西藏冷杉立木生物量和材积模型研建  

Establishment of biomass and tree volume equations for Abies in Tibet

作　　者：陈振雄[1,2] 贺东北 肖前辉[1] 周湘江[1] CHEN Zhenxiong;HE Dongbei;XIAO Qianhui;ZHOU Xianghong(Central South Forest Inventory and Planning Institute of State Forestry Administration, Changsha 410014, Hunan, China;The Afforestation Department of Forestry Department of Tibet Autonomous Region, Lasa 850000, Tibet, China)

机构地区：[1]国家林业局中南林业调查规划设计院,湖南长沙410014 [2]西藏自治区林业厅造林绿化处,西藏拉萨850000

出　　处：《中南林业科技大学学报》2018年第1期16-21,共6页Journal of Central South University of Forestry & Technology

基　　金：国家林业局专题研究项目"基于清查资料的中国森林植被生物量和碳储量评估";西藏林业厅资助项目"2011年西藏森林资源清查生物量调查建模"(XZLX-2011-001)

摘　　要：利用2011年采集的150株西藏天然冷杉数据,采用度量误差联立方程组方法同时进行整体建模和分段建模,分别建立了西藏冷杉一元、二元生物量与材积相容性模型,并分析对比两者拟合效果。结果表明:不论是一元、二元模型,采用整体建模方法都难以准确描述冷杉生物量、材积随胸径变化情况,导致径阶16 cm以下的林木立木材积和生物量估计值均小于实际值,径阶越小,偏差越大,其中4 cm径阶的预估偏差甚至达到了20%~30%;而采用分段建模方法能有效解决上述有偏估计的问题,模型改进效果十分良好,各径阶均无系统偏差;分段建立的地上生物量和立木材积方程,不论一元或二元模型,其预估精度分别达到了93.5%、92.8%以上,一元分段地下生物量方程预估精度也在91.5%以上。Based on the tree volume and biomass data of 150 Abies in Tibet,the compatible tree volume and biomass equations and biomass conversion functions were constructed by using the error-in-variable simultaneous equations and segmented modeling approach,and the fitting effect between the two models were analyzed and compared.The results show that：The whole single-tree equations is very difficult to accurately describe the biomass and tree volume with DBH variation,the whole simulation equations for single-tree tree volume and biomass may result in obvious biased estimation for small young trees（DBH 〈16 cm）,the prediction deviation reached 20% or even more than 30% in 4 cm diameter grade.But the segmented modeling approach can resolve the problem of systematically biased estimation in small diameter classes for commonly-used tree volume and biomass equations.Through the one or two variable-based segmented equations,both the prediction precision（P）of tree volume and above-ground biomass estimates for the whole data are more than 93.5%,92.8%,and through the one variables-based segmented equations,the prediction precision（P）of below-ground biomass estimates for the whole data is more than 91.5%.

关 键 词：立木材积 生物量 度量误差联立方程组 分段建模 相容性 冷杉 西藏 

分 类 号：S718.556[农业科学—林学]