The unmanned aerial vehicle (UAV) plays an increasingly important role in monitoring and managing islands recently for their high feasibility and the miniaturization of sensors, which provide new possibilities for accurate island green cover mapping. We developed a framework that integrates UAV-acquired high-spatial resolution multispectral image and LiDAR data for effective object-based green cover mapping of Donkey Island in the Yellow Sea, China. LiDAR-derived structural and intensity information were combined with multispectral-derived spectral information for obtaining green cover objects. Five kinds of feature types [i.e., spectral, texture, height, intensity, and geometry features (GFs)] were calculated based on each object for green cover classification. Meanwhile, a multiple classifier system was adopted to improve the classification accuracy. The results indicate that the accuracy of green cover mapping could be significantly improved by the combination of multiple feature types. The inclusion of height and intensity features (IFs) can increase the overall classification accuracy by 7% and 5%, respectively, but the statistical significant differences are not found between these two feature types. The best green cover map is generated via a feature group obtained by the sequential backward selection with random forest method, reaching an overall accuracy of 88.5% and overall disagreement of 18.5%. Among the three major green cover classes, the accuracy of shrub class mapping improves the most when compared to classification using individual data, followed by tree and grass. Analysis of feature importance implies that spectral, height, and IFs are more beneficial to green cover mapping compared to texture and GFs. Furthermore, integrating multispectral and LiDAR data can provide more reliable green cover distribution maps and reduce the classification uncertainties.