Currently, China's forestry development still faces challenges such as uneven distribution of resources and overall low quality, which hinder the transformation of forestry development from focusing on quantity growth to quality improvement. In the face of major strategic needs such as national ecological security, timber security, food and oil security, and the “dual carbon” goals, it is urgently necessary to achieve accurate monitoring of tree phenotype traits, thereby selecting excellent germplasm resources, shortening breeding cycles, improving tree resilience, and wood quality. Traditional tree phenotype monitoring methods suffer from limited samples, low efficiency, poor accuracy, and sometimes even destructive processes, which constrain the efficiency and quality of tree breeding. The lack of efficient and accurate high-throughput phenotype information acquisition methods and analysis techniques has become one of the main bottlenecks hindering genetic analysis and fine breeding of trees. Modern unmanned aerial vehicle (UAV) remote sensing technology has the capability to intelligently, rapidly, and accurately capture dynamic changes in tree phenotype traits at multiple scales, which is of great significance for breaking through the bottleneck of tree phenotype monitoring mentioned above. Leveraging high-resolution passive and active remote sensing data obtained by UAV and intelligent analysis algorithms such as deep learning, machine learning, and data mining, it is possible to accurately extract multi-scale tree phenotype traits, providing a quantitative data guarantee for revealing the response relationships between “genes-phenotypes-environment”. This, in turn, facilitates further achievements in the selection of excellent germplasm resources, precision cultivation, identification and quantification of stresses, and resistance breeding. This paper first introduces the current application status of UAV remote sensing sensors in tree phenotype monitoring. Then, it focuses on the application progress of UAV remote sensing technology in extracting tree morphological structure traits, physiological functional traits, and biochemical component contents. Finally, it outlines the future development trends of UAV-based tree phenotype monitoring remote sensing technology from four aspects: multi-temporal and periodic dynamic monitoring of tree phenotype traits, integration of multi-source phenotype data obtained by UAVs, fusion of remote sensing data from different platforms of space-air-ground for collaborative monitoring and multi-omics analysis of tree phenotypes.