## 1 Introduction  
High-value tree species are economically important [1; 2] as they are used in various industries, support local economies, and are the basis of valuable products for domestic and international markets. Moreover, they play a central role in ecosystem functioning, by supporting biodiversity (such as through the provision of habitat and food resources) and providing essential ecosystem services, and in ecological interactions that help maintain ecological balance and sustainability [3]. The conservation and sustainable management of high-value tree species are therefore essential not only from an economic perspective, but also for the health and resilience of ecosystems and the well-being of natural and human communities. Meanwhile, as these high-value timber species with economic and ecological importance are usually distributed at very low densities [4], acquiring accurate information on the distribution of these trees within a forest is critical to forest management practices. The traditional method of acquiring this information is based on individual tree species and thus time-consuming and laborious [5; 6], as well as difficult to apply on complex uneven-mixed forests [7]. The recent development of remote sensing techniques has provided an effective and efficient means of obtaining highly accurate forest information [6]. In particular, high- or very-high-resolution remote sensing imagery data [8; 9], high-spatial-resolution airborne multispectral or hyperspectral data, and high-point-density Light Detection and Ranging (LiDAR) point cloud data [10; 11; 12], can provide precise information on tree species. However, due to the high data acquisition cost, the applicability of remote sensing for individual tree species detection in the field is limited, particularly over large forest areas [8; 13]. Over the past decade, the use of unmanned aerial vehicles (UAVs) to assess forest regeneration, monitor forest health and identify individual trees has grown rapidly due to the advantages of low cost, high efficiency, and high precision [14]. Furthermore, recent technological advances integrating UAV imagery and machine learning algorithms have provided an efficient method for tree species mapping. Specifically, accurate results in predicting individual tree distribution have been obtained using Convolutional Neural Network (CNN)-based deep learning algorithms, given their ability to extract deeper features from the UAV imagery [13; 15]. Among the many deep learning models, U-Net has demonstrated state-of-the-art performance in multiple image segmentation tasks in the forestry sector, including forest change detection, post-forest-fire monitoring, forest type classification, and the mapping of tree species distribution [16; 17; 18; 19; 20]. However, the U-Net architecture, like many deep neural networks, may suffer from the vanishing gradient problem, which can make the models less capable of capturing fine details and distinguishing borders between different objects or classes. Meanwhile, Residual Network (ResNet) is known for its ability to train very deep neural networks. It introduces residual connections that allow gradients to flow more easily during training, addressing the vanishing gradient problem [21]. The combination of the semantic segmentation network U-Net and the feature extraction network ResNet has therefore garnered considerable research interest [22]. This combined approach has been applied in the areas of single-target applications, such as sea-land segmentation, the extraction of buildings, individual plant detection, and urban land classification [23; 24; 25; 26]. Furthermore, these two models' combination allows for the preservation of both global and local spatial contexts, which are critical for recognizing intricate patterns and details in tree species identification. Thus, the use of this combined technique is a robust choice for tree species identification tasks [13; 22].