Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

The use of drone-borne imagery for tree recognition holds high potential in forestry and ecological studies. Accurate species identification and crown delineation are essential for tasks such as species mapping and ecological assessments. In this study, we compared the results of tree crown recognition across three neural networks using high-resolution optical imagery captured by an affordable drone with an RGB camera. The tasks included the detection of two evergreen coniferous tree species using the YOLOv8 neural network, the semantic segmentation of tree crowns using the U-Net neural network, and the instance segmentation of individual tree crowns using the Mask R-CNN neural network. The evaluation highlighted the strengths and limitations of each method. YOLOv8 demonstrated effective multiple-object detection (F1-score-0.990, overall accuracy (OA)-0.981), enabling detailed analysis of species distribution. U-Net achieved less accurate pixel-level segmentation for both species (F1-score-0.981, OA-0.963). Mask R-CNN provided precise instance-level segmentation, but with lower accuracy (F1-score-0.902, OA-0.822). The choice of a tree crown recognition method should align with the specific research goals. Although YOLOv8 and U-Net are suitable for mapping and species distribution assessments, Mask R-CNN offers more detailed information regarding individual tree crowns. Researchers should carefully consider their objectives and the required level of accuracy when selecting a recognition method. Solving practical problems related to tree recognition requires a multi-step process involving collaboration among experts with diverse skills and experiences, adopting a biology- and landscape-oriented approach when applying remote sensing methods to enhance recognition results. We recommend capturing images in cloudy weather to increase species recognition accuracy. Additionally, it is advisable to consider phenological features when selecting optimal seasons, such as early spring or late autumn, for distinguishing evergreen conifers in boreal or temperate zones.

Automated summary

This study compares three neural networks for tree crown recognition using drone-borne imagery and highlights the strengths and limitations of each method. The results provide important insights for selecting appropriate tree recognition methods.