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Mapping forest degradation in the Eastern Amazon SPOT 4 through spectral mixture models / Cristiano B. Souza in Remote sensing of environment, vol 87 n° 4 (15/11/2003)  

Public [article]  inRemote sensing of environment > vol 87 n° 4 (15/11/2003) . - pp 494 - 506 Titre : Mapping forest degradation in the Eastern Amazon SPOT 4 through spectral mixture models Type de document : Article/Communication Auteurs : Cristiano B. Souza, Auteur ; L. Firestone, Auteur ; L. Moreira Silva, Auteur ; D. Roberts, Auteur Année de publication : 2003 Article en page(s) : pp 494 - 506 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse visuelle [Termes IGN] cartographie écologique [Termes IGN] classification ascendante hiérarchique [Termes IGN] déboisement [Termes IGN] image Ikonos [Termes IGN] image multibande [Termes IGN] image SPOT [Termes IGN] statistique mathématique Résumé : (Auteur) In this paper, we present a methodology to map classes of degraded forest in the Eastern Amazon. Forest degradation field data, available in the literature, and 1-m resolution IKONOS image were linked with fraction images (vegetation, nonphotosynthetic vegetation (NPV), soil and shade) derived from spectral mixture models applied to a Satellite Pour l'Observation de la Terre (SPOT) 4 multispectral image. The forest degradation map was produced in two steps. First, we investigated the relationship between ground (i.e., field and IKONOS data) and satellite scales by analyzing statistics and performing visual analyses of the field classes in terms of fraction values. This procedure allowed us to define four classes of forest at the SPOT 4 image scale, which included: intact forest; logged forest (recent and older logged forests in the field); degraded forest (heavily burned, heavily logged and burned forests in the field) ; and regeneration (old heavily logged and old heavily burned forest in the field). Next, we used a decision tree classifier (DTQ to define a set of rules to separate the forest classes using the fraction images. We classified 35% of the forest area (2097.3 km2 ) as intact forest. Logged forest accounted for 56% of the forest area and 9% of the forest area was classified as degraded forest. The resultant forest degradation map showed good agreement (86% overall accuracy) with areas of degraded forest visually interpreted from two IKONOS images. In addition, high correlation (R2 = 0.97) was observed between the total live aboveground biomass of degraded forest classes (defined at the field scale) and the NPV fraction image. The NPV fraction also improved our ability to mapping of old selectively logged forests. Numéro de notice : A2003-338 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2002.08.002 En ligne : https://doi.org/10.1016/j.rse.2002.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22633 [article]