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应用生态学报 ›› 2018, Vol. 29 ›› Issue (11): 3685-3695.doi: 10.13287/j.1001-9332.201811.020

• 研究报告 • 上一篇    下一篇

基于不同预测变量的天然椴树可加性地上生物量模型构建

王佳慧, 李凤日, 董利虎*   

  1. 东北林业大学林学院, 哈尔滨 150040
  • 收稿日期:2018-04-27 出版日期:2018-11-20 发布日期:2018-11-20
  • 通讯作者: *E-mail: donglihu2006@163.com
  • 作者简介:王佳慧,女,1994年生,硕士. 主要从事林分生长与收获研究. E-mail: 1206557598@qq.com
  • 基金资助:

    本文由国家自然科学基金项目(31600510)和林业科学技术推广项目([2016]36)资助

Additive aboveground biomass equations based on different predictors for natural Tilia Linn

WANG Jia-hui, LI Feng-ri, DONG Li-hu*   

  1. School of Forestry, Northeast Forestry University, Harbin 150040, China
  • Received:2018-04-27 Online:2018-11-20 Published:2018-11-20
  • Contact: *E-mail: donglihu2006@163.com
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (31600510) and the Forestry Science and Technology Extension Project ([2016]36).

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,准确测定生物量对于计算碳储量以及研究气候变化、森林健康、森林生产力、养分循环等十分重要.目前,测算森林生物量常用的方法为生物量模型估算法.本研究基于小兴安岭地区和张广才岭地区97株实测生物量数据,建立了3个天然椴树立木可加性生物量模型系统(基于胸径的一元可加性生物量模型系统、基于胸径和树高的二元可加性生物量模型系统、基于最优变量的最优可加性生物量模型系统),采用非线性似乎不相关回归法进行参数估计,用加权方法解决模型的异方差问题,并采用“刀切法”进行模型检验.结果表明: 3种可加性生物量模型系统均能较好地对椴树各部分生物量进行拟合和预测(调整后确定系数Ra2>0.84,平均预测误差百分比MPE<8.5%,平均绝对误差MAE<16.3 kg,平均百分标准误差MPSE<28.5%),其中,树干和地上生物量的拟合效果优于树叶、树枝和树冠;在引入树高和树冠因子后,提高了模型的拟合效果和预测能力(Ra2提高0.01~0.04,MAE降低0.01~4.55 kg),缩小了预测值置信区间的范围,树干、树叶和地上生物量提高较多,树枝和树冠提高较少.总体来看,最优生物量模型系统效果最好,其次为二元生物量模型系统,再次是一元生物量模型系统,添加树高和树冠因子进行生物量模型的构建十分必要.

关键词: 可加性模型, 刀切法, 生物量, 异速生长方程, 椴树

Abstract: Biomass is a basic quantitative character of forest ecosystem. Biomass data are foundation of researching many forestry and ecology problems. Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Constructing biomass models is considered a good approach to estimate forest biomass. Based on biomass data of 97 sampling trees of natural Tilia Linn. in Xiaoxing’an Mountains and Zhangguangcai ranges, three additive systems of individual tree biomass equations were developed: based on tree diameter at breast height (D) only, based on tree diameter at breast height and height (H), and based on the best models. The nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass equations. The heteroscedasticity in model residuals was addressed by applying a unique weight function to each equation. The individual tree biomass model validation was accomplished by Jackknifing technique. The results showed that three additive systems of individual tree biomass equations could fit and predict the biomass of Tilia Linn. well (adjusted coefficient of determination Ra2>0.84, mean predicted error percentage MPE<8.5%, mean absolute error MAE<16.3 kg,mean standard error percentage MPSE<28.5%). The biomass equations of stem and aboveground were better than biomass equations of branch, foliage and crown. Adding total tree height and crown factor in the additive systems of biomass equations could significantly improve model fitting performance and predicting precision (Ra2 improved from 0.01 to 0.04, MAE decreased from 0.01 to 4.55 kg), narrow the confidence interval of the predicted value and the biomass of stem, foliage and aboveground were increased more than the biomass of branch and crown. In general, the equations of the additive system based on the best models produced the best model fitting, followed by that of the additive system based on D and H, and that based on D. It was essential to develop biomass model by adding total tree height and crown factor.

Key words: Tilia Linn., allometric equation, Jackknifing technique, biomass, additive system