Heterogeneous forest classification by creating mixed vegetation classes using EO-1 Hyperion

Hyperspectral satellite data is an efficient tool in vegetation mapping; however, previous studies indicate that classifying heterogeneous forests might be difficult. In this study, we propose a mapping method for a heterogeneous forest using the data of the Earth Observing-1 (EO-1) Hyperion supplemented by field survey. We introduced a band reduction method to raise classification accuracy of the Support Vector Machine classification algorithm and compared the results to the one reduced by principal component analysis (PCA), stepwise discriminant analysis (SDA), and the original data set. We also used a modified version of the Vegetation–Impervious–Soil model to create mixed vegetation classes consisting of the commonly mixing species in the area and classified them using Decision Tree classification method. We managed to achieve 84.28% approximately using our band reduction method which is 2.36% increase compared to PCA (81.92%), 1.43% compared to the SDA (82.85%), and 7.61% compared to the original data set (76.67%). Introducing the mixed vegetation classes raised the overall accuracy even higher (85.79%).