Individual Tree Species Classification Using Pseudo Tree Crown (PTC) on Coniferous Forests

Abstract

Highlights: What are the main findings? Expanded the study of the pseudo tree crown (PTC), a pseudo-3D (2.5D) data representation derived from nadir UAV RGB imagery for individual tree classification and vertical structure estimation. Demonstrated that PTC consistently improves individual conifer species classification accuracy across multiple ML/DL models compared with standard RGB images. What is the implication of the main finding? PTC provides a cost-effective and scalable alternative to LiDAR, enabling the accurate classification of tree species and the extraction of pseudo-3D crown structure using only single nadir images. This approach strengthens forest monitoring, biodiversity assessment, and climate resilience planning, supporting sustainable forestry and future integration with full 3D crown models. Coniferous forests in Canada play a vital role in carbon sequestration, wildlife conservation, climate change mitigation, and long-term sustainability. Traditional methods for classifying and segmenting coniferous trees have primarily relied on the direct use of spectral or LiDAR-based data. In 2024, we introduced a novel data representation method, pseudo tree crown (PTC), which provides a pseudo-3D pixel-value view that enhances the informational richness of images and significantly improves classification performance. While our original implementation was successfully tested on urban and deciduous trees, this study extends the application of PTC to Canadian conifer species, including jack pine, Douglas fir, spruce, and aspen. We address key challenges such as snow-covered backgrounds and evaluate the impact of training dataset size on classification results. Classification was performed using Random Forest, PyTorch (ResNet50), and YOLO versions v10, v11, and v12. The results demonstrate that PTC can substantially improve individual tree classification accuracy by up to 13%, reaching the high 90% range.