The subalpine coniferous forest in west Sichuan is located in southwest China, which is affected by cloudy, rainy, and foggy conditions. Thus, conducting vegetation classification in the area by using satellite images is difficult.(Objective)Therefore, this work selects Wanglang Nature Reserve, which is a typical area of subalpine coniferous forest in western Sichuan, as the study area. A multi-rotor UAV is used to acquire high-resolution RGB images of the northern part of the study area, and it is combined with a convolutional neural network model for vegetation classification.(Method)This study selects the semantic segmentation method (U-Net) for classification, constructs vegetation classification models based on UAV images of different spatial resolutions and sample sets under different tile sizes, and establishes a forest fingerprint library to further explore the potential of convolutional neural networks on UAV remote sensing images.(Result)(1) The combination of UAV visible images and convolutional neural network model for classification can obtain classification results of high accuracy, which reached the optimum at a spatial resolution of 5 cm and a size of 256×256. The overall accuracy was 93.21%, and the kappa coefficient was 0.90. (2) The increase in ultrahigh spatial resolution had limited improvement on the model accuracy. When the spatial resolution was increased from 10 cm to 5 cm, the overall accuracy of the model improved by 0.02 and the Kappa coefficient improved by 0.03, and the classification accuracy of the model did not improve significantly. (3) Choosing the appropriate size can improve the classification accuracy of the model. Under the spatial resolution of 5 cm, the overall accuracy of the model with the size of 128×128 was 82.30% and the kappa coefficient was 0.76, and the overall accuracy of the model with the size of 256×256 was 93.21% and the kappa coefficient was 0.90. (4) For the vegetation types that were underrepresented in the region, the influence of spatial resolution and tile size was much higher than that of the dominant tree species, especially the influence of spatial resolution was the highest. The producer and user accuracies for deciduous shrubs at a spatial resolution of 20 cm were below 70%.(Conclusion)This study shows that vegetation classification of subalpine coniferous forests in western Sichuan using UAV high-resolution RGB images combined with convolutional neural networks can achieve high-precision classification results. The effects of UAV spatial resolution and tile sizes on the accuracy of convolutional neural network models are explored, which further details the potential of convolutional neural networks on UAV high-resolution RGB images to provide an automatic and accurate research method for vegetation classification in this region.

UAV RGB imagery;Convolutional Neural Network(CNN);subalpine coniferous forest in western Sichuan;vegetation classification;tile size;spatial resolution