• 论文与研究报告 •

### 杉木单木和林分水平地下生物量模型的构建

1. 中国林业科学研究院资源信息研究所 北京 100091
• 收稿日期:2016-05-17 修回日期:2016-12-17 出版日期:2018-02-25 发布日期:2018-03-30
• 基金资助:
国家自然科学基金项目"基于森林清查数据的大尺度森林碳储量监测方法研究"（31370634）。

### Establishment of Below-Ground Biomass Equations for Chinese Fir at Tree and Stand Level

Zhao Jiacheng, Li Haikui

1. Research Institute of Forest Resource Information Techniques, CAF Beijing 100091
• Received:2016-05-17 Revised:2016-12-17 Online:2018-02-25 Published:2018-03-30

Abstract: [Objective] In order to provide a scientific basis for below-ground biomass estimation at stand level, heavy sample and second sample were used, below-ground biomass equations were constructed and fitted to compare the effect of sampling forms on the individual tree biomass equations. In the main distribution area of Chinese fir(Cunninghamia lanceolata), the study on expanding below-ground biomass from individual tree to regional scale was conducted and the advantage and disadvantage of the different forms of below-ground biomass equations at stand level was explored.[Method] 278 trees of Chinese fir(C. lanceolata) with measured above-ground biomass were taken as a heavy sample, 88 trees of which with measured below-ground biomass as second sample. The models at individual level included single independent model, the simultaneous equations compatible with above-ground biomass, which only used second sample, and the simultaneous equations compatible with above-ground biomass, which combines a heavy sample and second sample. Choosing the below-ground biomass equation based on stand description factors, fixed root-shoot ratio equation and the root-shoot ratio equation based on stand description factors, the expansion method from tree level to regional scale on below-ground biomass were studied in Fujian, Jiangxi and Guangdong provinces. Coefficient of determination(R2), root mean square error(RMSE), average system error(ASE), relatively mean absolute error(RMA),relatively total error(TRE)and mean prediction error(MPE) were used to evaluate the model fitness. The model parameters in different provinces were compared and the relationship between stability of parameter estimates and sample size were analyzed. Meanwhile, parameter estimates were also compared with the root-shoot ratios recommended by IPCC.[Result] All three types of individual tree equations basically performed the same efficiency with R2 reaching to 0.95,the compatible equation combing a heavy sample and second sample performed best. The root-shoot ratio equation based on stand description factors had a significantly better fitting than the fixed root-shoot ratio equation(R2 improved 0.04-0.08, RMSE reduced 1 t·hm-2) when expanding to regional scale. The below-ground biomass equation based on stand description factors performed better than the fixed root-shoot ratio equation but inferior to the root-shoot ratio equation based on stand description factors. The prediction error had geography diversity and the same method on predicting error in different provinces failed to give a consistent law.[Conclusion] Combining a heavy sample and second sample contributes to model fitting at tree level. Adding stand description factors into below-ground biomass stand model significantly increases model fitness. As the form of fixed root-shoot ratio equation is sample, it could be conveniently used to conduct below-ground biomass expansion. The research result contribute to the selection and establishment of the best individual tree below-ground biomass equation, and provide an accurate and scientific method for tree-level biomass expansion to regional scale.