This paper describes a simple and adaptive methodology for large area forest/non-forest mapping using Landsat ETM+ imagery and CORINE Land Cover 2000. The methodology is based on scene-by-scene analysis and supervised classification. The fully automated processing chain consists of several phases, including image segmentation, clustering, adaptive spectral representativity analysis, training data extraction and nearest-neighbour classification. This method was used to produce a European forest/non-forest map through the processing of 415 Landsat ETM+ scenes. The resulting forest/non-forest map was validated with three independent data sets. The results show that the map¿s overall point-level agreement with our validation data generally exceeds 80%, and approaches 90% in central European conditions. Comparison with country-level forest area statistics shows that in most cases the difference between the forest proportion of the derived map and that computed from the published forest area statistics is below 5%.
PEKKARINEN Anssi;
REITHMAIER Lucia;
STROBL Peter;
2010-01-19
ELSEVIER SCIENCE BV
JRC37818
0924-2716,
www.elsevier.com/locate/isprsjprs,
https://publications.jrc.ec.europa.eu/repository/handle/JRC37818,
10.1016/j.isprsjprs.2008.09.004,
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