Chlorophyll deficiency (chlorosis) detection based on spectral shift and yellowness index using hyperspectral AVIRIS-NG data in Sholayar reserve forest, Kerala

Hyperspectral remote sensing is highly efficient in retrieving the leaf chlorophyll concentrations and its deficiency, which is manifested in the form of a spectral shift in reflectance. In the present study, the detection of chlorosis in vegetation was assessed through spectral measures and Yellowness Index (YI) utilizing hyperspectral Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data in Sholayar reserve forest, Kerala. Chlorophyll concentration was spatially derived based on regression analysis between the field-based leaf chlorophyll concentration and hyperspectral narrowband indices. Various indices like enhanced vegetation index (EVI), Red-edge normalised difference vegetation index (RNDVI), atmospherically resistant vegetation index (ARVI) and Vogelmann red-edge index (VRI) were found to be highly sensitive towards leaf chlorophyll concentrations and exhibit good correlations (R2 = 0.6374, R2 = 0.5493, R2 = 0.5711 and R2 = 0.5003, respectively) with significant P-value (