Volume is an important attribute used in many forest management decisions. Data from 83 fixed-area plots located in central New Brunswick, Canada, are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer’s (1967) volume equation. When density was included in the models ($\text{Volume}=f\left(\text{Diameter},\text{Height},\text{Density}\right))$ choice of diameter measure was more important than choice of height measure. When density was not included $\left(\text{Volume}=f\left(\text{Diameter},\text{Height}\right)\right)$, the opposite was true. For models with density included, moment-based estimators of stand diameter and height performed better than all other measures. For models without density, largest tree estimators of stand diameter and height performed better than other measures. The overall best equation used quadratic mean diameter, Lorey’s height, and density (root mean square error = 5.26 m³ ha⁻¹; 1.9 % relative error). The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha (root mean square error = 32.08 m³ ha⁻¹; 11.8 % relative error). The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.

体积是许多森林管理决策中使用的重要属性。使用来自加拿大新不伦瑞克省中部 83 个固定面积地块的数据，利用 Honer (1967) 体积方程的林分水平变体来研究不同的林分直径和高度测量如何影响体积预测。当模型中包含密度时（$\text{体积}=F（\text{直径},\text{高度},\text{密度}））$直径测量的选择比高度测量的选择更重要。当不包括密度时$（\text{体积}=F（\text{直径},\text{高度}））$，事实恰恰相反。对于包含密度的模型，基于矩的支架直径和高度估计器的性能优于所有其他测量。对于没有密度的模型，林分直径和高度的最大树木估计器的表现优于其他测量。总体最佳方程使用二次平均直径、Lorey 高度和密度（均方根误差 = 5.26 m ³  ha ^-1；1.9% 相对误差）。没有密度的最佳方程使用计算林分密度指数 400 所需的最大树木的平均直径和每公顷最高 400 棵树的平均高度（均方根误差 = 32.08 m ³  ha ⁻¹；11.8 % 相对误差） 。这项研究的结果对于高度二次采样和激光雷达衍生的森林清查分析具有一些重要意义。