Large-scale delineation of individual trees from remote sensing imagery is crucial to the advancement of ecological research, particularly as climate change and other environmental factors rapidly transform forest landscapes across the world. The prevailing paradigm for RGB-based tree segmentation is to train specialized machine learning models with labeled tree datasets. While learning-based approaches, if sufficiently accurate, are more attractive than manual data collection, existing models are still trained using data that is difficult to scale up. In this paper, we investigate the efficacy of using a state-of-the-art image segmentation model, Segment Anything Model 2 (SAM2), in a zero-shot manner for individual tree detection and segmentation. We evaluate a pretrained SAM2 model on two tasks in this domain: (1) zero-shot segmentation and (2) zero-shot transfer by using predictions from an existing tree detection model as prompts. Our results suggest that SAM2 not only has impressive generalization capabilities, but also can form a natural synergy with specialized methods trained on in-domain labeled data. We find that directly applying large pretrained models to problems in remote sensing is a promising avenue for future progress.