Centre de documentation
scientifique

Delineation of forest/nonforest land use classes using nearest neighbor methods / R. Haapanen in Remote sensing of environment, vol 89 n° 3 (15/02/2004)  

Public [article]  inRemote sensing of environment > vol 89 n° 3 (15/02/2004) . - pp 265 - 271 Titre : Delineation of forest/nonforest land use classes using nearest neighbor methods Type de document : Article/Communication Auteurs : R. Haapanen, Auteur ; A.R. Ek, Auteur ; Andrew O. Finley, Auteur ; M.E. Bauer, Auteur Année de publication : 2004 Article en page(s) : pp 265 - 271 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification barycentrique [Termes IGN] délimitation [Termes IGN] forêt [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] occupation du sol Résumé : (Auteur) The k-Nearest Neighbor (kNN) method of forest attribute estimation and mapping has become an integral part of national forest inventory methods in Finland in the last decade. This success of kNN method in facilitating multisource inventory has encouraged trials of the method in the Great Lakes Region of the United States. Here we present results from applying the method to Landsat TM and ETM+ data and land cover data collected by the USDA Forest Service's Forest Inventory and Analysis (FIA) program. In 1999, the FIA program in the state of Minnesota moved to a new annual inventory design to reach its targeted full sampling intensity over a 5-year period. This inventory design also utilizes a new 4-subplot cluster plot configuration. Using this new plot design together with 1 year of field plot observations, the kNN classification of forest/nonforest/water achieved overall accuracies ranging from 87% to 91%. Our analysis revealed several important behavioral features associated with kNN classification using the new FIA sample plot design. Results demonstrate the simplicity and utility of using kNN to produce FIA defined forest/nonforest/water classifications. Numéro de notice : A2004-017 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.10.002 En ligne : https://doi.org/10.1016/j.rse.2003.10.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26545 [article]