• 论文与研究报告 •

### 基于异速参数概率分布的立木地上生物量估算

1. 中国林业科学研究院林业研究所国家林业局林木培育重点实验室 北京 100091
• 收稿日期:2013-07-09 修回日期:2013-09-22 出版日期:2014-06-25 发布日期:2014-07-07
• 基金资助:

林业公益性行业科研专项经费项目（201104027）。

### Estimation of Above-Ground Tree Biomass Based on Probability Distribution of Allometric Parameters

Huang Xingzhao, Chen Dongsheng, Sun Xiaomei, Zhang Shougong

1. Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration Research Institute of Forestry, CAF Beijing 100091
• Received:2013-07-09 Revised:2013-09-22 Online:2014-06-25 Published:2014-07-07
• Contact: 张守攻

Abstract:

Allometric biomass equations are widely used to predict above-ground biomass in forest ecosystems. It found the distribution of the parameters a and b of the allometry between above-ground biomass (M) and diameter at breast height(D), lnM=a + blnD, well approximated by a bivariate normal from analysis a data of 304 functions of 80 papers. ANOVA was tested to parameters in seven genera. In contrast to the parameter a, there was significant difference in parameter b. There were negative correlation between the parameter a and b, the parameter b and latitude. From this negative correlation, simultaneous-equation was used to build general model for parameters which were changed by latitude. Three methods which include established general model, minimum-least-square regression and Bayesian approach were used to fitting the above-ground biomass of Larix kaempferi in sub-tropical alpine area. The result showed that general model was the lowest precise quantifications(R2=0.892), but it could estimate the biomass where forest situated in latitude without samples. With sample size was more than 50, both Bayesian method and minimum-least-square regression was no significant difference in the mean absolute error. And it was less than 50, Bayesian method was better than minimum-least-square regression. Therefore, it was suggested that Bayesian method was used to estimate above-ground biomass when the sample size was less than 50.