Tree stem measurement using 2D-projected raw mobile laser scanning data

Close-range remote sensing systems are driving a transition towards precision forestry, increasing the demand for accurate in situ tree-level measurements. The presented study investigates the potential of directly measuring tree stems from raw Mobile Laser Scanning (MLS) data using 2D projections and 2D Convolutional Neural Networks to improve both processing speed and accuracy. We present a comprehensive workflow, including instance segmentation on projected MLS data, which is evaluated using 48 manually labelled raw scans. The proposed method achieved a diameter at breast height estimation root mean square error of 2.0 cm, comparable with approaches based on simultaneous localization and mapping (SLAM). Diameter measurements at different stem heights were successfully obtained for 70 out of 84 explored trees, with a mean absolute error of 1.8 cm. These findings demonstrate that leveraging 2D projections of raw MLS data enables efficient, SLAM-free, and truly real-time measurement of tree stems, advancing decision-making in managed forests.