Pan-European forest/non-forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data
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 maps 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%.
landsat classification segmentation remote sensing forestry