The functional activity of a tree is affected by various biotic and abiotic factors. The vitality and health status of a tree also affects the growth. Recent remote sensing technologies provide powerful means for monitoring forest health The aim of this study is to discriminate Tectona grandis L. healthy trees from non-healthy or infected trees using the Spectral Angle Mapper (SAM) algorithm. The present study site was located in a Southern Tropical Dry Deciduous Forests, of Gujarat, western India. The forest was dominated by Tectona grandis L. The healthy plots and the unhealthy plots of T.grandis were chosen for the present research. Vitality of T. grandis was understood after detailed study on damage assessment in 45 different plots distributed in the study area. A mask for forest area from non-forest area was applied to extract forest area from the data. Pure endmembers of the masked dataset for healthy and non-healthy or infected tree were extracted. By utilizing the derived pure endmembers, spectral angle mapping was applied to differentiate between healthy and non-healthy or infected trees in the image.. The results show that SAM of Sentinel-2 data can provide T.grandis maps that compare favorably with ground truth. Suggesting that there is a great potential of discrimination of T.grandis healthy trees from the non-healthy or infected using Sentinel-2 data