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Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations / S. Sadro in Remote sensing of environment, vol 110 n° 2 (28/09/2007)  

Public [article]  inRemote sensing of environment > vol 110 n° 2 (28/09/2007) . - pp 226 - 239 Titre : Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations Type de document : Article/Communication Auteurs : S. Sadro, Auteur ; M. Gastil-Buhl, Auteur ; J. Melack, Auteur Année de publication : 2007 Article en page(s) : pp 226 - 239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte de la végétation [Termes IGN] classification dirigée [Termes IGN] couvert végétal [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] marais salé [Termes IGN] marée océanique [Termes IGN] modèle numérique de surface Résumé : (Auteur) We used LiDAR topographic data, AVIRIS hyperspectral data, and locally measured tidal fluctuations to characterize patterns of plant distribution within a southern California salt marsh (Carpinteria Salt Marsh (CSM)). LiDAR data required ground truthing and correction before they were suitable for use. Twenty to forty percent of the uncertainty associated with LiDAR was due to variance in the elevation of the target surface, the balance was attributed to error inherent in the LiDAR system. The incidence of LiDAR penetration of plant canopy cover (i.e., registration of ground elevation) was only three percent. The depth of LiDAR penetration into the plant canopy varied according to plant species composition; plant species-specific corrections significantly improved LiDAR accuracy (58% reduction in overall uncertainty) and with the use of ground-based surveys, reduced overall RMSE to an average of 6.3 cm in vegetated areas. A supervised classification of AVIRIS data was used to generate a vegetation map with six classification types; overall classification accuracy averaged 59% with a kappa coefficient of 0.40. The vegetation classification map was overlaid with a LiDAR-based digital elevation model (DEM) to compute elevation distributions and frequencies of tidal inundation. The average elevations of the dominant plant classifications found in CSM (e.g., Salicornia virginica, Jaumea carnosa, and salt-grass mix, a mixture of multiple marsh plant species) occurred within a 17 cm range, a vertical change that resulted in a 7% difference in the period of tidal inundation. Numéro de notice : A2007-150 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2007.02.024 En ligne : https://doi.org/10.1016/j.rse.2007.02.024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28513 [article]