广东主要乡土阔叶树种含年龄和胸径的单木生物量模型
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摘要
【目的】建立樟树、木荷和枫香3个树种以胸径、年龄为自变量的单木地上生物量模型,为精准估计森林生物量和碳储量变化规律提供理论、实践支撑和技术支持。【方法】基于每个树种按10个径阶均匀分配的90株伐倒木数据(3个树种共270株伐倒木),使用破坏性试验和树干解析分别获取生物量和年龄,采用哑变量方法区分起源,分别建立和比较3个树种不同起源(天然林和人工林)含胸径、年龄和2种常用的含胸径、树高单木地上生物量模型,并通过联立方程组总量控制法解决地上部分各组分(干材、树皮、树枝、树叶)的模型相容性问题。【结果】3个树种3个地上生物量模型修正的确定系数(R■)在0.89～0.94之间,使用含胸径、年龄的生物量模型(1)估计地上生物量是可行的,模型具有良好的估计效果且方便使用。增加哑变量后,3个树种3个地上生物量模型的R■均达0.90以上,模型(4)可以进一步提高模型精度,细化模型应用条件;基于B-D-T的相容性生物量模型系统(7)3个树种树干生物量模型的R■在0.90～0.95之间,树皮生物量模型的R■在0.84～0.94之间,树枝生物量模型的R■在0.73～0.91之间,树叶生物量模型的R■在0.63～0.75之间;构建含有哑变量的B-D-T相容性生物量模型系统(8),树干生物量模型的R■在0.88～0.97之间,树皮生物量模型的R■在0.82～0.93之间,树枝生物量模型的R■在0.84～0.90之间,树叶生物量模型的R■在0.62～0.69之间,表明含胸径、年龄的生物量模型比含胸径、树高的生物量模型效果更好,满足估计需求。【结论】含胸径、年龄的单木地上生物量模型(1)和分起源的单木地上生物量哑变量模型(4)拟合精度均高于2种常用的含胸径、树高的单木地上生物量模型(2)和(3)以及分起源的单木地上生物量模型(5)和(6),含哑变量的非线性联立方程组(8)比B-D-T模型系统(7)精度更高,同时含胸径、年龄的哑变量非线性联立方程组(8)精度指标也优于含胸径、树高的哑变量联立方程组(9)和(10),联立方程组(8)不仅可保证不同起源各分量生物量之间的相容性,还能得到更优化的参数估计。含胸径、年龄的单木生物量模型(1)和考虑起源的地上生物量模型(4)、模型系统(7)中的含胸径、年龄的地上各组分生物量相容性方程组以及考虑起源的地上各组分生物量相容性方程组(8)比2种常用的含胸径、树高的地上生物量模型拟合精度高,实践中更适用于人工阔叶林和碳汇造林项目的碳汇计量、监测和评估。
【Objective】 In order to improve the monitoring efficiency and precisely calculate forest carbon sequestration in afforestation projects in Guangdong Province, this study selected major broad-leaved tree species in Guangdong, such as Cinnamomum camphora, Schima superba and Liquidambar formosana,and established aboveground biomass models with age and diameter as independent variables.【Method】 All 270 sample trees with 90 sample trees for each tree species were obtained according to 10 diameter classes during the process of modeling, using destructive experiments and stem analysis method to obtain tree biomass and age data. Using the dummy variable method to distinguish the different origins(natural forests and plantations),single tree biomass models with the diameter of breast height(DBH) and age were established for the three species. Also the aboveground biomass components(stem wood and bark, branches, foliage)were established for natural forests and plantations based on dummy variable method. The additivity or compatibility of the components of the aboveground parts was solved by the nonlinear simultaneous equation systems.【Result】 The determination coefficients R■ of the three above-ground biomass models of the three tree species are between 0.89 and 0.94. It is feasible to use biomass model(1) with DBH and age to estimate tree above-ground biomass. Model(1) has good estimation result and is easy to use. After adding the dummy variable, the three above-ground biomass models R~2_(adj) of the three tree species all reached 0.90 or more. Model(4) can further improve biomass estimate accuracy and refine the model application conditions; for the B-D-T based compatible biomass model system(7) there are good performances in 3 tree species. R~2_(adj) of stem biomass model is between 0.90 and 0.95, R~2_(adj) of bark biomass model is between 0.84 and 0.94, and R~2_(adj) of branch biomass model is between 0.73 and 0.91. The R~2_(adj) of the leaf biomass model is between 0.63 and 0.75, and a compatible B-D-T biomass model system(8) with dummy variables is constructed. The stem biomass equation R~2_(adj) is between 0.88 and 0.97, and the bark biomass equation R~2_(adj) is between 0.82 and 0.93, the branch biomass equation R~2_(adj) is between 0.84 and 0.90, and the leaf biomass equation R~2_(adj) is between 0.62 and 0.69. The result further indicate that the biomass model with DBH and age is more effective than the DBH and tree height biomass models.【Conclusion】 This study established the aboveground biomass model with diameter and age for three major native broad-leaved tree species in Guangdong Province. The biomass compatibility model of each component in the aboveground biomass part was established by simultaneous equations. The single tree aboveground biomass model with the DBH and age model(1) have higher fitting accuracy than the commonly used two DBH and height model(2) and(3) and the model(4) with dummy variable also have higher fitting accuracy than model(5) and(6). The precision of nonlinear simultaneous equations(8) with dummy variables is higher than that of model system(7) with B-D-T. Meanwhile, the precision index of the dummy variable simultaneous equations(8) with the DBH and age variable is also better than that of the dummy variables simultaneous equations(9) and(10) with the DBH high variable. The simultaneous equations(8) not only ensure the compatibility between the aboveground biomass of each component, but also obtain more optimal parameter estimates from different origins. The single-wood biomass model with DBH and age(1) and the aboveground biomass model considering the origin(4), the model system(7), the biomass compatibility equations for the above-ground components with the DBH and age, and considerations the above-ground biomass compatibility equations(8) have higher fitting precision than the commonly used above-ground biomass models of DBH and tree height, and are more suitable for artificial broad-leaved forest and carbon sink afforestation projects(arbon sink measurement, monitoring and evaluation)in practice.
【Objective】 In order to improve the monitoring efficiency and precisely calculate forest carbon sequestration in afforestation projects in Guangdong Province, this study selected major broad-leaved tree species in Guangdong, such as Cinnamomum camphora, Schima superba and Liquidambar formosana,and established aboveground biomass models with age and diameter as independent variables.【Method】 All 270 sample trees with 90 sample trees for each tree species were obtained according to 10 diameter classes during the process of modeling, using destructive experiments and stem analysis method to obtain tree biomass and age data. Using the dummy variable method to distinguish the different origins(natural forests and plantations),single tree biomass models with the diameter of breast height(DBH) and age were established for the three species. Also the aboveground biomass components(stem wood and bark, branches, foliage)were established for natural forests and plantations based on dummy variable method. The additivity or compatibility of the components of the aboveground parts was solved by the nonlinear simultaneous equation systems.【Result】 The determination coefficients R■ of the three above-ground biomass models of the three tree species are between 0.89 and 0.94. It is feasible to use biomass model(1) with DBH and age to estimate tree above-ground biomass. Model(1) has good estimation result and is easy to use. After adding the dummy variable, the three above-ground biomass models R~2_(adj) of the three tree species all reached 0.90 or more. Model(4) can further improve biomass estimate accuracy and refine the model application conditions; for the B-D-T based compatible biomass model system(7) there are good performances in 3 tree species. R~2_(adj) of stem biomass model is between 0.90 and 0.95, R~2_(adj) of bark biomass model is between 0.84 and 0.94, and R~2_(adj) of branch biomass model is between 0.73 and 0.91. The R~2_(adj) of the leaf biomass model is between 0.63 and 0.75, and a compatible B-D-T biomass model system(8) with dummy variables is constructed. The stem biomass equation R~2_(adj) is between 0.88 and 0.97, and the bark biomass equation R~2_(adj) is between 0.82 and 0.93, the branch biomass equation R~2_(adj) is between 0.84 and 0.90, and the leaf biomass equation R~2_(adj) is between 0.62 and 0.69. The result further indicate that the biomass model with DBH and age is more effective than the DBH and tree height biomass models.【Conclusion】 This study established the aboveground biomass model with diameter and age for three major native broad-leaved tree species in Guangdong Province. The biomass compatibility model of each component in the aboveground biomass part was established by simultaneous equations. The single tree aboveground biomass model with the DBH and age model(1) have higher fitting accuracy than the commonly used two DBH and height model(2) and(3) and the model(4) with dummy variable also have higher fitting accuracy than model(5) and(6). The precision of nonlinear simultaneous equations(8) with dummy variables is higher than that of model system(7) with B-D-T. Meanwhile, the precision index of the dummy variable simultaneous equations(8) with the DBH and age variable is also better than that of the dummy variables simultaneous equations(9) and(10) with the DBH high variable. The simultaneous equations(8) not only ensure the compatibility between the aboveground biomass of each component, but also obtain more optimal parameter estimates from different origins. The single-wood biomass model with DBH and age(1) and the aboveground biomass model considering the origin(4), the model system(7), the biomass compatibility equations for the above-ground components with the DBH and age, and considerations the above-ground biomass compatibility equations(8) have higher fitting precision than the commonly used above-ground biomass models of DBH and tree height, and are more suitable for artificial broad-leaved forest and carbon sink afforestation projects(arbon sink measurement, monitoring and evaluation)in practice.
引文
符利勇, 雷渊才, 孙伟, 等. 2014. 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.(Fu L Y, Lei Y C, Sun W, et al. 2014. Development of compatibel biomass models for trees from different stand oringin. Acta Ecologica Sinica, 34(6): 1461-1470. [ in Chinese])
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.(Li H K, Lei Y C, Zeng W S. 2011. Forest carbon storage in China estimated using forestry inventory data. Scientia Silvae Sinicae, 47(7): 7-12. [ in Chinese])
林丽平, 徐期瑚, 罗勇, 等. 2018. 广东主要乡土阔叶树种单木生长模型研究. 林业与环境科学, 34(3): 14-22.(Lin L P, Xu Q H, Luo Y, et al. 2018. Study on the growth models for main broadleaved tree species in Guangdong Province. Forestry and Environmental Science, 34(3): 14-22. [ in Chinese])
刘茂秀, 史军辉, 王新英, 等. 2016. 塔河流域天然胡杨林不同林龄地上生物量及碳储量. 水土保持通报, 36(5): 326-332.(Liu M X, Shi J H, Wang X Y, et al. 2016. Aboveground biomass and carbon storage of Populus euphratica plantation with different ages in Tahe river basin. Bulletin of Soil and Water Conservation, 36(5): 326-332. [ in Chinese])
施文涛, 谢昕云, 刘西军, 等. 2015. 安徽大别山区杉木人工林乔木层生物量模型及碳贮量. 长江流域资源与环境, 24(5): 758-764.(Shi W T, Xie X Y, Liu X J, et al. 2015. Biomass model and carbon storage of Chinese fir plantation in Dabieshan Mountains in Anhui. Resources and Environment in the Yangtze Basin, 24(5): 758-764. [ in Chinese])
徐期瑚, 薛春泉, 罗勇, 等. 2018. 广东主要乡土阔叶树种生物量模型研建思路与框架. 林业调查规划, 43(3): 16-21.(Xu Q H, Xue C Q, Luo Y, et al. 2018. Research thought and structure of biomass models for main native broad-leaved tree species in Guangdong. Forest Inventory and Planning, 43(3): 16-21. [ in Chinese])
曾伟生, 唐守正. 2010. 利用度量误差模型方法建立相容性立木生物量方程系统. 林业科学研究, 23(6): 797 - 802.(Zeng W S, Tang S Z. 2010. Using measurement error modeling method to establish compatible single-tree biomass equations system. Forest Research, 23(6): 797 - 802. [ in Chinese])
曾伟生, 唐守正, 夏忠胜, 等. 2011. 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. 林业科学研究, 24(3): 285-291.(Zeng W S, Tang S Z, Xia Z S, et al. 2011. Using linear mixed model and dummy variable model approaches to construct generalized single-tree biomass equations in Guizhou. Forest Research, 24(3): 285 -291. [ in Chinese])
赵菡, 雷渊才, 符利勇. 2017. 江西省不同立地等级的马尾松林生物量估计和不确定性度量. 林业科学, 53(8): 81-93.(Zhao H, Lei Y C, Fu L Y. 2017. Biomass and uncertainty estimates of Pinus massoniana forest for different site classes in Jiangxi Province. Scientia Silvae Sinicae, 53(8): 81-93. [ in Chinese])
周国模. 2006. 毛竹林生态系统中碳储量、固定及其分配与分布的研究. 杭州: 浙江大学博士学位论文.(Zhou G M. 2006. Carbon storage, fixation, distribution and distribution in Mao Zhu lin ecosystem. Hangzhou: PhD thesis of Zhejiang University. [in Chinese])
Barrio-Anta M, Balboa-Murias M A, Castedo-Dorado F, et al. 2006. An ecoregional model for estimating volume, biomass and carbon pools in maritime pine stands in Galicia(northwestern Spain). Forest Ecology and Management, 223(1/3): 20-34.
Brown S. 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3): 363-372.
Cao M, Woodward F I. 1998. Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their responses to climate change. Global Change Biology, 4(2):185-198.
Fehrmann L, Kleinn C. 2006. General consideration about the use of allometric equations for biomass estimation on the example of Norway spruce in central Europe. Forest Ecology and Management, 236(2): 412-421.
Fu Y, Lei Y C, Zeng W S, et al. 2017. Uncertainty assessment in aboveground biomass estimationat the regional scale using a new method considering both sampling error and model error, Canadian Journal of Forest Research, 47(8): 1095-1103.
Helmisaari H S, Makkonen K, Kellomaki S, et al. 2002. Below- and above-ground biomass, production and nitrogen use in Scots pine stands in eastern Finland. Forest Ecology and Management, 165(1): 317-326.
Kitterge J. 1944. Estimation of amount of foliage of trees and shrubs. Journal of Forest, 42(12): 905-912.
Kramer P J. 1981. carbon dioxide concentration, photosynthesis and dry matter production. Bioscience, 31(1): 29-33.
Kurz W A, Beukema S J, Apps M J. 1996. Estimation of root biomass and dynamics for the carbon budget model of the Canadian forest sector. Canadian Journal of Forest Research, 26(11): 1973-1979.
Lal R. 2005. Forest soils and carbon sequestration. Forest Ecology and Management, 220(1): 242-258.
Lehtonen A, Cienciala E, Tatarinov F, et al. 2007. Uncertainty estimation of biomass expansion factors for Norway spruce in the Czech Republic. Annals of Forest Science, 64(2): 133-140.
Mc Roberts R E, Westfall J A. 2014. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science, 60(1): 34-42.
Peichl M, Arain M A. 2007. Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. Forest Ecology and Management, 253(1/3): 68-80.
Ruard G A, martin G L, Bockheim J G. 1987. Comparison of constant and variable allometric ratios for estimating Populus tremuloides biomass. Forest Science, 33(2): 294 -300.
Sadiq R A, Smith V G. 1983. Estimation of individual tree volumes with age and diameter. Canadian Journal of Forest Research, 13(1): 32-39.
Saint-André L, M’Bou A T, Mabiala A, et al. 2005. Age-related equations for above-and below-ground biomass of a Eucalyptus hybrid in Congo. Forest Ecology and Management, 205(1): 199-214.
Sundquist E T. 1993. The global carbon dioxide budget. Science, 259(5097): 935-941.
Vogt K. 1991. Carbon budgets of temperate forest ecosystems. Tree Physiology, 9(1/2): 69-86.
Waring R W, Running S W. 1998. Forest ecosystems analysis at multiple scales. Academic Press, New York, 37, 697.
Woodwell G M, Whittacker R H, Reiners W A, et al. 1978. The biota and the world carbon budget. Science, 199(4325): 141-146.
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.(Li H K, Lei Y C, Zeng W S. 2011. Forest carbon storage in China estimated using forestry inventory data. Scientia Silvae Sinicae, 47(7): 7-12. [ in Chinese])
林丽平, 徐期瑚, 罗勇, 等. 2018. 广东主要乡土阔叶树种单木生长模型研究. 林业与环境科学, 34(3): 14-22.(Lin L P, Xu Q H, Luo Y, et al. 2018. Study on the growth models for main broadleaved tree species in Guangdong Province. Forestry and Environmental Science, 34(3): 14-22. [ in Chinese])
刘茂秀, 史军辉, 王新英, 等. 2016. 塔河流域天然胡杨林不同林龄地上生物量及碳储量. 水土保持通报, 36(5): 326-332.(Liu M X, Shi J H, Wang X Y, et al. 2016. Aboveground biomass and carbon storage of Populus euphratica plantation with different ages in Tahe river basin. Bulletin of Soil and Water Conservation, 36(5): 326-332. [ in Chinese])
施文涛, 谢昕云, 刘西军, 等. 2015. 安徽大别山区杉木人工林乔木层生物量模型及碳贮量. 长江流域资源与环境, 24(5): 758-764.(Shi W T, Xie X Y, Liu X J, et al. 2015. Biomass model and carbon storage of Chinese fir plantation in Dabieshan Mountains in Anhui. Resources and Environment in the Yangtze Basin, 24(5): 758-764. [ in Chinese])
徐期瑚, 薛春泉, 罗勇, 等. 2018. 广东主要乡土阔叶树种生物量模型研建思路与框架. 林业调查规划, 43(3): 16-21.(Xu Q H, Xue C Q, Luo Y, et al. 2018. Research thought and structure of biomass models for main native broad-leaved tree species in Guangdong. Forest Inventory and Planning, 43(3): 16-21. [ in Chinese])
曾伟生, 唐守正. 2010. 利用度量误差模型方法建立相容性立木生物量方程系统. 林业科学研究, 23(6): 797 - 802.(Zeng W S, Tang S Z. 2010. Using measurement error modeling method to establish compatible single-tree biomass equations system. Forest Research, 23(6): 797 - 802. [ in Chinese])
曾伟生, 唐守正, 夏忠胜, 等. 2011. 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. 林业科学研究, 24(3): 285-291.(Zeng W S, Tang S Z, Xia Z S, et al. 2011. Using linear mixed model and dummy variable model approaches to construct generalized single-tree biomass equations in Guizhou. Forest Research, 24(3): 285 -291. [ in Chinese])
赵菡, 雷渊才, 符利勇. 2017. 江西省不同立地等级的马尾松林生物量估计和不确定性度量. 林业科学, 53(8): 81-93.(Zhao H, Lei Y C, Fu L Y. 2017. Biomass and uncertainty estimates of Pinus massoniana forest for different site classes in Jiangxi Province. Scientia Silvae Sinicae, 53(8): 81-93. [ in Chinese])
周国模. 2006. 毛竹林生态系统中碳储量、固定及其分配与分布的研究. 杭州: 浙江大学博士学位论文.(Zhou G M. 2006. Carbon storage, fixation, distribution and distribution in Mao Zhu lin ecosystem. Hangzhou: PhD thesis of Zhejiang University. [in Chinese])
Barrio-Anta M, Balboa-Murias M A, Castedo-Dorado F, et al. 2006. An ecoregional model for estimating volume, biomass and carbon pools in maritime pine stands in Galicia(northwestern Spain). Forest Ecology and Management, 223(1/3): 20-34.
Brown S. 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3): 363-372.
Cao M, Woodward F I. 1998. Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their responses to climate change. Global Change Biology, 4(2):185-198.
Fehrmann L, Kleinn C. 2006. General consideration about the use of allometric equations for biomass estimation on the example of Norway spruce in central Europe. Forest Ecology and Management, 236(2): 412-421.
Fu Y, Lei Y C, Zeng W S, et al. 2017. Uncertainty assessment in aboveground biomass estimationat the regional scale using a new method considering both sampling error and model error, Canadian Journal of Forest Research, 47(8): 1095-1103.
Helmisaari H S, Makkonen K, Kellomaki S, et al. 2002. Below- and above-ground biomass, production and nitrogen use in Scots pine stands in eastern Finland. Forest Ecology and Management, 165(1): 317-326.
Kitterge J. 1944. Estimation of amount of foliage of trees and shrubs. Journal of Forest, 42(12): 905-912.
Kramer P J. 1981. carbon dioxide concentration, photosynthesis and dry matter production. Bioscience, 31(1): 29-33.
Kurz W A, Beukema S J, Apps M J. 1996. Estimation of root biomass and dynamics for the carbon budget model of the Canadian forest sector. Canadian Journal of Forest Research, 26(11): 1973-1979.
Lal R. 2005. Forest soils and carbon sequestration. Forest Ecology and Management, 220(1): 242-258.
Lehtonen A, Cienciala E, Tatarinov F, et al. 2007. Uncertainty estimation of biomass expansion factors for Norway spruce in the Czech Republic. Annals of Forest Science, 64(2): 133-140.
Mc Roberts R E, Westfall J A. 2014. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science, 60(1): 34-42.
Peichl M, Arain M A. 2007. Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. Forest Ecology and Management, 253(1/3): 68-80.
Ruard G A, martin G L, Bockheim J G. 1987. Comparison of constant and variable allometric ratios for estimating Populus tremuloides biomass. Forest Science, 33(2): 294 -300.
Sadiq R A, Smith V G. 1983. Estimation of individual tree volumes with age and diameter. Canadian Journal of Forest Research, 13(1): 32-39.
Saint-André L, M’Bou A T, Mabiala A, et al. 2005. Age-related equations for above-and below-ground biomass of a Eucalyptus hybrid in Congo. Forest Ecology and Management, 205(1): 199-214.
Sundquist E T. 1993. The global carbon dioxide budget. Science, 259(5097): 935-941.
Vogt K. 1991. Carbon budgets of temperate forest ecosystems. Tree Physiology, 9(1/2): 69-86.
Waring R W, Running S W. 1998. Forest ecosystems analysis at multiple scales. Academic Press, New York, 37, 697.
Woodwell G M, Whittacker R H, Reiners W A, et al. 1978. The biota and the world carbon budget. Science, 199(4325): 141-146.