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An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)  

Public [article]  inSilva fennica > vol 54 n° 2 (March 2020) Titre : An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data Type de document : Article/Communication Auteurs : Olga Grigorieva, Auteur ; Olga Brovkina, Auteur ; Alisher Saidov, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Betula (genre) [Termes IGN] carte forestière [Termes IGN] classification [Termes IGN] erreur de classification [Termes IGN] image hyperspectrale [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] phénologie [Termes IGN] Pinus (genre) [Termes IGN] réflectance spectrale [Termes IGN] République Tchèque [Termes IGN] Russie [Termes IGN] signature spectrale [Termes IGN] variation saisonnière Résumé : (auteur) his study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectral–temporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time. Numéro de notice : A2020-324 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10143 Date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.14214/sf.10143 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95198 [article]