The growth of residential and commercial areas threatens vegetation and ecosystems. Thus, an urgent urban management
issue involves determining the state and the quantity of urban tree species to protect the environment, as well as controlling
their growth and decline. This study focused on the detection of urban tree species by considering three types of tree
species, namely, Mesua ferrea L., Samanea saman, and Casuarina sumatrana. New rule sets were developed to detect these
three species. In this regard, two pixel-based classification methods were applied and compared; namely, the method of
maximum likelihood classification and support vector machines. These methods were then compared with object-based
image analysis (OBIA) classification. OBIA was used to develop rule sets by extracting spatial, spectral, textural and color
attributes, among others. Finally, the new rule sets were implemented into WorldView-2 imagery. The results indicated
that the OBIA based on the rule sets displayed a significant potential to detect different tree species with high accuracy.