Forest carbon estimation currently largely relies on remote sensing techniques in combination with field measurement. High-resolution images, which are commonly utilized for carbon estimation, are not readily available, and their cost prohibits communities from reaping the benefits of maintaining their forest under the UN reducing emissions from deforestation and forest degradation program. Our study explores the combination of readily available and relatively cheaper unmanned aerial vehicle (UAV) (4-cm resolution) and multispectral Pleiades (50-cm resolution) images for species classification robustness in view for carbon estimation through object-based image analysis. The images are resampled and used to evaluate the effect of combining multispectral Pleiades image on the accuracies of segmenting UAV images for tree crown projection area (CPA) estimation and species classification. RGB images from a UAV platform are processed in a photogrametric software and combined with the near-infrared band of a Pleiades image to get a UAV-Pleiades image composite. The images are segmented using the ESP 2 tool and the segmentation accuracy compared using a paired t-test. The segmented tree crowns are classified using random trees (RT), support vector machines (SVM), and maximum likelihood (ML) classifiers, and the classification accuracies of the three classifiers are compared using the McNemar's chi-squared test. Our study demonstrates a 93.5% accuracy of segmenting UAV-Pleiades image composite, which is significantly higher than the 84.8% accuracy of segmenting UAV images (p < 0.05). Also an 84% classification accuracy of UAV-Pleiades image composite is significantly higher than the 54% classification accuracy of the UAV images (p < 0.05). Of the three classifiers used, the classification accuracies of SVM and RT are significantly higher (p < 0.05) than that of the ML classifier. Given the significantly high accuracies observed from this study for tree CPA extraction and tree species classification, carbon/above ground biomass modeling is possible with significantly high accuracy using the combination of multispectral Pleiades and UAV images.