VidFin: A video-based monocular UAV pipeline for automatic forest inventory in diverse scenes

Forestry inventory, the task of systematically measuring each tree within a given area, constitutes the basis for sustainable forest management and addresses a significant need in modern digital forestry. To automate the inventory process, existing methods mainly rely on LiDAR systems to scan forest plots. However, these LiDAR scanning sensors are often expensive and not readily accessible. To address these limitations, we propose VidFin, a novel video-based forest inventory system architecture that simultaneously generates DBH (Diameter at Breast Height) estimation, tree tracking, and tree positions from a consumer-level, monocular drone video. Within the system, each component is tightly interconnected; for example, DBH measurements depend on accurate distance estimation, which is jointly trained with UAV trajectory calculation, while tree positions are optimized using both UAV trajectory data and distance measurements. This temporal association in the video data and the interconnection between components results in improved inventory performance across diverse forestry scenes. To validate the system's performance, we developed a benchmark encompassing various forestry scenes on 13 flight paths. The experimental results demonstrate the superiority of our approach, achieving an average measurement error of 2.26 cm in DBH and a recall of 98.3%. • We propose VidFin: a video-based pipeline for automated forest inventory. • From a single monocular UAV flight, it simultaneously estimates DBH and tree position for each stem. • Joint optimization of the drone's trajectory and distance boosts DBH accuracy. • Validation on 13 diverse forest flights demonstrates strong cross-site generalization. • Our method achieves 2.26 cm DBH error and 98.3% tree recall.