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A quantitative comparison of methods for classifying burned areas with LISS-3 imagery / R.M. Roman-Cuesta in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 9 (May 2005) . - pp 1979 - 2003 Titre : A quantitative comparison of methods for classifying burned areas with LISS-3 imagery Type de document : Article/Communication Auteurs : R.M. Roman-Cuesta, Auteur ; J. Retana, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1979 - 2003 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] analyse multibande [Termes IGN] classificateur paramétrique [Termes IGN] classification dirigée [Termes IGN] image IRS-LISS [Termes IGN] impact sur l'environnement [Termes IGN] incendie de forêt [Termes IGN] surveillance écologique Résumé : (Auteur) Environmental agencies frequently require tools for quick assessments of areas affected by large fires. Remote sensing techniques have been reported as efficient tools to evaluate the effects of fire. However, there exist few quantitative comparisons about the performance of the diverse methods. This study quantitatively evaluated the accuracy of five different techniques, a field survey and four satellite-based techniques, in order to quickly classify a large forest fire that occurred in 1998 in Solsonès (north-east Spain) by means of an IRS LISS-3 image. Three pure classes were determined: burned area, unburned vegetation, and bare soil; along with a non-pure class that we called mixed area. These selected techniques were included into a tree classifier to investigate their partial contribution to the final classification. The most accurate methods when focusing on pure classes were those directly related to the spectral characteristics of the pixel: Reflectance Data and Spectral Unmixing (82% of overall accuracy), versus the poorer performances of Vegetation Indices (70%), Textural measures (72%) and the field survey (68.6%). Since no image processing technique was applied to the Raw Reflectance Data, it can be considered the most cost-effective method, and the tree classifier reinforces its importance. The results of this study reveal that time consuming and expensive methods are not necessarily the most accurate, especially when potentially easily distinguishable classes are involved. Numéro de notice : A2005-258 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331299315 En ligne : https://doi.org/10.1080/01431160512331299315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27394 [article]

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