Aboveground forest biomass and carbon estimation at landscape scale is crucial for implementation of REDD+ programmes. This study aims to upscale the forest carbon estimates using GeoEye-1 image and small footprint lidar data from small areas to a landscape level using RapidEye image. Species stratification was carried out based on the spectral separability curve of GeoEye-1 image, and comparison of mean intensity and mean plot height of the trees from lidar data. GeoEye-1 image and lidar data were segmented using region growing approach to delineate individual tree crowns; and the segmented crowns (CPA) of tree were further used to establish a relationship with field measured carbon and total trees’ height. Carbon stock measured from field, individual tree crown (ITC) segmentation approach and area-based approach (ABA) was compared at plot level using one-way ANOVA and post hoc Tukey comparison test. ITC-based carbon estimates was used to establish a relationship with spectral reflectance of RapidEye image variables (NDVI, RedEdge NDVI, PC1, single band of RedEdge, and NIR) to upscale the carbon at landscape level. One-way ANOVA resulted in a highly significant difference (p-value < 0.005) between the mean plot height and lidar intensity to stratify Shorea robusta and Other species successfully. ITC carbon stock estimation models of two major tree species explained about 88% and 79% of the variances, respectively, at 95% confidence level. The ABA estimated carbon was highly correlated (R2 = 0.83, RMSE = 20.04) to field measured carbon with higher accuracy than the ITC estimated carbon. A weak relationship was observed between the carbon stock and the RapidEye image variables. However, upscaling of carbon estimates from ABA is likely to improve the relationship of the RapidEye variables rather than upscaling the carbon estimates from ITC approach.