为提高干形复杂树种材积无损估算的精度，利用地基激光雷达点云数据，构建基于人工蒙古栎最优削度模型的二元材积方程。以哈尔滨市城市林业示范基地的蒙古栎人工林为研究对象，使用地基激光雷达扫描获得完整点云数据，经过裁剪、高程归一化、滤波、单木分割和枝叶分离等处理提取树干结构参数。根据蒙古栎干形特征，选用6种削度方程模型(Biging(1984)、Amidon(1984)、孟宪宇(1982)、Kozak(2004)-Ⅱ、曾伟生等(1997)、Max and Burkhart(1976))，通过非线性回归拟合，筛选最优模型并构建削度-二元材积方程。研究结果表明，单木定位识别精度为95。22%，树高和胸径的提取值与实测值决定系数(R²)分别为0。97和0。98;最优削度模型拟合结果的决定系数(R²)和均方根误差(RMSE)分别为0。99和0。38 cm。所构建的蒙古栎削度-二元材积方程与现有材积计算方法进行残差分析表明，其估算结果具备可靠性，可为利用地基激光雷达点云数据估算干形复杂的树种材积提供重要技术支持。
Construction of a Taper-Binary Volume Equation for Plantation Mongolian Oak Based on TLS Data
To improve the accuracy of non-destructive estimation of volume of trees with complex trunk shapes,this study uses terrestrial laser scanning(TLS)point cloud data and developes a taper-binary volume equation based on the optimal taper model for plantation Mongolian oak.The study was conducted in a Mongolian oak plantation located at the Urban Forestry Demonstration Base in Harbin.Complete point cloud data were collected via TLS,and tree trunk structural parameters were extracted after processes such as clipping,elevation normalization,filtering,individual tree segmentation,and leaf-branch separation.Based on the trunk shape char-acteristics of Mongolian oak,six taper equation models were tested(Biging(1984),Amidon(1984),Meng Xianyu(1982),Kozak(2004)-Ⅱ,Zeng Weisheng et al.(1997),Max and Burkhart(1976)),and the best-fitting model was selected through nonlinear re-gression to construct the taper-binary volume equation.Results showed that the accuracy of individual tree identification was 95.22%,with the coefficient of determination(R²)for extracted tree height and diameter at breast height(DBH)being 0.97 and 0.98,respec-tively,when compared to field measurements.The optimal taper model achieved an R² of 0.99 and a root mean square error(RMSE)of 0.38 cm.Residual analysis of the Mongolian oak taper-binary volume equation from this study with the existing volume equation showed the reliability of its estimation results.It can provide important technical support for estimating the volume of trees with com-plex trunk shapes using TLS point cloud data.
国家科技期刊平台
NSTL
万方数据
