哑变量在云杉地上生物量模型中的应用研究

Research on Dummy Variable in Aboveground Biomass Models for Spruce

基于150株样木的生物量数据,采用常规回归方法和哑变量模型方法,构建了黑龙江、吉林两省云杉地上总生物量与各分项生物量的一元、二元以及三元模型。结果表明:2种方法建立的模型中,地上总生物量模型预估精度最高,在96%以上;树叶生物量最低,仍达87%以上;其他生物量预估精度均在91%以上;总相对误差均控制在±5%的范围内。通过对一元、二元和三元模型对比分析,发现随着解释变量增加,2种方法生物量模型的预估精度和确定系数都有所提高。引入地域哑变量后,一元、二元和三元模型的预估精度和确定系数都比常规模型有所提高,估计值的标准误差和总相对误差有一定下降,哑变量可以提高模型的拟合优度和预测效果。

Based on the biomass data of 150 spruce sampling trees, by using conventional regression meth- ods and dummy variable modeling approach, one variable and two or three variables biomass models were established for the total aboveground biomass and the biomass of components for spruce in Heilongjiang and Jilin provinces. The results showed that the total aboveground biomass models had the highest predic- tion accuracy（96% or more）and the leaf biomass models had the lowest prediction accuracy which still reached more than 87%. The prediction of other models reached more than 91% and the total relative er- ror was controlled within ± 5%. The prediction accuracy and the determination coefficient of biomass models were improved with the increase of the explanatory variables. After the introduction of the dummy variable, the prediction accuracy and the determination coefficient was improved meanwhile the standard error and the total relative error was reduced for one variable and two or three variables biomass mod- els. Dummy variable can improve the prediction effect of the model.