ASSESSMENT OF TEXTURE FEATURES FOR BRAZILIAN SAVANNA CLASSIFICATION: A CASE STUDY IN BRASÍLIA NATIONAL PARK

Abstract

Distinguishing Brazilian savanna physiognomies is an essential task to better evaluate carbon storage and potential emissions of greenhouse gases. In this study, we propose to evaluate the potential of texture features to improve the discrimination among fi ve physiognomies in the Brazilian savanna: Open Grasslands, Shrubby Grassland, Shrubby Savanna, Savanna Woodland and Gallery Forest. Texture features extracted from RapidEye images and also from Spectral Linear Mixture Model components and Vegetation Index were evaluated. Results showed that texture features based on GLCM can reduce misclassifi cation for Open Grasslands, Shrubby Grasslands and Shrubby Savanna classes.