杉木三维模型各方向枝下高分布研究

【目的】通过分析实测枝下高分布方向与空间竞争强度的关系,解决基于传统林学研究调查数据所构建的林木三维模型对不同方向枝下高分布差异难以直观表达,林木三维模型多态性表现不足的问题。【方法】以江西省新余市分宜县亚热带林业实验中心山下林场8块杉木临时样地为数据源,以已有枝下高模型为理论基础,将空间分析方法缓冲区构建与林分空间结构单元构建结合,构建对林木造成直接影响的水平空间结构参数与垂直空间结构参数,分析空间结构参数与枝下高相关性,并以此计算各方向空间竞争强度,建立空间竞争强度与实测枝下高的分布关系,再按照枝下高模型求解剩余方向枝下高,最终按照实测数据与分析计算结果加载分枝、主干模型,构建林木三维模型。【结果】所选模型变量包括林木属性与空间结构参数,原始模型决定系数为0.720,消除树高影响的调整后实测枝下高与水平空间结构参数相关系数为0.410、与垂直空间结构参数相关系数为0.782,且均呈正相关;将各自相关系数为权重计算对应方向空间竞争强度,将最小竞争强度方向空间结构参数代入模型,拟合结果决定系数为0.790,相比原始模型拟合精度有所提高;将实测枝下高分配到竞争强度最小的方向,根据模型可对其他方向枝下高进行估算。【结论】以杉木为例,通过空间竞争强度判别枝下高分布,在提高已有数据利用率、减小外业工作强度的基础上,可直观表现林木不同方向枝下高分布的差异性,增强了林木三维模型的多态性表达。

The distribution of under branch heights in various directions of the three-dimensional Chinese fir model

【Objective】Actual measurements of the relationship between the distribution direction of branch heights and the intensity of spatial competition have highlighted the downside to three-dimensional(3 D)forest models construction based on traditional forestry research survey data.Such models are unable to directly express differences in branch height distribution in varying directions.This results in the insufficient performance of the forest tree 3 D model polymorphism.【Method】This study used eight temporary sample plots of Chinese fir in the Shanxia Forest Farm of Fenyi County,Xinyu City,Jiangxi Province,China to supply data.The existing undershoot height model was used alongside establishing the buffer zone of the geological analysis method combined with the forest stand spatial structure unit.The horizontal and vertical spatial structure parameters that directly affect the forest trees were established,and the high correlation between the spatial structure parameters and the undershoot was analyzed.This analysis provided the basis for the calculation of the spatial competition intensity in each direction,and the established relationship between spatial competition intensity and the measured undershoot height distribution.The under-branch height model was used to calculate the remaining-direction under-branch height.Finally,the branch and trunk model was loaded according to the measured data and analysis and calculation structure to construct a 3 D forest model.【Result】The selected basic model variables included forest attributes and spatial structure parameters;the original model coefficient of determination was 0.720,the horizontal spatial structure and the adjusted branch height correlation coefficient was 0.410 to eliminate the influence of tree height.The vertical spatial structure correlation coefficient was 0.782,and positively correlated with the branch height.The respective correlation coefficients were weights to calculate spatial competition intensity in the corresponding direction.The minimum competition intensity direction spatial structure parameter was used to achieve the basic model fitting coefficient of determination of 0.790;this was an improvement compared with the original model.The measured branch height was allocated to competition intensity in the smallest direction and to quantify the lower height of branches in remaining directions.【Conclusion】Chinese fir was used as an example of utilizing spatial competition intensity to discriminate the high distribution under branches to improve the utilization of existing data and reducing field work intensity.This approach intuitively expresses differences in the high distribution under the branches of the forest and enhances the polymorphism of the 3 D forest model expression.