Urban trees enhance the urban environment through various ecosystem services. Individual tree delineation and species classification provide information on the location, structure, and species of each tree from remote sensing datasets, offering valuable data support for efficient and refined urban greening management. However, existing individual tree delineation algorithms developed based on single-source remote sensing datasets struggle to address the complexity of urban green landscapes, such as conifer-broadleaf mixtures, tree-shrub complexes. Additionally, the relationship between classification accuracy and individual tree delineation quality remains unclear. This study integrates the Segment Anything Model (SAM) and Marker-Controlled Watershed Segmentation (MCWS), combining imagery and LiDAR features, to optimize individual tree delineation in complex urban landscapes. Species classification was then performed on crown datasets from different algorithms to investigate how classification accuracy responds to varying crown qualities. The results demonstrate that the proposed SAM-WS algorithm effectively enhances individual tree delineation accuracy, achieving the highest F1-Score of 0.75, with improvements of 0.20 and 0.27 over SAM and MCWS, respectively. The classification accuracy based on SAM-WS crowns was the highest among all algorithm-derived crown datasets, with an Overall Accuracy (OA) of 0.79 and a Kappa of 0.64. As the average F1-Score of crown delineation dropped from 1.00 to 0.48, the OA for classification decreased from 0.86 to 0.74, and Kappa from 0.77 to 0.38. Additionally, the classification accuracy of conifers and shrubs was more sensitive to the crown quality. This research offers new methodologies and insights into the application of remote sensing-based urban vegetation monitoring.