Estimating tree diameter at breast height (DBH) from UAV data: A comparison of oblique–Vertical imagery fusion and allometric modeling

Accurate estimation of tree diameter at breast height (DBH) is essential for forest inventory, biomass assessment, and ecological monitoring. Unmanned aerial vehicles (UAVs) have emerged as powerful tools for remote DBH estimation, yet challenges remain in accurately capturing stem dimensions from aerial perspectives. This study compares two approaches for estimating the DBH of pine trees: (I) a data fusion method that combines vertical and oblique UAV imagery to generate high-density point clouds, and (II) a Random Forest-based allometric modeling approach based on metrics derived from vertical UAV data. The first approach evaluated three fusion configurations combining vertical (90°) and oblique (30°, 60°) imagery: S1 (30°/90°), S2 (60°/90°), and S3 (30°/60°/90°). The second approach (S4) employed a Random Forest regression model using features derived from vertical UAV data, including tree height and crown size metrics to estimate DBH. Results demonstrate that the geometric approach using all three viewing angles (S3) achieved the highest accuracy (R 2 = 0.985, RMSE = 2.47 cm), followed closely by S2 (R 2 = 0.949), indicating the effectiveness of multi-angle image integration in improving DBH prediction. S4, while less accurate (R 2 = 0.824), provided moderately reliable estimates, offering a simpler and more scalable alternative in operational settings. In contrast, S1 significantly overestimated DBH, especially for larger trees, with a high positive bias and the largest RMSE. When evaluated across DBH size classes, S3 consistently outperformed other methods, with strong agreement across small, medium, and large trees. These findings highlight the value of oblique–vertical image fusion in enhancing DBH estimation accuracy, particularly when multiple viewing angles are used. While this study focuses on open-canopy pine stands, future research should assess these methods in denser forests and explore deep learning algorithms for DBH estimation from complex point clouds. • Multi-angle UAV imagery improves DBH estimation accuracy in pine forests. • Fusion of vertical and oblique images (S3) achieved highest accuracy (R 2 = 0.985). • Random Forest (S4) offers scalable DBH estimates, though less precise than S3. • S3 consistently outperforms other methods across small, medium, and large trees.