DISCRIMINATION AND CHARACTERIZATION OF PROMINENT DESERTIC VEGETATIONS USING HYPERSPECTRAL IMAGING DATA

Abstract. Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. In this study, a ground-based hyperspectral imaging data (AISA VNIR 400–960 nm, Spectral Resolution @ 2.5 nm) was used for spectral vegetation discrimination and characterization of natural desertic tree species. This study assessed the utility of hyperspectral imagery of 240 narrow bands in discrimination and classification of desert tree species in Jodhpur region using ENVI software. Vegetation indices derived from hyperspectral images used in the Analysis for tree species classification discrimination study. Prominent occurring two desertic tree species, viz., Neem and Babul in Jodhpur region could be effectively discriminated. Study demonstrated the potential utility of narrow spectral bands of Hyperspectral Imaging data in discriminating vegetation species in a desertic terrain.