Instance segmentation of individual tree crowns in aerial imagery is a critical task for forest management, carbon storage estimation, and biodiversity modeling. However, achieving effective segmentation faces significant challenges including dense canopy overlapping, diverse crown characteristics, and varying environmental conditions across different geographical regions. This paper proposes YOLOv8-UT, a unified training approach for cross-environment tree crown instance segmentation that enhances model generalization across rural and urban environments. YOLOv8-UT employs a two-stage training strategy that leverages unified pre-training on combined datasets followed by environment-specific fine-tuning to learn robust cross-environment features. Moreover, YOLOv8-UT incorporates the Large Kernel Attention mechanism to enhance feature representation for complex tree crown identification. Comprehensive experiments on aerial imagery from the Greater Wellington region demonstrate that YOLOv8-UT outperforms other recent peer competitors, achieving Box AP of 39.7 and Mask AP of 34.4 on the rural dataset, and Box AP of 48.2 and Mask AP of 40.5 on the urban dataset.