Measuring tree stem diameters and straightness with depth-image computer vision

Abstract

Current techniques of forest inventory rely on manual measurements and are slow and labor intensive. Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor costs. We developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem straightness. Time use, mean absolute error (MAE), and root mean squared error (RMSE) metrics were used to compare the system against manual methods, and to compare the system against itself (reproducibility). Depth-derived diameter measurements of the stems at the heights of 0.3, 1.4, and 2.7 m achieved RMSE of 1.7, 1.5, and 2.7 cm, respectively. The ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained foresters. The ForSense system was also consistent, achieving sub-centimeter diameter difference with subsequent measures and less than 4% difference in straightness value between runs. This method of forest inventory, which is based on depth-image computer vision, is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion (SFM) photogrammetry and ground-based LiDAR or terrestrial laser scanning (TLS).

Keywords

Forest inventory / Depth sensing / Computer vision / Tree diameter / Stem straightness / Trunk volume