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Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing / E. Belluco in Remote sensing of environment, vol 105 n° 1 (15/11/2006)  

Public [article]  inRemote sensing of environment > vol 105 n° 1 (15/11/2006) . - pp 54 - 67 Titre : Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing Type de document : Article/Communication Auteurs : E. Belluco, Auteur ; M. Camuffo, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 54 - 67 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] flore locale [Termes IGN] image CASI [Termes IGN] image hyperspectrale [Termes IGN] image Ikonos [Termes IGN] image MIVIS [Termes IGN] image multibande [Termes IGN] image Quickbird [Termes IGN] image ROSIS [Termes IGN] marais salé [Termes IGN] plante halophile [Termes IGN] Venise Résumé : (Auteur) Tidal marshes are characterized by complex patterns both in their geomorphic and ecological features. Such patterns arise through the elaboration of a network structure driven by the tidal forcing and through the interaction between hydrodynamical, geophysical and ecological components (chiefly vegetation). Intertidal morphological and ecological structures possess characteristic extent (order of kilometers) and small-scale features (down to tens of centimeters) which are not simultaneously accessible through field observations, thus making remote sensing a necessary observation tool. This paper describes a set of remote sensing observations from several satellite and airborne platforms, the collection of concurrent ground reference data and the vegetation distributions that may be inferred from them, with specific application to the Lagoon of Venice (Italy). The data set comprises ROSIS, CASI, MIVIS, IKONOS and QuickBird acquisitions, which cover a wide range of spatial and spectral resolutions. We show that spatially-detailed and quantitatively reliable vegetation maps may be derived from remote sensing in tidal environments through unsupervised (K-means) and supervised algorithms (Maximum Likelihood and Spectral Angle Mapper). We find that, for the objective of intertidal vegetation classification, hyperspectral data contain largely redundant information. This in particular implies that a reduction of the spectral features is required for the application of the Maximum Likelihood classifier. A large number of experiments with different feature extraction/selection algorithms show that the use of four bands derived from Maximum Noise Fraction transforms and four RGBI broad bands obtained by spectral averaging yield very similar classification performances. The classifications from hyperspectral data are somewhat superior to those from multispectral data, but the close performance and the results of the features reduction experiments show that spatial resolution affects classification accuracy much more importantly than spectral resolution. Monitoring schemes of tidal environment vegetation may thus be based on high-resolution satellite acquisitions accompanied by systematic ancillary field observations at a relatively limited number of reference sites, with practical consequences of some relevance. Copyright Elsevier Numéro de notice : A2006-502 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.06.006 En ligne : https://doi.org/10.1016/j.rse.2006.06.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28226 [article]