作　　者： (樊伟）; (许崇华）; (崔珺）; (王晶晶）; (刘西军）; (徐小牛）;

机构地区： 安徽农业大学林学与园林学院,合肥230036

出　　处： 《应用生态学报》 2017年第9期2831-2839,共9页

摘　　要： 基于安徽省大别山区马鬃岭林场杉木人工林30块样地1087组数据,选用7个常用树高-胸径(H-D)模型(线性模型、Chapman-Richards模型、Logistic模型等),采用最小二乘法拟合并选出最优基础模型(式11,只含D变量的Chapman-Richards模型),然后基于该模型构建含林分变量优势木平均高度、密度的H-D模型(式12),同时考虑样地水平的随机效应,分别基于式11、12构建混合模型(式13、14),并用幂函数、指数函数消除误差异方差,利用决定系数(R^2)、均方根误差(RMSE)、平均绝对误差(MAE)和平均相对误差绝对值(MAPE)等指标来评价模型的拟合与预测能力,最终获取最优树高预测模型.结果表明:含林分变量的模型的拟合精度(式12,R^2=0.863、RMSE=1.381、MAE=0.971)优于基础模型(式11,R^2=0.827、RMSE=1.554、MAE=0.101).对于误差方差,幂函数、指数函数均能较好地消除异方差,但幂函数相对最好.混合模型的拟合与预测能力均优于式11、12,但混合模型(式13、14)之间的拟合与预测精度相差不大.基于混合效应的H-D模型(式13)能够较好地描述不同林分间H-D关系的差异,实际运用中可选用该模型来预测杉木树高,具有较高的预测精度. A total of 1087 sets of data from 30 plots of Chinese fir （ Cunninghamia lanceolata） plantations were collected from Mazongling forestry farm in Dabie Mountains of Anhui Province. Seven commonly used height-diameter （H-D） models （i.e. linear, Chapman-Richards, Logistic models, etc. ） were selected and fitted by the least square method to obtain the optimal basic model （equation 11, a Chapman-Richards model with variable D only）. Based on this optimal basic model, we built up the H-D model （equation 12） with two stand variables [mean height of dominant trees （DH） and density）. Meanwhile, with the consideration of plot random effect, the mixed model, called equation 13 and 14, which based on equation 11 and 12 were constructed, using the power and exponent functions to eliminate heteroscedasticity. Then coefficient of determination （R^2）, root-mean-square error （RMSE）, mean absolute error （MAE） and mean absolute percentage error （MAPE） were used to evaluate their abilities of model fitting and prediction for determining the best model. The results showed that the fit accuracy of the model with stand variables （equation 12） （R^2 = 0.863, RMSE = 1.381, MAPE = 0.971 ） was better than the basic model （equation 11 ） （R^2 = 0.827, RMSE = 1.554, MAPE = 0.101 ）. For error variance, the power function and the exponent function could eliminate the heteroscedasticity, but the former was better than the latter. The mixed models （equation 13, 14） had better fitting and prediction precision than equation 11, 12. There was no significant difference between the two mixed models （equation 13, 14） in fitting and prediction precision. In application, due to a better description of H-D relationship between different stands, the mixed-effect model （equation 13） could be used to oredict tree height for Chinese fir plantations with higher precision.

关 键 词： 树高 胸径模型 林分变量 非线性混合模型 杉木人工林