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Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing / C. Tottrup in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 4 (February 2004) . - pp 717 - 730 Titre : Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing Type de document : Article/Communication Auteurs : C. Tottrup, Auteur Année de publication : 2004 Article en page(s) : pp 717 - 730 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] canopée [Termes IGN] carte de la végétation [Termes IGN] classification par maximum de vraisemblance [Termes IGN] écosystème [Termes IGN] forêt tropicale [Termes IGN] image Landsat-TM [Termes IGN] image multitemporelle [Termes IGN] lissage de données Résumé : (Auteur) The present study explores the possibility of using Landsat imagery for mapping tropical forest types with relevance to forest ecosystem services. The central part in the classification process is the use of multi-date image data and pre-classification image smoothing. The study argues that multi-date imagery contains information on phenological and canopy structural properties and shows how the use of multi-date imagery has a significant impact on classification accuracy. Furthermore, the study shows the value of applying small kernel smoothing filters to reduce in-class spectral variability and enhance between-class spectral separability. Making use of these approaches and a maximum likehood algorithm, six tropical forest types were classified with an overall accuracy of 90.94%, and with individual forest classes mapped with accuracies above 75.19% (user's accuracy) and above 74.17% (producer accuracy). Numéro de notice : A2004-074 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160310001598926 En ligne : https://doi.org/10.1080/01431160310001598926 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26602 [article]

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