Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning

The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees - thus validating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance - achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality.