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Mountain pine beetle red-attack forest damage classification using stratified Landsat TM data in British Columbia, Canada / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 3 (March 2003)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 3 (March 2003) . - pp 283 - 288 Titre : Mountain pine beetle red-attack forest damage classification using stratified Landsat TM data in British Columbia, Canada Type de document : Article/Communication Auteurs : Steven E. Franklin, Auteur ; Michael A. Wulder, Auteur ; R.S. Skakun, Auteur ; A.L. Caroll, Auteur Année de publication : 2003 Article en page(s) : pp 283 - 288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte forestière [Termes IGN] cartographie thématique [Termes IGN] classification par maximum de vraisemblance [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] dommage matériel [Termes IGN] image Landsat-TM [Termes IGN] Insecta [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] maladie phytosanitaire [Termes IGN] outil d'aide à la décision [Termes IGN] Pinus contorta [Termes IGN] sylviculture [Termes IGN] système d'information géographique Résumé : (Auteur) The identification and classification of mountain pine beetle, Dentroctonus ponderos (Hopkins), red-attack damage patterns in a mature lodgepole pine (Pinus contorta) forest located in the Forst St-James forest Distric, British Columbia, was accomplished using 1999 Landsat TM satellite imagery, 1999 mountain pine beetle field and aerial survey point data, and GIS forest inventory data. Unrelated variance in the observed spectral response at mountain pine beetle field and aerial survey points was reduced following image stratification with the GIS forest inventory data and removal of other factors uncharacteristic of red-attack damage. Locations of known mountain pine beetle infestation were used to train a maximum-likelihood algorithm ; overall classification accuracy was 73 percent, based on an assessment of 360 independent validation points. If local stand variability is reduced prior to signature generation, accuracies and mao products can be useful for those involved in active forest management decision making regarding mountain pine beetle infestations. Numéro de notice : A2003-030 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.69.3.283 En ligne : https://doi.org/10.14358/PERS.69.3.283 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22327 [article]