A Multiview UAV Imagery-Based Method for Assessing Spruce Tree Health at the Individual Tree Level

Abstract. Assessing the health of individual spruce trees in forests is critical for early detection of bark beetle infestations and effective forest management. This study presents a novel methodology that leverages multi-view uncrewed aerial vehicle (UAV) imagery to improve tree health classification at the individual tree level. High-resolution images were collected over a spruce-dominated forest and processed to extract multiple perspectives of each tree crown. Compared to the typical case of using one orthophoto per tree, our process yielded on average 31 images per tree crown. Deep learning models, including VGG16 and a simple CNN, were trained to classify trees as healthy, infested, dead, or non-spruce. Results demonstrate that incorporating multi-view images increased classification accuracy, particularly for the challenging infested and non-spruce categories, compared to traditional orthophoto-based approaches. The best-performing model achieved an overall accuracy of 0.94 and a macro F1-score of 0.85, with notable improvements in detecting infested trees.