Assessment of Individual Tree Crown Detection Based on Dual-Seasonal RGB Images Captured from an Unmanned Aerial Vehicle

Unmanned aerial vehicle (UAV)-captured RGB imagery, with high spatial resolution and ease of acquisition, is increasingly applied to individual tree crown detection (ITCD). However, ITCD in dense subtropical forests remains challenging due to overlapping crowns, variable crown size, and similar spectral responses between neighbouring crowns. This paper investigates to what extent the ITCD accuracy can be improved by using dual-seasonal UAV-captured RGB imagery in different subtropical forest types: urban broadleaved, planted coniferous, and mixed coniferous–broadleaved forests. A modified YOLOv8 model was employed to fuse the features extracted from dual-seasonal images and perform the ITCD task. Results show that dual-seasonal imagery consistently outperformed single-seasonal datasets, with the greatest improvement in mixed forests, where the F1 score range increased from 56.3%–60.7% (single-seasonal datasets) to 69.1%–74.5% (dual-seasonal datasets) and the AP value range increased from 57.2%–61.5% to 70.1%–72.8%. Furthermore, performance fluctuations were smaller for dual-seasonal datasets than for single-seasonal datasets. Finally, our experiments demonstrate that the modified YOLOv8 model, which fuses features extracted from dual-seasonal images within a dual-branch module, outperformed both the original YOLOv8 model with channel-wise stacked dual-seasonal inputs and the Faster R-CNN model with a dual-branch module. The experimental results confirm the advantages of using dual-seasonal imagery for ITCD, as well as the critical role of model feature extraction and fusion strategies in enhancing ITCD accuracy.