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Article << Previous     |     Next >>   Contents Vol 13(3)

A semi-automated object-oriented model for burned area mapping in the Mediterranean region using Landsat-TM imagery

G. H. Mitri A C, I. Z. Gitas B

A Department of Environmental Management, Mediterranean Agronomic Institute of Chania, Crete, Greece.
B Laboratory of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, Greece. Telephone: +30 2310 992699; fax: +30 2310 998797; email: igitas@for.auth.gr
C Corresponding author. Telephone: +30 2310 992688; fax: +30 2310 998897; email: gmitri@for.auth.gr
 
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Abstract

Pixel-based classification methods that make use of the spectral information derived from satellite images have been repeatedly reported to create confusion between burned areas and non-vegetation categories, especially water bodies and shaded areas. As a result of the aforementioned, these methods cannot be used on an operational basis for mapping burned areas using satellite images. On the other hand, object-based image classification allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. Sophisticated classification, incorporating contextual and semantic information, can be performed by using not only image object attributes, but also the relationship between networked image objects. In this study, the synergy of all these features allowed us to address image analysis tasks that, up until now, have not been possible. The aim of this work was to develop an object-based classification model for burned area mapping in the Mediterranean using Landsat-TM imagery. The object-oriented model developed to map a burned area on the Greek island of Thasos was then used to map other burned areas in the Mediterranean region after the Landsat-TM images had been radiometrically, geometrically and topographically corrected. The results of the research showed that the developed object-oriented model was transferable and that it could be effectively used as an operative tool for identifying and mapping the three different burned areas (~98% overall accuracy).

   
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