湖南省杉木林分相容性树高曲线方程组研究

Compatibility of height-diameter curve equations for Cunninghamia lanceolata stands in Hunan Province

为了掌握杉木Cunninghamia lanceolata天然林与人工林的树高、胸径生长规律,改进杉木的营林生产模式,利用湖南省杉木一类清查数据,选择了6种常用经验方程拟合杉木树高曲线和胸径生长曲线,从中选出最优模型。在此基础上,考虑林分起源的差异,采用引入哑变量的度量误差模型方法建立不同起源林分相容性树高曲线方程组,并分析比较林分各阶段的生长状况。结果表明:引入哑变量后所建立的树高曲线方程组效果更好,在含哑变量的树高曲线方程组中杉木林分平均胸径生长过程曲线、平均树高曲线的拟合决定系数分别为0.820 5,0.758 0,预估精度分别达到了98.30%和98.01%,且其他各项评价指标均优于不含哑变量的树高曲线方程组;通过对比分析各林分不同林龄段的生长状况知:杉木人工林和天然林的总体趋势一致,但在中幼龄阶段,人工林的树高和胸径生长率高于天然林,且人工林的树高与胸径均比天然林先达到最大值。利用引入哑变量的度量误差模型方法建立树高生长模型,不仅考虑度量误差提高了模型精度,还解决了模型间不相容的问题。

To improve the forest production mode of Cunninghamia lanceolata artificial and natural forests in Hunan Province, their height-diameter curves were studied. Based on periodic inventory data of C. lanceolata in Hunan, six common empirical models were used to fit height-growth and diameter-growth curves,and selected the best one from them. On the basis of the optimal model, differences in stand origin were considered and compatible height-diameter curve equations for a group of stands with different stand origins were built by introducing dummy variables and using the measurement error model method. These growth status for stand in different origins were compared and analyzed by drawing scatter plots. Results showed that the fitted coefficient of determination for the C. lanceolata height-growth model with the dummy variables was better than this without dummy variables. The fitted coefficients for the tree diameter-growth curve was 0.820 5 with prediction accuracy of 98.30%, and for tree height was 0.758 0 with prediction accuracy of 98.01%. Also, DMADand ERMSE indicators were superior to the traditional methods. The growth progress of C. lanceolata artificial and natural forests were the same; however, in the young stage, tree height and diameter growth rate of the artificial forest were higher than the natural forest. Moreover, tree height and diameter in the artificial forest reached a maximum value earlier than in natural forests. In conclusion, the model of tree height growth was established using the model with dummy variables, which not only improved the accuracy of the model but also solved the problem of incompatibility between models.