文献类型：期刊文献

中文题名：相容性生物量模型的建立及其估计方法研究

英文题名：STUDY ON ESTABLISH AND ESTIMATE METHOD OF COMPATIBLE BIOMASS MODEL

作者：唐守正[1] 张会儒[1] 胥辉[1]

第一作者：唐守正

机构：[1]中国林业科学研究院资源信息研究所

年份：2000

卷号：36

期号：z01

起止页码：19-27

中文期刊名：林业科学

外文期刊名：SCIENTIA SILVAE SINICAE

收录：CSTPCD;;北大核心:【北大核心1996】；CSCD:【CSCD2011_2012】；

基金：林业部重点课题!"我国主要树种二元生物量模型及其相容的一元自适应模型系列的研究"( 95 0 3 0 2 )部分内容

语种：中文

中文关键词：森林生物量模型;相容性;非线性联合估计

外文关键词：Biomass model, Compatibility, Nonlinear joint estimate

分类号：S718.556;F758.1

摘要：森林生物量是森林生态系统的最基本数量特征 ,生物量数据是研究许多林业问题和生态问题的基础 ,因此 ,准确测定生物量十分重要。建立生物量模型是生物量估测的主要手段。以往所建模型 ,存在一个严重的缺陷 ,即各分量模型间不相容。如何解决相容性问题 ,一直是生物量估计领域所面临的一个难题。本文以长白落叶松为实例 ,提出了一种新方法———非线性联合估计法 ,并与比例平差法进行了对比。针对不同建模方法 ,设计了 5种估计方案 ,经过分析比较 ,确定了 1种方案为最优估计方案。该方案以树干生物量作为控制量 ,采取两级联合估计。模型构成如下第一级,W1=f2(x)+f5(x),W2=f2(x),W5=f5(x);;第二级,W3=f3(x),W4=f2(x)-f3(x);;第三级,W6=f6(x),W7=f5(x)-f6(x)。本文中模型选型采用了变量逐步筛选法,参数估计采用了加权最小二乘法,以消除异方差现象。同时,提出了5个指标用于模型评价,即参数变动系数C%、总相对误差RS%、平均相对误差EE%,平均相对误差绝对值RMA%和预估精度P%
Forest biomass is a basic quantity character of the forest ecological system. Biomass data are foundation of researching many forestry and ecology problems, thus accurate measurement of biomass is very important. Establishing biomass models is a major way to biomass estimation. There were a serious shortcomings in the models established previously, i.e.the results were incompatible for models of each component, in other words, the sum of estimated biomass of wood, bark, branches and foliage was unequal to estimated biomass of total aboveground, the sum of estimated biomass of wood and bark was unequal to estimated biomass of stem, the sum of estimated biomass of branches and foliage was unequal to estimated biomass of crown. There fore how to obtain the compatibility is stile a difficult problem for biomass estimate. A new method, nonlinear joint estimate, was proposed in this paper, and compared with method of adjustment in proportion. To the different methods of establishing models, five alternative methods were designed, then one of them was determined as a optimum method through the analysis and comparison. The optimum method took stem as a basis component and adopted two steps joint estimate, structure of models was as follows; the first step, total aboveground\;W\-1=f\-2(x)+f\-5(x), stem W\-2=f\-2(x), crown W\-5=f\-5(x); the second step, wood W\-3=f\-3(x), bark W\-4=f\-2(x)-f\-3(x); branch W\-6=f\-6(x), foliage W\-7=f\-5(x)-f\-6(x).\;In this paper, the progressive variable selection method was used to select models structure, and weighted least squares method was used to estimate parameters for reducing errors of non\|homogeneous variance. At meantime, the paper used five indices to evaluate models, they were coefficient of variation for parameters \%C?%\%, total relative error \%RS?%\%,average relative error \%EE?%\%,average absolute value of relative error \%RMA?%\% and prediction precision \%P?%\%. All of researches shown above in this paper took \% Larix olgensis\% as an example.