Forests are one of the most important components to balance and regulate the terrestrial ecosystem on the Earth in protecting the environment. Accurate forest above-ground biomass (AGB) assessment is vital for sustainable forest management to recognize climate change and deforestation for mitigation processes. In this study, Sentinel 2 remote sensing image has been used to calculate the fraction of vegetation cover (FVC) in order to accurately estimate the forest above-ground biomass of Tundi reserved forest in the Dhanbad district located in the Jharkhand state, India. The FVC is calculated in four steps: first, vegetation index image generation; second, vegetation index image rescaled between 0 to 1; third, the ratio of vegetated and non-vegetated areas was calculated with respect to the total image area, and finally, FVC image is generated. In this paper, three vegetation indices have been calculated from the Sentinel 2 image, namely: normalized difference vegetation index (NDVI), normalized difference index 45 (NDI45), and inverted red-edge chlorophyll index (IRECI). Then, the FVC images were generated from the above vegetation indices individually. The ground FVC values were estimated from 22 different locations from the study area. Finally, the image based FVC estimates were compared with the ground estimated FVC. The results show that the IRECI based FVC provided the best approximation to the ground FVC among the different vegetation indices tested.