论文部分内容阅读
区域森林生物量的估算方法是人们目前关注的焦点,建立林分生物量模型成为一种趋势.本文以吉林省落叶松人工林固定样地为例,采用非线性似乎不相关回归法构建2种林分生物量模型,即基于林分变量的林分生物量模型(模型系统Ⅰ)和基于生物量换算系数的林分生物量模型(模型系统Ⅱ),给出落叶松人工林固定生物量换算系数值,并比较了3种林分生物量估算方法的预估精度.结果表明:所建立的2种林分生物量模型中,总生物量和树干生物量模型拟合和预测效果较好,其R_a~2>0.95,且均方根误差(RMSE)、平均预测误差(MPE)和平均绝对误差(MAE)都较小.树叶和树枝生物量模型拟合和预测效果相对较差,其模型的R_a~2<0.95.模型系统Ⅰ和模型系统Ⅱ的预测精度均优于固定生物量换算系数法.基于生物量换算系数的林分生物量模型属于材积源生物量法,其本质与基于林分变量的林分生物量模型不同,但二者的预测效果相当.固定生物量换算系数的预测能力较差,将生物量与蓄积量之比假定为恒定常数是不恰当的.此外,为了使模型参数估计更有效,所建立的生物量模型应当考虑林分总生物量及各分项生物量的可加性.
The estimation method of regional forest biomass is the focus of people’s attention at present and it becomes a trend to establish stand biomass model.In this paper, taking Jilin larch plantation as an example, two kinds of non-linear regression analysis methods The stand biomass model, ie stand biomass model based on stand variables (model system I) and stand biomass model (model system II) based on biomass conversion factors, gives the conversion of fixed biomass of larch plantation And the prediction accuracy of three methods of biomass estimation was compared.The results showed that the biomass and trunk biomass model fit and predict well in the two stand biomass models, (RMSE), average prediction error (MPE) and average absolute error (MAE) of R_a ~ 2> 0.95.The fitting and prediction results of leaf and branch biomass models are relatively poor, and the model Of R_a ~ 2 <0.95.The predictive accuracy of Model I and Model II are better than that of fixed biomass conversion.The forest biomass model based on biomass conversion coefficient belongs to the volume source biomass method, Sub-variables of the forest The model of biomass is different, but the prediction results of the two are equivalent, the prediction ability of fixed biomass conversion coefficient is poor, and it is not appropriate to assume the ratio of biomass to accumulation as constant.In addition, in order to make model parameter estimation more effective, The established biomass model should consider the total biomass of the stands and the additivity of the biomass of the sub-categories.