis for lithological identification in hyperspectral imagery Type de document : Article/Communication Auteurs : C. Gomez, Auteur ; H. Le Borgne, Auteur ; P. Allemand, Auteur ; C. Delacourt, Auteur ; P. Ledru, Auteur Année de publication : 2007 Article en page(s) : pp 5315 - 5338 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes indépendantes [Termes IGN] classification automatique [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] lithologie [Termes IGN] méthode robuste [Termes IGN] Namibie [Termes IGN] photo-interprétation assistée par ordinateur Résumé : (Auteur) The current study addresses the problem of the identification of each natural material present in each pixel of a hyperspectral image. Two end member extraction methods from hyperspectral imagery were studied: the N-FindR method and the independent component analysis (ICA). The N-FindR is an automatic technique that selects extreme points (end members) of an n-dimensional scatter plot. It assumes the existence of pure pixels in the distribution, which is infrequent in practice. ICA is a blind source separation technique studied in the signal processing community, which allows each spectrum of natural elements (end member spectra) to be extracted from the observation of some linear combinations of these. It considers a more realistic situation than N-FindR, assuming a spectra mixture for all the pixels. To increase the robustness of ICA, continuum-removed reflectance spectra were used and an iterative algorithm was introduced that takes into account a major part of the available information. The end member abundances were estimated by the fully constrained least squares spectral mixture analysis (FLCS). The end member identification and quantification were carried out on two surficial formations of a semi arid region located in the Rehoboth region, in Namibia, from hyperspectral Hyperion data. It appears that the two end member extraction methods have a similar potential. Whichever end member extraction method is used, the analysis of the rock abundance maps produces a lot of geological information: the distribution of natural elements is in line with the field observations and allows the description of the formation processes of surficial units. Copyright Taylor & Francis Numéro de notice : A2007-536 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701227679 En ligne : https://doi.org/10.1080/01431160701227679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28899 [article]

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