FOREST/NON FOREST MAPPING USING LANDSAT THEMATIC MAPPER IMAGERY AND ARTIFICIAL NEURAL NETWORKS (ANNs)

Abstract

Forest area and the landscape level spatial pattern of forests are two of the indicators for sustainable forest management in Europe (MCPFE 2003). As they are important for forest policymaking (MCPFE 2007), there is a constant need of timely and accurate information about their current status. The aim of this study was to examine the potential of Artificial Neural Networks (ANNs) in differentiating forest from non-forested areas and to explore how the use of higherorder features, derived from a Landsat-5 TM image, could improve the performance of the ANNs classifier. The features were generated through the application of the Tasseled Cap transformation and Principal Component Analysis (PCA). The study area is a typical Mediterranean region located in the north-east part of Greece. The results from the classification accuracies of the study revealed that the most accurate map (Overall Accuracy (OA) =91,76 %-Kappa Index of Agreement (KIA) =0,787) was generated through the implementation of ANNs on the three bands produced by the application of Tasseled Cap transformation on the Landsat TM image. The comparison of the produced map products with the Pan-European Forest Map 2000 of the Joint Research Centre (JRC) (FMAP 2000), showed that the overall accuracy of the JRC map (OA=78,02 %-KIA=0,446) is lower than the ones of the maps that were produced by ANNs. Finally, it is concluded that, for this study area, the implemented methodology for differentiating areas covered by forest from other classes led to the production of maps of high accuracy, which exceed the adequate accuracy of the FMAP 2000.