 This study proposes a hybrid approach combining random forest and texture analysis to improve the accuracy of urban vegetation mapping from unmanned aerial vehicles, UAV. The study found that random forest outperforms traditional maximum likelihood classifier and performs similarly to object-based image analysis. Additionally, the inclusion of texture features improves classification accuracy significantly. Furthermore, the study also demonstrates that the use of UAV provides an efficient and ideal platform for urban vegetation mapping. This article was authored by Quan Long Feng, Jian Tao Liu, and Jian Wagong.