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Multi-attribute vegetation maps of Forest Service lands in California supporting resource management decisions / Janet Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 66 n° 10 (October 2000)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 66 n° 10 (October 2000) . - 8 p. ; pp 1209 - 1217 Titre : Multi-attribute vegetation maps of Forest Service lands in California supporting resource management decisions Type de document : Article/Communication Auteurs : Janet Franklin, Auteur ; Curtis E. Woodcock, Auteur ; R. Warbington, Auteur Année de publication : 2000 Article en page(s) : 8 p. ; pp 1209 - 1217 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] aide à la décision [Termes IGN] base de données localisées [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte de la végétation [Termes IGN] couvert végétal [Termes IGN] flore locale [Termes IGN] forêt [Termes IGN] image Landsat-TM [Termes IGN] incendie de forêt [Termes IGN] inventaire forestier étranger (données) [Termes IGN] risque naturel Résumé : (Auteur) Vegetation databases (digital maps) for USDA Forest Service lands in California (approximately 10 million ha) have been developed over the last decade using remote sensing and GIS methods. The databases are intended to support national and regional land-cover inventory and monitoring, interagency conservation and fire risk assessment, and wildlife habitat evaluation, as well as more traditional uses including land management planning and forest inventory within each National Forest. The digital maps are fine-scale relative to their extent, being derived from 30-m-resolution Landsat Thematic Mapper (TM) data and digital elevation models (DEMs). Map attributes included a vegetation life form class, a vegetation type, and canopy cover and size class estimates for forested polygons. Land-cover and vegetation type labels were more accurate than forest structure estimates. However, the mapping methodology is not static. New remote sensing data and analysis methods offer some promise to improve map attribute estimation. The database is being provided by the Forest Service to agency personnel, cooperators, and the public. Numéro de notice : A2000-259 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/INFORMATIQUE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/2000journal/october/2000_oct_1209- [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21676 [article]