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Auteur D. Vikhamar |
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Snow-cover mapping in forest by constrained linear spectral unimixing of MODIS data / D. Vikhamar in Remote sensing of environment, vol 88 n° 3 (15/12/2003)
[article]
Titre : Snow-cover mapping in forest by constrained linear spectral unimixing of MODIS data Type de document : Article/Communication Auteurs : D. Vikhamar, Auteur ; R. Solberg, Auteur Année de publication : 2003 Article en page(s) : pp 309 - 323 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] cartographie thématique
[Termes IGN] classification
[Termes IGN] forêt
[Termes IGN] image Terra-MODIS
[Termes IGN] neigeRésumé : (Auteur) A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and endmember spectra. The landcover fraction map is applied in the unmixing procedure to identify the number and types of endmembers for every pixel, but also to set constraints on the area fractions of the forest endmembers. SnowFrac was applied on two Terra Moderate Resolution Imaging Spectroradiometer (MODIS) images with different snow conditions covering a forested area in southern Norway. Six experiments were carried out, each with different endmember constraints. Estimated snow-cover fractions were compared with snow-cover fraction reference maps derived from two Landsat Enhanced Thematic Mapper Plus (ETM+) images acquired the same days as the MODIS images. Results are presented for non-forested areas, deciduous forests, coniferous forests and mixed deciduous/coniferous forests. The snow-cover fraction estimates are enhanced by increasing constraints introduced to the unmixing procedure. The classification accuracy shows that 96% of the pixels are classified with less than 20% error (absolute units) on 7 May 2001 when all forested and non-forested areas are included. The corresponding figure for 4 May 2000 is 88%. Numéro de notice : A2003-347 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.06.004 En ligne : https://doi.org/10.1016/j.rse.2003.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26427
in Remote sensing of environment > vol 88 n° 3 (15/12/2003) . - pp 309 - 323[article]