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Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa / Kabir Yunus Peerbhay in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 79 (May 2013) . - pp 19 - 28 Titre : Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa Type de document : Article/Communication Auteurs : Kabir Yunus Peerbhay, Auteur ; Onisimo Mutanga, Auteur ; Riyad Ismail, Auteur Année de publication : 2013 Article en page(s) : pp 19 - 28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Afrique du sud (état) [Termes IGN] analyse discriminante [Termes IGN] arbre (flore) [Termes IGN] classification dirigée [Termes IGN] espèce végétale [Termes IGN] forêt [Termes IGN] image AISA+ [Termes IGN] image hyperspectrale [Termes IGN] méthode des moindres carrés Résumé : (Auteur) Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies. Numéro de notice : A2013-231 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.01.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.01.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32369 [article]

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