In this research, hyperspectral and multispectral images were utilized for vegetation studies in the greenbelt corridor near Denton. EO-1 Hyperion was the hyperspectral image and Landsat Thematic Mapper (TM) was the multispectral image used for this research. In the first part of the research, both the images were classified for land cover mapping (after necessary atmospheric correction and geometric registration) using supervised classification method with maximum likelihood algorithm and accuracy of the classification was also assessed for comparison. Hyperspectral image was preprocessed for classification through principal component analysis (PCA), segmented principal component analysis and minimum noise fraction (MNF) transform. Three different images were achieved after these pre-processing of the hyperspectral image. Therefore, a total of four images were classified and assessed the accuracy. In the second part, a more precise and improved land cover study was done on hyperspectral image using linear spectral unmixing method. Finally, several vegetation constituents like chlorophyll a, chlorophyll b, caroteoids were distinguished from the hyperspectral image using feature-oriented principal component analysis (FOPCA) method and which component dominates which type of land cover particularly vegetation were correlated.