SELECTION OF OPTIMAL HYPERSPECTRAL VEGETATION INDICES FOR ESTIMATING CHLOROPHYLL CONTENT OF SOME PLANT SPECIES

Abstract

Evolution in hyperspectral remote sensing can provide more exact information on structural and biochemical properties of plant species. 1 Most of the work on hyperspectral remote sensing of biophysical and biochemical prosperities has been achieved through the evolution of new hyperspectral indices. 2, 3, 4 Spectral indices are mathematical processing of spectral reflectance to promote vegetation signal. 5, 6 Vegetation indices might supply new capabilities for discriminate plant species or communities that differ in vegetation structure. 7 NDVI (normalized difference vegetation index) calculation is based on the difference in canopy reflectance at red and near-infrared wavelengths. 8, 9 NDVI has been used to differentiate plant species consisting of structurally featured formations, eg, shrubland and forest (Table 1) 10, 11 or phenological featured formations. 12 New hyperspectral indices that supply correlation with the biochemical properties of canopies have been developed. Several studies show that narrow band vegetation indices involving visible reflectance near 550 and 700 nm can estimate leaf chlorophyll content such as chlorophyll a and b13 and carotenoids. 14, 15 These pigments play important roles within the process of photosynthesis, and their concentrations can depend on some factors such as phenology, the degree of canopy development and type of environmental stress. 16 Spectral vegetation indices constitute a simple and restful approach to evolve information from remotely sensed data, due to their facility of use, which facilitates the processing and analysis of huge amounts of data obtained by satellite platforms.