ABSTRACT

Personal laser scanning (PLS) has shown great potential in diameter of breast height (DBH) estimation. Compared with the current laser scanning technique, PLS is hardly restricted by occlusion and trafficability, and produces omnidirectional point cloud data. However, the DBH estimation accuracy using PLS data was not higher in the previous research. One of the leading sources of bias is overlapping and inaccurately co-registered point cloud fragments. Points of these fragments can be far from the stem surface, which leads to a large bias in DBH estimation. In this letter, a novel DBH estimation method using PLS data, the stem surface node method (SSN) is proposed. To reduce the impacts of the inaccurately co-registered points far from the surface, SSN attempts to use a number of stem surface nodes in polar coordinates instead of the whole stem point cloud to estimate DBH. Six plots with 247 stems under three different working conditions were selected to evaluate the method. Compared with the DBH estimation method using direct circle fitting, SSN achieved a total error reduction of 62.64% and 38.35% for bias and root mean squared error, respectively. These findings confirm that SSN is generally reliable and adaptive for different kinds of working conditions.

摘要

个人激光扫描（PLS）在估计乳房高度（DBH）的直径方面显示出巨大潜力。与目前的激光扫描技术相比，PLS几乎不受遮挡和交通限制，并且可以产生全方位的点云数据。但是，在以前的研究中，使用PLS数据进行DBH估算的准确性并不高。偏差的主要来源之一是重叠且不准确地共注册点云片段。这些碎片的点可能离茎表面较远，这导致DBH估算存在较大偏差。在这封信中，提出了一种新的使用PLS数据的DBH估计方法，即茎表面节点方法（SSN）。为减少远离地面的不正确注册点的影响，SSN尝试使用极坐标中的许多茎表面节点而不是整个茎点云来估计DBH。选择了在三种不同工作条件下具有247个茎的六个样地来评估该方法。与使用直接圆拟合的DBH估计方法相比，SSN的偏差和均方根误差总误差分别降低了62.64％和38.35％。这些发现证实，SSN通常是可靠的，并且可以适应不同的工作条件。