Accurate mapping of tree species is fundamental for sustainable forest management, biodiversity monitoring, and ecological research. Recent advances in Uncrewed Aerial Vehicle (UAV) photogrammetry provide rich spatial and spectral information at unprecedented detail (0.02 m Ground Sampling Distance). Meanwhile, the development of large-scale foundational models, pre-trained on expansive, multi-modal remote sensing datasets, offers highly transferable representations critical for advancing geoscience applications. This paper investigates the underexplored integration of foundation model pre-training with multi-temporal high-resolution UAV imagery. We introduce a novel two-phase framework: first, leveraging a Vision Transformer (ViT)-based foundation model (FoMo-Net) pre-trained on the multi-scale FoMo-Bench benchmark to initialize a DeepLabv3+ architecture; and second, fusing multi-temporal UAV data (May and September RGB) through change composites and pseudo-labeling to exploit species-specific phenology. The proposed approach, tested on the multi-class Québec Trees Dataset, yields an Overall Accuracy (OA) of 78.21%, demonstrating that foundation model initialization significantly boosts feature generalization, while multi-temporal cues are essential for disambiguating closely related species.