新疆云杉一体化立木生物量模型系统研建
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摘要
[目的]研究建立地上生物量与地下生物量、立木材积之间相容,以及地上生物量与各分量之间可加的一体化生物量模型系统,为准确估计森林生物量提供定量依据。[方法]以新疆自治区的云杉(Picea spp.)为研究对象,基于230株和78株样木的实测地上生物量、树干材积和地下生物量数据,综合利用误差变量联立方程组方法和哑变量建模方法,研究建立集地上生物量、树干材积和地下生物量为一体,兼具相容性和可加性的二元和一元生物量模型系统,并分析一元模型是否受地域的影响。[结果]所建云杉一元和二元一体化生物量模型系统,地上生物量方程的平均预估误差在7%以下,干、皮、枝、叶各分项生物量方程的平均预估误差在10%左右,地下生物量方程的平均预估误差在15%以下,均达到了相关技术规定的预估精度要求。除了干材和树皮生物量的估计效果不如二元模型外,一元模型对其它各项生物量的估计均要优于二元模型。比例控制法和代数控制法均能解决地上生物量与干、皮、枝、叶各分项生物量之间的可加性问题,且两种方法得出的模型预估结果无显著差异。[结论]将哑变量引入误差变量联立方程组,不仅能解决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,还能同时解决地上生物量与地下生物量和立木材积之间的相容性问题及地上生物量与各分量之间的可加性问题,方法切实可行;对地上生物量、地下生物量及立木材积的估计,含区域因子的哑变量模型均要优于总体平均模型。
[Objective] The purpose of this study is to develop integrated individual tree biomass equation systems,in which above-ground biomass is compatible with below-ground biomass and stem volume,and stem,bark,branches and foliage biomass are additive to above-ground biomass,for providing a quantitative basis on accurate estimation of forest biomass. [Method]Based on the mensuration data of above-and below-ground biomass from 230 and78 destructive sample trees of Picea spp. in Xinjiang,respectively,one-and two-variable integrated biomass systems with compatibility and additivity,including above-and below-ground biomass,component biomass,and stem volume,were developed using error-in-variable simultaneous equations approach and dummy variable modeling approach,and the impact of region on estimation of biomass and volume was analyzed. [Result]The mean prediction errors( MPEs) of above-ground biomass equations in the developed one-and two-variable integrated biomass systems for Picea spp. in Xinjiang were less than 7%,the MPEs of components biomass equations were about 10%,and the MPEs of below-ground biomass equations were less than 15%,which could meet the need of precision re-quirements from relevant regulation. One-variable equations were better than two-variable equations for estimation of biomass except for stem and bark biomass. Both proportion control and algebraic control methods could ensure the compatibility between above-ground biomass and component biomass,and the difference between estimates of models from the two methods was not significant. [Conclusion]Integrating dummy variable into error-in-variable simultaneous equations is a practical approach,which can simultaneously develop a system even though the numbers of above-and below-ground biomass observations are very different,and ensure not only the compatibility between above-and below-ground biomass and stem volume,but also the additivity between above-ground biomass and component biomass. For estimation of above-and below-ground biomass,and stem volume,the dummy variable models are better than population average models.
[Objective] The purpose of this study is to develop integrated individual tree biomass equation systems,in which above-ground biomass is compatible with below-ground biomass and stem volume,and stem,bark,branches and foliage biomass are additive to above-ground biomass,for providing a quantitative basis on accurate estimation of forest biomass. [Method]Based on the mensuration data of above-and below-ground biomass from 230 and78 destructive sample trees of Picea spp. in Xinjiang,respectively,one-and two-variable integrated biomass systems with compatibility and additivity,including above-and below-ground biomass,component biomass,and stem volume,were developed using error-in-variable simultaneous equations approach and dummy variable modeling approach,and the impact of region on estimation of biomass and volume was analyzed. [Result]The mean prediction errors( MPEs) of above-ground biomass equations in the developed one-and two-variable integrated biomass systems for Picea spp. in Xinjiang were less than 7%,the MPEs of components biomass equations were about 10%,and the MPEs of below-ground biomass equations were less than 15%,which could meet the need of precision re-quirements from relevant regulation. One-variable equations were better than two-variable equations for estimation of biomass except for stem and bark biomass. Both proportion control and algebraic control methods could ensure the compatibility between above-ground biomass and component biomass,and the difference between estimates of models from the two methods was not significant. [Conclusion]Integrating dummy variable into error-in-variable simultaneous equations is a practical approach,which can simultaneously develop a system even though the numbers of above-and below-ground biomass observations are very different,and ensure not only the compatibility between above-and below-ground biomass and stem volume,but also the additivity between above-ground biomass and component biomass. For estimation of above-and below-ground biomass,and stem volume,the dummy variable models are better than population average models.
引文
[1] Somogyi Z,Cienciala E,MkipR,et al. Indirect methods of large-scale forest biomass estimation[J]. European Journal of Forest Research,2007,126(2):197-207.
[2]曾伟生,陈新云,蒲莹,等.基于国家森林资源清查数据的不同生物量和碳储量估计方法的对比分析[J].林业科学研究,2018,31(1):66-71.
[3]Ter-Mikaelian M T,Korzukhin M D. Biomass equations for sixty-five north American tree species[J]. Forest Ecology and Management,1997,97(1):1-24.
[4]Jenkins J C,Chojnacky D C,Heath L S,et al. National-scale biomass estimators for United States tree species[J]. Forest Science,2003,49(1):12-35.
[5]Zianis D,Muukkonen P,MkipR,et al. Biomass and stem volume equations for tree species in Europe[J]. Silva Fennica,2005,4(4):1-63.
[6] Snorrason A,Einarsson S F. Single-tree biomass and stem volume functions for eleven tree species used in Icelandic forestry[J]. Icelandic Agricultural Sciences,2006,19:15-24.
[7]Muukkonen P. Generalized allometric volume and biomass equations for some tree species in Europe[J]. European Journal of Forest Research,2007,126(2):157-166.
[8]Návar J. Allometric equations for tree species and carbon stocks for forests of northwestern Mexico[J]. Forest Ecology and Management,2009,257(2):427-434.
[9]Fayolle A,Doucet J L,Gillet J F,et al. Tree allometry in Central Africa:Testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks[J]. Forest Ecology and Management,2013,305(4):29-37.
[10]Zeng W S. Development of monitoring and assessment of forest biomass and carbon storage in China[J]. Forest Ecosystems,2014,1(1):1-10.
[11]Adrien D N,Nicolas P,Adeline F,et al. Tree allometry for estimation of carbon stocks in African tropical forests[J]. Forestry,2016:89(4):1-10.
[12]曾伟生.基于木材密度的34个树种组一元立木生物量模型建立[J].林业资源管理,2017(6):33-38.
[13]陈传国,朱俊凤.东北主要林木生物量手册[M].北京:中国林业出版社. 1989.
[14]骆期邦,曾伟生,贺东北,等.立木地上部分生物量模型的建立及其应用研究[J].自然资源学报,1999,14(3):271-277.
[15]唐守正,张会儒,胥辉.相容性生物量模型的建立及其估计方法研究[J].林业科学,2000,36(专刊1):19-27.
[16]Parresol B R. Additivity of nonlinear biomass equations[J]. Canadian Journal of Forest Research,2001,31(5):865-878.
[17]Zeng W S,Zhang H R,Tang S Z. Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales—a case study for Masson pine(Pinus massoniana)in southern China[J]. Canadian Journal of Forest Research,2011,41(7):1547-1554.
[18]董利虎,李凤日,贾炜炜,等.含度量误差的黑龙江省主要树种生物量相容性模型[J].应用生态学报,2011,22(10):2653-2661.
[19]Zeng W S,Tang S Z. Modeling compatible single-tree aboveground biomass equations of Masson pine(Pinus massoniana)in southern China[J]. Journal of Forestry Research,2012,23(4):593-598.
[20]董利虎,李凤日,贾炜炜.东北林区天然白桦相容性生物量模型[J].林业科学,2013,49(7):75-85.
[21]曾鸣,聂祥永,曾伟生.中国杉木相容性立木材积和地上生物量方程[J].林业科学,2013,49(10):74-79.
[22]Dong L H,Zhang L J,Li F R. A compatible system of biomass equations for three conifer species in Northeast,China[J]. Forest Ecology and Management,2014,329(5):306-317.
[23] Zeng W S. Integrated individual tree biomass simultaneous equations for two larch species in northeastern and northern China[J].Scandinavian Journal of Forest Research,2015,30(7):594-604.
[24]董利虎,李凤日,宋玉文.东北林区4个天然针叶树种单木生物量模型误差结构及可加性模型[J].应用生态学报,2015,26(3):704-714.
[25]Dong L H,Zhang L J,Li F R. Developing two additive biomass equations for three coniferous plantation species in northeast China[J]. Forests,2016,7,136; doi:10. 3390/f7070136.
[26]Zeng WS,Zhang LJ,Chen XY,et al. Construction of compatible and additive individual-tree biomass models for Pinus tabulaeformis in China[J]. Canadian Journal of Forest Research,2017,47(4):467-475.
[27]曾伟生,唐守正.利用度量误差模型方法建立相容性立木生物量方程系统[J].林业科学研究,2010,23(6):797-803.
[28]符利勇,雷渊才,孙伟,等.不同林分起源的相容性生物量模型构建[J].生态学报,2014,34(6):1461-1470.
[29]Zeng W S. Using nonlinear mixed model and dummy variable model approaches to construct origin-based single tree biomass equations[J]. Trees-Structure and Function,2015,29(1):275-283.
[30]曾伟生,唐守正,夏忠胜,等.利用线性混合模型和哑变量模型方法建立贵州省通用性生物量方程[J].林业科学研究,2011,24(3):285-291.
[31]国家林业局.中国森林资源报告(2009-2013)[M].北京:中国林业出版社. 2014.
[32]国家林业局.立木生物量建模样本采集技术规程(LY/T 2259-2014)[S].北京:中国标准出版社. 2015.
[33]国家林业局.立木生物量建模方法技术规程(LY/T 2258-2014)[S].北京:中国标准出版社. 2015.
[34]曾伟生,唐守正.非线性模型对数回归的偏差校正及与加权回归的对比分析[J].林业科学研究,2011,24(2):137-143.
[35]曾伟生.加权回归估计中不同权函数的对比分析[J].林业资源管理,2013(5):55-61.
[36]Wang X P,Fang J Y,Zhu B A. Forest biomass and root-shoot allocation in northeast China[J]. Forest Ecology and Management,2008,255(12):4007-4020.
[37]曾伟生,唐守正.东北落叶松和南方马尾松地下生物量模型研建[J].北京林业大学学报,2011,33(2):1-6.
[38]Mugasha W A,Eid T,Bollandsas O M,et al. Allometric models for prediction of above-and belowground biomass of trees in the miombo woodlands of Tanzania[J]. Forest Ecology and Management,2013,310:87-101.
[39]曾伟生,姚顺彬,肖前辉.中国湿地松立木生物量方程研建[J].中南林业科技大学学报,2015,35(1):8-13.
[40]Crecente-Campo F,Soares P,ToméM,et al. Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observa-tions[J]. Forest Ecology and Management,2010,260(11):1965-1974.
[41]Fu L,Lei Y,Wang G,et al. Comparison of seemingly unrelated regressions with errors-invariables models for developing a system of nonlinear additive biomass equations[J]. Trees,2016,30(3):1-19.
[42]唐守正,郎奎建,李海奎.统计和生物数学模型计算(For Stat教程)[M].北京:科学出版社. 2008.
[43]Parresol B R. Assessing tree and stand biomass:a review with examples and critical comparisons[J]. Forest Science,1999,45(4):573-593.
[44]曾伟生,唐守正.立木生物量模型的优度评价和精度分析[J].林业科学,2011,47(11):106-113.
[45]Meng S X,Huang S,Lieffers V J,et al. Wind speed and crown class influence the height-diameter relationship of lodgepole pine:nonlinear mixed effects modeling[J]. Forest Ecology and Management,2008,256(4):570-577.
[46]Zeng W S,Duo H R,Lei X D,et al. Individual tree biomass and growth models sensitive to climate variables for Larix spp. in China[J]. European Journal of Forest Research,2017,136(2):233-249.
[47]陈振雄,甘世书,贺东北.云南省云杉立木生物量模型研建[J].中南林业调查规划,2011,30(4):56-61.
[48]国家林业局.立木生物量模型及碳计量参数—云杉(LY/T2655-2016)[S].北京:中国标准出版社. 2017.
[49]曾伟生,唐守正.一个新的通用性相对生长生物量模型[J].林业科学,2012,48(1):48-52.
[2]曾伟生,陈新云,蒲莹,等.基于国家森林资源清查数据的不同生物量和碳储量估计方法的对比分析[J].林业科学研究,2018,31(1):66-71.
[3]Ter-Mikaelian M T,Korzukhin M D. Biomass equations for sixty-five north American tree species[J]. Forest Ecology and Management,1997,97(1):1-24.
[4]Jenkins J C,Chojnacky D C,Heath L S,et al. National-scale biomass estimators for United States tree species[J]. Forest Science,2003,49(1):12-35.
[5]Zianis D,Muukkonen P,MkipR,et al. Biomass and stem volume equations for tree species in Europe[J]. Silva Fennica,2005,4(4):1-63.
[6] Snorrason A,Einarsson S F. Single-tree biomass and stem volume functions for eleven tree species used in Icelandic forestry[J]. Icelandic Agricultural Sciences,2006,19:15-24.
[7]Muukkonen P. Generalized allometric volume and biomass equations for some tree species in Europe[J]. European Journal of Forest Research,2007,126(2):157-166.
[8]Návar J. Allometric equations for tree species and carbon stocks for forests of northwestern Mexico[J]. Forest Ecology and Management,2009,257(2):427-434.
[9]Fayolle A,Doucet J L,Gillet J F,et al. Tree allometry in Central Africa:Testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks[J]. Forest Ecology and Management,2013,305(4):29-37.
[10]Zeng W S. Development of monitoring and assessment of forest biomass and carbon storage in China[J]. Forest Ecosystems,2014,1(1):1-10.
[11]Adrien D N,Nicolas P,Adeline F,et al. Tree allometry for estimation of carbon stocks in African tropical forests[J]. Forestry,2016:89(4):1-10.
[12]曾伟生.基于木材密度的34个树种组一元立木生物量模型建立[J].林业资源管理,2017(6):33-38.
[13]陈传国,朱俊凤.东北主要林木生物量手册[M].北京:中国林业出版社. 1989.
[14]骆期邦,曾伟生,贺东北,等.立木地上部分生物量模型的建立及其应用研究[J].自然资源学报,1999,14(3):271-277.
[15]唐守正,张会儒,胥辉.相容性生物量模型的建立及其估计方法研究[J].林业科学,2000,36(专刊1):19-27.
[16]Parresol B R. Additivity of nonlinear biomass equations[J]. Canadian Journal of Forest Research,2001,31(5):865-878.
[17]Zeng W S,Zhang H R,Tang S Z. Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales—a case study for Masson pine(Pinus massoniana)in southern China[J]. Canadian Journal of Forest Research,2011,41(7):1547-1554.
[18]董利虎,李凤日,贾炜炜,等.含度量误差的黑龙江省主要树种生物量相容性模型[J].应用生态学报,2011,22(10):2653-2661.
[19]Zeng W S,Tang S Z. Modeling compatible single-tree aboveground biomass equations of Masson pine(Pinus massoniana)in southern China[J]. Journal of Forestry Research,2012,23(4):593-598.
[20]董利虎,李凤日,贾炜炜.东北林区天然白桦相容性生物量模型[J].林业科学,2013,49(7):75-85.
[21]曾鸣,聂祥永,曾伟生.中国杉木相容性立木材积和地上生物量方程[J].林业科学,2013,49(10):74-79.
[22]Dong L H,Zhang L J,Li F R. A compatible system of biomass equations for three conifer species in Northeast,China[J]. Forest Ecology and Management,2014,329(5):306-317.
[23] Zeng W S. Integrated individual tree biomass simultaneous equations for two larch species in northeastern and northern China[J].Scandinavian Journal of Forest Research,2015,30(7):594-604.
[24]董利虎,李凤日,宋玉文.东北林区4个天然针叶树种单木生物量模型误差结构及可加性模型[J].应用生态学报,2015,26(3):704-714.
[25]Dong L H,Zhang L J,Li F R. Developing two additive biomass equations for three coniferous plantation species in northeast China[J]. Forests,2016,7,136; doi:10. 3390/f7070136.
[26]Zeng WS,Zhang LJ,Chen XY,et al. Construction of compatible and additive individual-tree biomass models for Pinus tabulaeformis in China[J]. Canadian Journal of Forest Research,2017,47(4):467-475.
[27]曾伟生,唐守正.利用度量误差模型方法建立相容性立木生物量方程系统[J].林业科学研究,2010,23(6):797-803.
[28]符利勇,雷渊才,孙伟,等.不同林分起源的相容性生物量模型构建[J].生态学报,2014,34(6):1461-1470.
[29]Zeng W S. Using nonlinear mixed model and dummy variable model approaches to construct origin-based single tree biomass equations[J]. Trees-Structure and Function,2015,29(1):275-283.
[30]曾伟生,唐守正,夏忠胜,等.利用线性混合模型和哑变量模型方法建立贵州省通用性生物量方程[J].林业科学研究,2011,24(3):285-291.
[31]国家林业局.中国森林资源报告(2009-2013)[M].北京:中国林业出版社. 2014.
[32]国家林业局.立木生物量建模样本采集技术规程(LY/T 2259-2014)[S].北京:中国标准出版社. 2015.
[33]国家林业局.立木生物量建模方法技术规程(LY/T 2258-2014)[S].北京:中国标准出版社. 2015.
[34]曾伟生,唐守正.非线性模型对数回归的偏差校正及与加权回归的对比分析[J].林业科学研究,2011,24(2):137-143.
[35]曾伟生.加权回归估计中不同权函数的对比分析[J].林业资源管理,2013(5):55-61.
[36]Wang X P,Fang J Y,Zhu B A. Forest biomass and root-shoot allocation in northeast China[J]. Forest Ecology and Management,2008,255(12):4007-4020.
[37]曾伟生,唐守正.东北落叶松和南方马尾松地下生物量模型研建[J].北京林业大学学报,2011,33(2):1-6.
[38]Mugasha W A,Eid T,Bollandsas O M,et al. Allometric models for prediction of above-and belowground biomass of trees in the miombo woodlands of Tanzania[J]. Forest Ecology and Management,2013,310:87-101.
[39]曾伟生,姚顺彬,肖前辉.中国湿地松立木生物量方程研建[J].中南林业科技大学学报,2015,35(1):8-13.
[40]Crecente-Campo F,Soares P,ToméM,et al. Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observa-tions[J]. Forest Ecology and Management,2010,260(11):1965-1974.
[41]Fu L,Lei Y,Wang G,et al. Comparison of seemingly unrelated regressions with errors-invariables models for developing a system of nonlinear additive biomass equations[J]. Trees,2016,30(3):1-19.
[42]唐守正,郎奎建,李海奎.统计和生物数学模型计算(For Stat教程)[M].北京:科学出版社. 2008.
[43]Parresol B R. Assessing tree and stand biomass:a review with examples and critical comparisons[J]. Forest Science,1999,45(4):573-593.
[44]曾伟生,唐守正.立木生物量模型的优度评价和精度分析[J].林业科学,2011,47(11):106-113.
[45]Meng S X,Huang S,Lieffers V J,et al. Wind speed and crown class influence the height-diameter relationship of lodgepole pine:nonlinear mixed effects modeling[J]. Forest Ecology and Management,2008,256(4):570-577.
[46]Zeng W S,Duo H R,Lei X D,et al. Individual tree biomass and growth models sensitive to climate variables for Larix spp. in China[J]. European Journal of Forest Research,2017,136(2):233-249.
[47]陈振雄,甘世书,贺东北.云南省云杉立木生物量模型研建[J].中南林业调查规划,2011,30(4):56-61.
[48]国家林业局.立木生物量模型及碳计量参数—云杉(LY/T2655-2016)[S].北京:中国标准出版社. 2017.
[49]曾伟生,唐守正.一个新的通用性相对生长生物量模型[J].林业科学,2012,48(1):48-52.