Enhancing image quality and adopting new image classification methods are two breakthrough points to improve the accuracy of tree species identification by remote sensing images. The research focuses on the identification of Chinese fir and Masson pine by the pre-trained model of VGG16 and unmanned aerial vehicle (UAV) visible images. The DJI Phantom 4RTK UAV with an FC6310R camera is used to collect color images of artificial pure forests of Chinese fir and Masson pine in Nanping and Sanming cities. Then, two datasets UAVTree2k and UAVTree20k are constructed through image preprocessing, annotation, cropping, and enhancement. Furthermore, three full connection layers and Sigmoid layer are trained by the UAVTree2K dataset and the pre-trained model of VGG16 on the ImageNet dataset to investigate the effects of the number of iterations, batch size, partition ratios of the training set and the test set on identification accuracy. The results show that when the number of iterations is 40, the batch size is 16, and the ratio between the training set and the test set is 6:4, the identification effect of the model is best, and the test accuracy reaches 98.63%. Meanwhile, the VGG16-based pre-trained model has a good feature learning ability for a small sample size.