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Annual crop type classification of the US great plains for 2000 to 20011 / Daniel M. Howard in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 6 (June 2014) . - pp 537 - 549 Titre : Annual crop type classification of the US great plains for 2000 to 20011 Type de document : Article/Communication Auteurs : Daniel M. Howard, Auteur ; Bruce K. Wyllie, Auteur Année de publication : 2014 Article en page(s) : pp 537 - 549 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification à base de connaissances [Termes IGN] climatologie [Termes IGN] culture [Termes IGN] environnement [Termes IGN] modèle de géopotentiel [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] surveillance agricole Résumé : (Auteur) The purpose of this study was to increase the spatial and temporal availability of crop classification data. In this study, nearly 16.2 million crop observation points were used in the training of the US Great Plains classification tree crop type model (CTM). Each observation point was further defined by weekly Normalized Difference Vegetation Index, annual climate, and a number of other biogeophysical environmental characteristics. This study accounted for the most prevalent crop types in the region, including, corn, soybeans, winter wheat, spring wheat, cotton, sorghum, and alfalfa. Annual CTM crop maps of the US Great Plains were created for 2000 to 2011 at a spatial resolution of 250 meters. The CTM achieved an 87 percent classification success rate on 1.8 million obser-vation points that were withheld from model training. Product validation was performed on greater than 15,000 county records with a coefficient of determination of R2 = 0.76. Numéro de notice : A2014-293 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.6.537-549 En ligne : https://doi.org/10.14358/PERS.80.6.537-549 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33196 [article]