两种相容性生物量模型的比较——以广东省3个阔叶树种为例
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摘要
基于广东省樟树、木荷和枫香3个阔叶树种共270株样木的生物量实测数据,采用多元非线性联合估计,按分量相加和总量控制两种方法,分别构建了一元(胸径)和二元(胸径、树高和胸径、冠幅)的相容性生物量模型,比较分析了这两种相容性生物量模型的拟合效果和预估精度。结果表明:(1)分量相加和总量控制构建相容性模型对3个树种拟合效果均较好,预估精度均较高,二者之间差别不大,但总体上分量相加模型拟合效果较优。(2)二元模型拟合效果和预估精度普遍优于一元模型,但加入第二个变量的异同,在各个分量中的表现不一。加入树高因子,显著改进树干和树皮的拟合效果;加入冠幅因子,显著改进树枝、树叶和地上部分的拟合效果。选择胸径和冠幅因子建立二元生物量模型可以获得较高的拟合精度。(3)基于最优独立模型构建的相容性模型拟合和预估效果最优,地上部分总量甚至优于最优独立模型。因此,各分量相加构建的相容性模型略优于总量控制。对于阔叶树种自变量的选取,二元模型加入冠幅因子的精度高于加入树高因子。
Based on biomass data of 270 individuals of three broadleaved tree species in Guangdong,Cinnamomum camphora,Schima superba,and Liquidambar formosana,a multiple nonlinear joint estimation was adopted with components addition and total control methods to separately establish one-variable( diameter at breast height,DBH) and bivariate( DBH and tree height or DBH and canopy width) compatible biomass equations. The fitting goodness and estimated accuracy of compatible equations derived from these two methods were evaluated. The results showed that:( 1) The compatible equations separately established by components addition and total control methods performed well,with no significant difference in estimated accuracy between them.The model of components addition performed slightly better.( 2) Binary equations generally performed better than the unitary equation. Adding the second variable could produce different impacts for tree components. With tree height as the second variable,the binary model improved the fitting effect of stem-wood and stem-bark biomass. With the canopy width as the second variable,the model improved the fitting effect of branch,foliage,and aboveground biomass. For the compatible equation of broadleaved species,using the DBH and canopy width of trees could get better accuracy.( 3) Compatible model based on the second variable respectively from components model performed better,even with higher accuracy for total biomass estimation than individual model. The components addition was slightly better than total control in constructing compatible model. For the selection of second explanatory variable of broadleaved tree species,the binary model accuracy with canopy width factor was higher than that with tree height factor.
Based on biomass data of 270 individuals of three broadleaved tree species in Guangdong,Cinnamomum camphora,Schima superba,and Liquidambar formosana,a multiple nonlinear joint estimation was adopted with components addition and total control methods to separately establish one-variable( diameter at breast height,DBH) and bivariate( DBH and tree height or DBH and canopy width) compatible biomass equations. The fitting goodness and estimated accuracy of compatible equations derived from these two methods were evaluated. The results showed that:( 1) The compatible equations separately established by components addition and total control methods performed well,with no significant difference in estimated accuracy between them.The model of components addition performed slightly better.( 2) Binary equations generally performed better than the unitary equation. Adding the second variable could produce different impacts for tree components. With tree height as the second variable,the binary model improved the fitting effect of stem-wood and stem-bark biomass. With the canopy width as the second variable,the model improved the fitting effect of branch,foliage,and aboveground biomass. For the compatible equation of broadleaved species,using the DBH and canopy width of trees could get better accuracy.( 3) Compatible model based on the second variable respectively from components model performed better,even with higher accuracy for total biomass estimation than individual model. The components addition was slightly better than total control in constructing compatible model. For the selection of second explanatory variable of broadleaved tree species,the binary model accuracy with canopy width factor was higher than that with tree height factor.
引文
蔡兆炜,孙玉军,施鹏程.2014.基于非线性度量误差的杉木相容性生物量模型.东北林业大学学报,42(9):28-32.
董利虎,李凤日,宋玉文.2015.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.应用生态学报,26(3):704-714.
董利虎,李凤日.2016.大兴安岭东部天然落叶松林可加性林分生物量估算模型.林业科学,52(7):13-21.
符利勇,雷渊才,孙伟,等.2014a.不同林分起源的相容性生物量模型构建.生态学报,34(6):1461-1470.
符利勇,雷渊才,曾伟生.2014b.几种相容性生物量模型及估计方法的比较.林业科学,50(6):42-54.
高志雄,王新杰,王廷蓉,等.2015.福建杉木单木生物量分配及相容性模型的应用.南京林业大学学报:自然科学版,39(4):157-162.
黄兴召,孙晓梅,张守攻,等.2014.辽东山区日本落叶松生物量相容性模型的研究.林业科学研究,27(2):142-148.
骆期邦,曾伟生,贺东北,等.1999.立木地上部分生物量模型的建立及其应用研究.自然资源学报,14(3):271-277.
李海奎,雷渊才,曾伟生.2011.基于森林清查资料的中国森林植被碳储量.林业科学,47(7):7-12.
李海奎,赵鹏祥,雷渊才,等.2012.基于森林清查资料的乔木林生物量估算方法的比较.林业科学,48(5):44-52.
彭娓,董利虎,李凤日.2016.基于可加性生物量模型的大兴安岭东部主要林型森林植被碳储量及其分配.应用生态学报,27(12):3749-3758.
冉启香,邓华锋,黄国胜,等.2016.云南松地上生物量模型研究.浙江农林大学学报,33(4):605-611.
郑聪慧.2015.华北地区栓皮栎生长模型的建立(博士学位论文).北京:北京林业大学.
唐守正,郎奎建,李海奎.2009.统计和生物数学模型计算(For Stat教程).北京:科学出版社.
唐守正,李勇,符利勇.2015.生物数学模型的统计学基础.北京:高等教育出版社.
唐守正,张会儒,胥辉.2000.相容性生物量模型的建立及其估计方法研究.林业科学,36(z01):19-27.
王仲锋.2006.森林生物量建模与精度分析(博士学位论文).北京:北京林业大学.
胥辉,刘伟平.2001.相容性生物量模型研究.森林与环境学报,21(1):18-23.
杨众养,薛杨,刘宪钊,等.2015.海南沿海木麻黄可加性生物量模型.东北林业大学学报,43(1):36-40.
国家林业局.2014.LY/T 2258-2014立木生物量建模方法技术规程.北京:技术标准出版社.
张会儒,唐守正,胥辉.1999.生物量模型中的异方差问题.林业资源管理,28(1):46-49.
曾伟生,骆期邦,贺东北.1999a.论加权回归与建模.林业科学,35(5):5-11.
曾伟生,骆期邦,贺东北.1999b.兼容性立木生物量非线性模型研究.生态学杂志,18(4):19-24.
曾伟生,唐守正.2011a.立木生物量方程的优度评价和精度分析.林业科学,47(11):106-113.
曾伟生,唐守正.2011b.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.林业科学研究,24(2):137-143.
曾伟生.2013.加权回归估计中不同权函数的对比分析.林业资源管理,42(5):55-61.
Basuki TM,van Laake PE,Skidmore AK,et al.2009.Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests.Forest Ecology and Management,257:1684-1694.
Berk KN.1984.Validating regression procedures with new data.Technimetrics,26:331-338.
Bi H,Turner J,Lambert MJ.2004.Additive biomass equations for native eucalypt forest trees of temperate Australia.Trees,18:467-479.
Bondlamberty B,Wang C,Gower ST.2002.Aboveground and belowground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba.Canadian Journal of Forest Research,32:1441-1450.
Fang JY,Wang ZM.2001.Forest biomass estimation at regional and global levels,with special reference to China’s forest biomass.Ecological Research,16:587-592.
Jenkins JC,Chojnacky DC,Heath LS,et al.2003.Nationalscale biomass estimators for United States tree species.Forest Science,49:12-35.
Ker MF,Raalte GDV.1981.Tree biomass equations for Abies balsamea and Picea glauca in northwestern New Brunswick.Canadian Journal of Forest Research,11:13-17.
Kozak A,Kozak R.2003.Does cross validation provide additional information in the evaluation of regression models.Canadian Journal of Forest Research,33:976-987.
Ketterings QM,Coe R,van Noordwijk M,et al.2001.Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests.Forest Ecology and Management,146:199-209.
Muukkonen P.2007.Forest Inventory-based Large-scale Forest Biomass and Carbon Budget Assessment:New Enhanced Methods and Use of Remote Sensing for Verification.Helsinki:University of Helsinki.
Meng S,Liu Q,Zhou G,et al.2017.Aboveground tree additive biomass equations for two dominant deciduous tree species in Daxing’anling,northernmost China.Journal of Forest Research,22:1-8.
Parresol BR.1999.Assessing tree and stand biomass:A review with examples and critical comparisons.Forest Science,45:573-593.
Parresol BR.2001.Additivity of nonlinear biomass equations.Canadian Journal of Forest Research,31:865-878.
Riofrío J,Herrero C,Grijalva J,et al.2015.Aboveground tree additive biomass models in Ecuadorian highland agroforestry systems.Biomass&Bioenergy,80:252-259.
Ter-Mikaelian MT,Korzukhin MD.1997.Biomass equations for sixty-five North American tree species.Forest Ecology and Management,97:1-24.
Tomppo E,Gschwantner T,Lawrence M,et al.2010.National Forest Inventories:Pathways for Common Reporting.Dordrecht:Springer.
Tang S,Li Y,Wang Y.2001.Simultaneous equations,error-invariable models,and model integration in systems ecology.Ecological Modelling,142:285-294.
Tang S,Wang Y.2012.A parameter estimation program for the error-in-variable model.Ecological Modelling,156:225-236.
Wang C.2006.Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests.Forest Ecology and Management,222:9-16.
Zou WT,Zeng WS,Zhang LJ,et al.2015.Modeling crown biomass for four pine species in China.Forests,6:433-449.
董利虎,李凤日,宋玉文.2015.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.应用生态学报,26(3):704-714.
董利虎,李凤日.2016.大兴安岭东部天然落叶松林可加性林分生物量估算模型.林业科学,52(7):13-21.
符利勇,雷渊才,孙伟,等.2014a.不同林分起源的相容性生物量模型构建.生态学报,34(6):1461-1470.
符利勇,雷渊才,曾伟生.2014b.几种相容性生物量模型及估计方法的比较.林业科学,50(6):42-54.
高志雄,王新杰,王廷蓉,等.2015.福建杉木单木生物量分配及相容性模型的应用.南京林业大学学报:自然科学版,39(4):157-162.
黄兴召,孙晓梅,张守攻,等.2014.辽东山区日本落叶松生物量相容性模型的研究.林业科学研究,27(2):142-148.
骆期邦,曾伟生,贺东北,等.1999.立木地上部分生物量模型的建立及其应用研究.自然资源学报,14(3):271-277.
李海奎,雷渊才,曾伟生.2011.基于森林清查资料的中国森林植被碳储量.林业科学,47(7):7-12.
李海奎,赵鹏祥,雷渊才,等.2012.基于森林清查资料的乔木林生物量估算方法的比较.林业科学,48(5):44-52.
彭娓,董利虎,李凤日.2016.基于可加性生物量模型的大兴安岭东部主要林型森林植被碳储量及其分配.应用生态学报,27(12):3749-3758.
冉启香,邓华锋,黄国胜,等.2016.云南松地上生物量模型研究.浙江农林大学学报,33(4):605-611.
郑聪慧.2015.华北地区栓皮栎生长模型的建立(博士学位论文).北京:北京林业大学.
唐守正,郎奎建,李海奎.2009.统计和生物数学模型计算(For Stat教程).北京:科学出版社.
唐守正,李勇,符利勇.2015.生物数学模型的统计学基础.北京:高等教育出版社.
唐守正,张会儒,胥辉.2000.相容性生物量模型的建立及其估计方法研究.林业科学,36(z01):19-27.
王仲锋.2006.森林生物量建模与精度分析(博士学位论文).北京:北京林业大学.
胥辉,刘伟平.2001.相容性生物量模型研究.森林与环境学报,21(1):18-23.
杨众养,薛杨,刘宪钊,等.2015.海南沿海木麻黄可加性生物量模型.东北林业大学学报,43(1):36-40.
国家林业局.2014.LY/T 2258-2014立木生物量建模方法技术规程.北京:技术标准出版社.
张会儒,唐守正,胥辉.1999.生物量模型中的异方差问题.林业资源管理,28(1):46-49.
曾伟生,骆期邦,贺东北.1999a.论加权回归与建模.林业科学,35(5):5-11.
曾伟生,骆期邦,贺东北.1999b.兼容性立木生物量非线性模型研究.生态学杂志,18(4):19-24.
曾伟生,唐守正.2011a.立木生物量方程的优度评价和精度分析.林业科学,47(11):106-113.
曾伟生,唐守正.2011b.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.林业科学研究,24(2):137-143.
曾伟生.2013.加权回归估计中不同权函数的对比分析.林业资源管理,42(5):55-61.
Basuki TM,van Laake PE,Skidmore AK,et al.2009.Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests.Forest Ecology and Management,257:1684-1694.
Berk KN.1984.Validating regression procedures with new data.Technimetrics,26:331-338.
Bi H,Turner J,Lambert MJ.2004.Additive biomass equations for native eucalypt forest trees of temperate Australia.Trees,18:467-479.
Bondlamberty B,Wang C,Gower ST.2002.Aboveground and belowground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba.Canadian Journal of Forest Research,32:1441-1450.
Fang JY,Wang ZM.2001.Forest biomass estimation at regional and global levels,with special reference to China’s forest biomass.Ecological Research,16:587-592.
Jenkins JC,Chojnacky DC,Heath LS,et al.2003.Nationalscale biomass estimators for United States tree species.Forest Science,49:12-35.
Ker MF,Raalte GDV.1981.Tree biomass equations for Abies balsamea and Picea glauca in northwestern New Brunswick.Canadian Journal of Forest Research,11:13-17.
Kozak A,Kozak R.2003.Does cross validation provide additional information in the evaluation of regression models.Canadian Journal of Forest Research,33:976-987.
Ketterings QM,Coe R,van Noordwijk M,et al.2001.Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests.Forest Ecology and Management,146:199-209.
Muukkonen P.2007.Forest Inventory-based Large-scale Forest Biomass and Carbon Budget Assessment:New Enhanced Methods and Use of Remote Sensing for Verification.Helsinki:University of Helsinki.
Meng S,Liu Q,Zhou G,et al.2017.Aboveground tree additive biomass equations for two dominant deciduous tree species in Daxing’anling,northernmost China.Journal of Forest Research,22:1-8.
Parresol BR.1999.Assessing tree and stand biomass:A review with examples and critical comparisons.Forest Science,45:573-593.
Parresol BR.2001.Additivity of nonlinear biomass equations.Canadian Journal of Forest Research,31:865-878.
Riofrío J,Herrero C,Grijalva J,et al.2015.Aboveground tree additive biomass models in Ecuadorian highland agroforestry systems.Biomass&Bioenergy,80:252-259.
Ter-Mikaelian MT,Korzukhin MD.1997.Biomass equations for sixty-five North American tree species.Forest Ecology and Management,97:1-24.
Tomppo E,Gschwantner T,Lawrence M,et al.2010.National Forest Inventories:Pathways for Common Reporting.Dordrecht:Springer.
Tang S,Li Y,Wang Y.2001.Simultaneous equations,error-invariable models,and model integration in systems ecology.Ecological Modelling,142:285-294.
Tang S,Wang Y.2012.A parameter estimation program for the error-in-variable model.Ecological Modelling,156:225-236.
Wang C.2006.Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests.Forest Ecology and Management,222:9-16.
Zou WT,Zeng WS,Zhang LJ,et al.2015.Modeling crown biomass for four pine species in China.Forests,6:433-449.