含地域和起源因子的马尾松立木生物量与材积方程系统
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摘要
【目的】建立含地域和起源因子的相容性立木生物量与材积方程系统,为准确估计森林生物量提供依据。【方法】以我国南方主要针叶树种马尾松为研究对象,基于301和104株样木的实测地上生物量、树干材积和地下生物量数据,综合利用哑变量建模方法和误差变量联立方程组方法,建立集地上生物量、树干材积和地下生物量为一体、确保与生物量转换因子和根茎比等变量相容的一元和二元方程系统,并分析立木生物量和材积估计是否受地域和起源的影响。【结果】所建立的马尾松一元和二元相容性立木生物量方程与材积方程,确定系数(R~2)均在0.92以上,其中地上生物量方程的平均预估误差在4%以内,地下生物量方程的平均预估误差在8%以内。对马尾松地上生物量和树干材积的估计,二元模型均显著优于一元模型,其F统计量远大于临界值;但对地下生物量的估计,二元模型反而不如一元模型效果好。不论是一元模型还是二元模型,地域和起源对马尾松地上生物量估计均无显著影响,地上生物量模型具有很好的通用性,同时也进一步印证了曾伟生等(2012)提出的通用性地上生物量模型M=0.3ρD~(7/3)的广泛适用性。对马尾松地下生物量的估计,不同地域的模型存在显著差异;相同直径的林木,总体1地域范围内(长江流域东南部)的地下生物量要大于总体2(长江流域中西部)。对马尾松树干材积的估计,二元模型不受地域和起源影响,但一元模型受起源影响;相同直径的林木,人工林的材积估计值大于天然林。【结论】将哑变量引入误差变量联立方程组,可同时解决多个变量之间的相容性及地上生物量和地下生物量样本单元数不相等时如何联合建模的问题,是切实可行的生物量建模方法;研究所建立的马尾松立木生物量方程、材积方程及其相容的生物量转换因子和根茎比方程,达到相关技术规定预估精度要求,可推广应用。
【Objective】 Climate change has increased the need of information on amount of forest biomass. The purpose of this study is to develop compatible individual tree biomass and volume equations, providing a quantitative basis on accurate estimation of forest biomass.【Method】 Based on the mensuration data of above-and belowground biomass from 301 and 104 destructive sample trees of Masson pine(Pinus massoniana)in southern China, respectively, one-and two-variable systems, which combined aboveground biomass and stem volume equations with belowground biomass equation, and ensured them compatible with biomass conversion factor and root-to-shoot ratio equations, were developed using dummy variable modeling approach and error-in-variable simultaneous equations approach, and effects of region and origin on estimation of biomass and volume were analyzed.【Result】 The coefficients of determination(R~2)of one-and two-variable compatible individual tree biomass and volume equations for Masson pine developed in this study were more than 0.92, whereas the mean prediction errors(MPEs) of above-and belowground biomass equations were less than 4% and 8%, respectively. For estimation of aboveground biomass and stem volume of Masson pine, two-variable equations were significantly better than one-variable equations, for the F-statistics between one-and two-variable aboveground biomass and stem volume equations were greatly larger than the critical F value. But for estimation of belowground biomass, one-variable were even better than two-variable ones. Effects of region and origin on estimation of both one-and two-variable aboveground biomass equations were not significant, indicating that aboveground biomass equations of Masson pine were generalized on national level. Furthermore, the general allometric biomass model M=0.3ρD~(7/3 )presented by Zeng et al.(2012)was proved to be applicable in practice. For estimation of belowground biomass of Masson pine, models in different regions were significantly different. For trees with the same diameter, estimate of belowground biomass in the region of modeling population 1(south-eastern part of Yangtze River basin) was larger than that in the region of modeling population 2(central-western part of Yangtze River basin). For estimation of stem volume of Masson pine, two-variable model was not affected by region and origin, whereas one-variable model was affected by origin. For trees with the same diameter, estimate of stem volume in a planted stand was larger than that in a natural stand.【Conclusion】 Integrating dummy variable into error-in-variable simultaneous equations is a practical approach, which not only can ensure the compatibility among several target variables, but also can develop simultaneously a system even though numbers of above-and belowground biomass observations are very different. The biomass equations, volume equations, and compatible biomass conversion factor equations and root-to-shoot ratio equations developed for Masson pine in this study meet the need of precision requirements to relevant regulation, and can be used in application.
【Objective】 Climate change has increased the need of information on amount of forest biomass. The purpose of this study is to develop compatible individual tree biomass and volume equations, providing a quantitative basis on accurate estimation of forest biomass.【Method】 Based on the mensuration data of above-and belowground biomass from 301 and 104 destructive sample trees of Masson pine(Pinus massoniana)in southern China, respectively, one-and two-variable systems, which combined aboveground biomass and stem volume equations with belowground biomass equation, and ensured them compatible with biomass conversion factor and root-to-shoot ratio equations, were developed using dummy variable modeling approach and error-in-variable simultaneous equations approach, and effects of region and origin on estimation of biomass and volume were analyzed.【Result】 The coefficients of determination(R~2)of one-and two-variable compatible individual tree biomass and volume equations for Masson pine developed in this study were more than 0.92, whereas the mean prediction errors(MPEs) of above-and belowground biomass equations were less than 4% and 8%, respectively. For estimation of aboveground biomass and stem volume of Masson pine, two-variable equations were significantly better than one-variable equations, for the F-statistics between one-and two-variable aboveground biomass and stem volume equations were greatly larger than the critical F value. But for estimation of belowground biomass, one-variable were even better than two-variable ones. Effects of region and origin on estimation of both one-and two-variable aboveground biomass equations were not significant, indicating that aboveground biomass equations of Masson pine were generalized on national level. Furthermore, the general allometric biomass model M=0.3ρD~(7/3 )presented by Zeng et al.(2012)was proved to be applicable in practice. For estimation of belowground biomass of Masson pine, models in different regions were significantly different. For trees with the same diameter, estimate of belowground biomass in the region of modeling population 1(south-eastern part of Yangtze River basin) was larger than that in the region of modeling population 2(central-western part of Yangtze River basin). For estimation of stem volume of Masson pine, two-variable model was not affected by region and origin, whereas one-variable model was affected by origin. For trees with the same diameter, estimate of stem volume in a planted stand was larger than that in a natural stand.【Conclusion】 Integrating dummy variable into error-in-variable simultaneous equations is a practical approach, which not only can ensure the compatibility among several target variables, but also can develop simultaneously a system even though numbers of above-and belowground biomass observations are very different. The biomass equations, volume equations, and compatible biomass conversion factor equations and root-to-shoot ratio equations developed for Masson pine in this study meet the need of precision requirements to relevant regulation, and can be used in application.
引文
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李际平,郭文清,曹小玉. 2013. 基于非线性度量误差的马尾松相容性立木生物量模型. 中南林业科技大学学报, 33(6):22-25.(Li J P, Guo W Q, Cao X Y. 2013. Compatible nonlinear tree biomass models with measurement error for Masson pine (Pinus massoniana). Journal of Central South University of Forestry & Technology, 33(6):22-25. [in Chinese])
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国家林业局. 1999. 立木材积表(LY/T 1353—1999). 北京: 中国标准出版社.(State Forestry Administration. 1999. Tree volume tables(LY/T 1353—1999). Beijing: China Standard Press. [in Chinese])
国家林业局. 2014. 中国森林资源报告(2009—2013). 北京: 中国林业出版社.(State Forestry Administration. 2014. Report of Forest Resources in China (2009—2013). Beijing: China Forestry Publishing House. [in Chinese])
国家林业局. 2015a. 立木生物量建模样本采集技术规程(LY/T 2259—2014). 北京: 中国标准出版社.(State Forestry Administration. 2015a. Technical regulation on sample collections for tree biomass modeling(LY/T 2259—2014). Beijing: China Standard Press. [in Chinese])
国家林业局. 2015b. 立木生物量建模方法技术规程(LY/T 2258—2014). 北京: 中国标准出版社.(State Forestry Administration. 2015b. Technical regulation on methodology for tree biomass modeling(LY/T 2258—2014). Beijing: China Standard Press. [in Chinese])
国家林业局. 2015c. 立木生物量模型及碳计量参数—马尾松(LY/T 2263—2014). 北京: 中国标准出版社.(State Forestry Administration. 2015c. Tree biomass models and related parameters to carbon accounting for Pinus massoniana(LY/T 2263—2014). Beijing: China Standard Press. [in Chinese])
贺东北,骆期邦,曾伟生. 1998. 立木生物量线性联立模型研究. 浙江林学院学报, 15(3): 298-303.(He D B, Luo Q B, Zeng W S. 1998. Study on linear simultaneous model of tree biomass. Journal of Zhejiang Forestry College, 15(3):298-303. [in Chinese])
李海奎,雷渊才. 2010. 中国森林植被生物量和碳储量评估. 北京:中国林业出版社.(Li H K, Lei Y C. 2010. Estimation and evaluation of forest biomass and carbon storage in China. Beijing: China Forestry Publishing House. [in Chinese])
李晖,曾伟生. 2016. 不同区域落叶松二元立木材积表的检验及差异分析. 林业科学, 52(6): 157-162.(Li H, Zeng W S. 2016. Validation and comparison of two-variable tree volume tables for Larix spp. in different regions of China. Scientia Silvae Sinicae, 52(6):157-162. [in Chinese])
李际平,郭文清,曹小玉. 2013. 基于非线性度量误差的马尾松相容性立木生物量模型. 中南林业科技大学学报, 33(6):22-25.(Li J P, Guo W Q, Cao X Y. 2013. Compatible nonlinear tree biomass models with measurement error for Masson pine (Pinus massoniana). Journal of Central South University of Forestry & Technology, 33(6):22-25. [in Chinese])
吕勇,曾思齐,邓湘文,等. 1996. 马尾松林分生物量的研究. 中南林学院学报, 16(4): 28-32.(Lü Y, Zeng S Q, Deng X W, et al. 1996. A study of stand biomass of Pinus massoniana. Journal of Central South Forestry University, 16(4): 28-32. [in Chinese])
唐守正,郎奎建,李海奎. 2008. 统计和生物数学模型计算(ForStat教程). 北京: 科学出版社.(Tang S Z, Lang K J, Li H K. 2008. Statistics and computation of biomathematical models (course of ForStat). Beijing: China Science Press. [in Chinese])
吴守蓉,杨惠强,洪蓉,等. 1999. 马尾松林生物量及其结构的研究. 福建林业科技, 26(1): 18-21.(Wu S R, Yang H Q, Hong R, et al. 1999. Studies on the biomass of Pinus massoniana plantations and its structure. Journal of Fujian Forestry Sci & Tech, 26(1):18-21. [in Chinese])
曾鸣,聂祥永,曾伟生. 2013. 中国杉木相容性立木材积和地上生物量方程. 林业科学, 49(10):74-79.(Zeng M, Nie X Y, Zeng W S. 2013. Compatible tree volume and aboveground biomass equations of Chinese fir in China. Scientia Silvae Sinicae, 49(10): 74-79. [in Chinese])
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