Abstract: Aims: Investigating the characteristics of community composition and structure in urban vegetation is of vital importance in evaluating its growth status and ecological function. The quantitative characteristics of different tree species in a community are the basis for quantitative description of the composition and structure of vegetation communities. Traditional vegetation surveys require substantial human and material resources and are difficult to carry out on a large scale, while a single remote sensing data source and corresponding method find it hard to provide accurate information on the number of trees, crown size, and tree species simultaneously. To address these challenges, it is essential to explore the potential of unmanned aerial vehicle (UAV) remote sensing technology with multi-source datasets for accurately acquiring the composition and quantitative characteristics of urban vegetation communities. Methods: The study area is located in Lushan, a famous tourist city with high vegetation density and complex terrain. We employed high-resolution RGB imagery, LiDAR point cloud, and their combination to conduct individual tree detection and species classification, and then compared the performance of different remote sensing data sources and corresponding methods in extracting quantitative characteristics of tree species in the urban vegetation community. Results: The results indicated: (1) The multi-source datasets yielded the optimal results for individual tree segmentation and species classification. Compared to using imagery or LiDAR data separately, the F-score for individual tree segmentation increased by 0.116 and 0.102 respectively, and the overall accuracy of tree species classification improved by 12.1% and 23.1%; (2) The combination of multi-source data extracted the relative quantitative characteristics of tree species within communities more accurately, with errors for the relative density and relative coverage of all tree species categories being within 2.3% and 4.8% respectively. In contrast, methods based on a single data source obviously overestimated or underestimated the relative quantities of specific tree species. Conclusion: The research demonstrated that the method combining multi-source data improved the accuracy of extracting the composition and quantitative characteristics of urban vegetation communities by simultaneously optimizing the processes of individual tree detection and species classification. And it offered theoretical support and a methodological framework for subsequent UAV remote sensing monitoring to the structure of urban vegetation.

Key words: Urban vegetation, tree species composition and quantitative characteristics, UAV remote sensing, high-resolution imagery, LiDAR