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 (<0.001). Narrow wavebands of AVIRIS-NG (561 to 730 nm) were critically utilized to assess variations in response due to stress in YI that exhibited a linear negative correlation with leaf chlorophyll concentration (R² = 0.515, P-value < 0.001). With a very high spatial and spectral resolution of AVIRIS-NG, an approximation of second derivative YI has less atmospheric effects which many broadband indices are bound to. Spectra plotted against different chlorophyll concentrations in Tectona grandis, exhibited a wide shift due to chlorophyll variation (10.7 μg cm⁻² to 32.5 μg cm⁻²) in the central part of Sholayar reserve area. Spectral measures like spectral angles and spectral distances measured within the reference and pixel spectra were well correlated with chlorophyll (R² = 0.515 and R² = 0.350, respectively), indicating their suitability for studying chlorophyll deficiency in vegetation. Strong sensitivity of pigments captured in different regions of the spectrum extends the application of AVIRIS-NG data, especially in the heterogeneous and complex forested landscape.