Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Leaf area index LAI is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager OLI sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices SVIs and six Landsat-8 surface reflectance ρ variables using univariate correlation analysis. Results showed that the red ρred, near-infrared ρNIR, shortwave infrared ρSWIR1, ρSWIR2 reflectance bands R2 > 0.6, and all SVIs R2 > 0.7 except simple ratio SR have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant high R2, low root mean square error RMSE, and p-level R2 = 0.83, RMSE = 0.78 using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer MODIS global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution 30 m as well as coarse resolution 1 km for regional climate models.