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Mapping dominant vegetation communities at Meili Snow Mountain, Yunnan Province, China using satellite imagery and plant community data / Z. Zhang in Geocarto international, vol 23 n° 2 (April - May 2008)  

Public [article]  inGeocarto international > vol 23 n° 2 (April - May 2008) . - pp 135 - 153 Titre : Mapping dominant vegetation communities at Meili Snow Mountain, Yunnan Province, China using satellite imagery and plant community data Type de document : Article/Communication Auteurs : Z. Zhang, Auteur ; E. De Clercq, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 135 - 153 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse spatiale [Termes IGN] carte de la végétation [Termes IGN] classification bayesienne [Termes IGN] classification par maximum de vraisemblance [Termes IGN] flore locale [Termes IGN] Kappa de Cohen [Termes IGN] milieu naturel [Termes IGN] montagne [Termes IGN] occupation du sol [Termes IGN] répartition géographique [Termes IGN] Yunnan (Chine) Résumé : (Auteur) Mapping dominant vegetation communities is important work for vegetation scientists. It is very difficult to map dominant vegetation communities using multispectral remote sensing data only, especially in mountain areas. However plant community data contain useful information about the relationships between plant communities and their environment. In this paper, plant community data are linked with remote sensing to map vegetation communities. The Bayesian soft classifier was used to produce posterior probability images for each class. These images were used to calculate the prior probabilities. One hundred and eighty plant plots at Meili Snow Mountain, Yunnan Province, China were used to characterize the vegetation distribution for each class along altitude gradients. Then, the frequencies were used to modify the prior probabilities of each class. After stratification in a vegetation part and a non-vegetation part, a maximum-likelihood classification with equal prior probabilities was conducted, yielding an overall accuracy of 82.1% and a kappa accuracy of 0.797. Maximum-likelihood classification with modified prior probabilities in the vegetation part, conducted with a conventional maximum-likelihood classification for the non-vegetation part, yielded an overall accuracy of 87.7%, and a kappa accuracy of 0.861. Copyright Taylor & Francis Numéro de notice : A2008-078 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040701337410 En ligne : https://doi.org/10.1080/10106040701337410 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29073 [article]

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