Analysis of Parameters for the Accurate and Fast Estimation of Tree Diameter at Breast Height Based on Simulated Point Cloud

Abstract

Terrestrial laser scanning (TLS) is a high-potential technology in forest surveys. Estimating the diameters at breast height (DBH) accurately and quickly has been considered a key step in estimating forest structural parameters by using TLS technology. However, the accuracy and speed of DBH estimation are affected by many factors, which are classified into three groups in this study. We adopt an additive error model and propose a simple and common simulation method to evaluate the impacts of three groups of parameters, which include the range error, angular errors in the vertical and horizontal directions, angular step width, trunk distance, slice thickness, and real DBH. The parameters were evaluated statistically by using many simulated point cloud datasets that were under strict control. Two typical circle fitting methods were used to estimate DBH, and their accuracy and speed were compared. The results showed that the range error and the angular error in horizontal direction played major roles in the accuracy of DBH estimation, the angular step widths had a slight effect in the case of high range accuracy, the distance showed no relationship with the accuracy of the DBH estimation, increasing the scanning angular width was relatively beneficial to the DBH estimation, and the algebraic circle fitting method was relatively fast while performing DBH estimation, as is the geometrical method, in the case of high range accuracy. Possible methods that could help to obtain accurate and fast DBH estimation results were proposed and discussed to optimize the design of forest inventory experiments.

The Influence of Cross-Section Thickness on Diameter at Breast Height Estimation from Point Cloud

“…The impact of two circle-fitting methods, range and angular errors, angular steps, DBH, distance to scanner, and cross-section thickness on DBH estimations was simulated in the work of Wang, P. et al [19]. They used point cloud cross-sections (10, 15, 20, and 25 cm thick) to analyze the influence of cross-section thickness on DBH estimation and expressed DBH estimation accuracy by the relative error of DBH estimations.…”

“…In the numerical experiments, increase in cross-section thickness resulted in better accuracy for wider angular steps of laser scanning, which is consistent with our findings. Wang, P. et al [19] hypothesized that the increase in the relative error of DBH estimation is related to the larger number of points in cross-sections.…”

“…J. Geo-Inf. 2020, 9, 495 2 of 29 thickness has been partly investigated in conjunction with the influence of TLS settings and point clouding processing on the accuracy of derived tree characteristics [8,19]. The optimization of cross-section thickness for DBH estimation is vital for data volume reduction, effective point cloud processing, and appropriate setting of scanning parameters.…”

Dynamic Analysis of Street Trees in Vehicle-mounted LiDAR Point Clouds

“…Ellipse fitting methods include adaptive ellipse fitting [8] and robust least squares fitting [9]. Circle fitting methods encompass image detection-based methods [10][11][12][13] (such as Hough transform, camera point clouds, wireframe models, and region segmentation), as well as algebraic and geometric methods [14][15][16][17][18][19][20][21][22](such as the least squares quadratic screening method, minimum variance iterative algorithm, and ringneighbor point method). Additionally, model-based methods include the tree height-DBH model and conical geometric model [23][24][25].…”