长白落叶松人工林单木和林分水平的相容性生物量模型研究
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摘要
[目的]构建落叶松人工林单木和林分水平的相容性生物量模型,使之既在数据采集区域内能够表征不同水平下的差异程度,又具有较强的通用性。[方法]基于64株长白落叶松人工林样木生物量实测数据和40个每木检尺样地数据,在考虑和未考虑林龄2种情形下,利用哑变量和非线性似然无关回归方法相结合,构建单木和林分水平的一元相容性生物量模型。[结果]表明:(1)地上及全株生物量模型单木水平下的R～均大于0.95,林分水平下的R～均大于0.78,(2)利用哑变量考虑林龄因素后,单木水平下各评价指标总体稳定,参数b值范围从0.905 5～2.512 5减小为1.047 0～2.202 8。林分水平下R~2提升0.201 9,参数b值范围从0.071 1～1.560 7减小为0.781 1～1.055 1;且具有更小的TRE、MPE和MSE。(3)利用对数转换的线性回归模型,全株及各组分生物量模型残差的分布趋势均平行于横轴。[结论]非线性似然无关回归和哑变量相结合的方法灵活、建模过程简单、模型稳定性好,适用于不同因素下落叶松人工林相容性生物量模型构建。林龄因素对林分模型拟合效果的改善更显著,在建模过程中,单木模型可以不考虑林龄的影响,而林分模型需要考虑林龄的影响。
[Objective]Compatible models for the single tree biomass and stand biomass of Larix olgensis were established to represent different levels of variations and to improve generalization capability of models. [Method] Based on the biomass data of 64 trees in 40 sample plots of L. olgensis plantation, the compatible models were established by combining dummy variable and nonlinear seemingly unrelated regression under the conditions of considering or not considering stand age. [Result](1) The models have good estimation precision with R～>0.95 and R～>0.78 for single tree biomass and stand biomass, respectively.(2) Under the consideration of stand age with dummy variable, the fitting goodness of model is improved with smaller TRE, MPE and MSE, the evaluation statistics are stable overall and the range of parameter b reduces form 0.905 5～2.512 5 to 1.047 0～2.202 8 for single level and R～ increases by 0.201 9 and the range of parameter b reduces form 0.071 1～1.560 7 to 0.781 1～1.055 1 for stand level.(3) Using the linear regression model of logarithmic transformation, the distribution trends of model residual error of the whole plant and its components are parallel to the transverse axis. [Conclusion] The method of combing dummy variable and nonlinear seemingly unrelated regression is flexible, simple and applicable to the establishment of single tree biomass and stand biomass models. The fitting goodness of biomass model is improved with the consideration of stand age, especially in stand biomass model. Thus, the influence of stand age should be considered in the process of stand biomass modeling.
[Objective]Compatible models for the single tree biomass and stand biomass of Larix olgensis were established to represent different levels of variations and to improve generalization capability of models. [Method] Based on the biomass data of 64 trees in 40 sample plots of L. olgensis plantation, the compatible models were established by combining dummy variable and nonlinear seemingly unrelated regression under the conditions of considering or not considering stand age. [Result](1) The models have good estimation precision with R～>0.95 and R～>0.78 for single tree biomass and stand biomass, respectively.(2) Under the consideration of stand age with dummy variable, the fitting goodness of model is improved with smaller TRE, MPE and MSE, the evaluation statistics are stable overall and the range of parameter b reduces form 0.905 5～2.512 5 to 1.047 0～2.202 8 for single level and R～ increases by 0.201 9 and the range of parameter b reduces form 0.071 1～1.560 7 to 0.781 1～1.055 1 for stand level.(3) Using the linear regression model of logarithmic transformation, the distribution trends of model residual error of the whole plant and its components are parallel to the transverse axis. [Conclusion] The method of combing dummy variable and nonlinear seemingly unrelated regression is flexible, simple and applicable to the establishment of single tree biomass and stand biomass models. The fitting goodness of biomass model is improved with the consideration of stand age, especially in stand biomass model. Thus, the influence of stand age should be considered in the process of stand biomass modeling.
引文
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[8] 王永刚,舒清态,李圣娇,等.香格里拉高山松天然林林分蓄积混合效应模型构建[J].西南林业大学学报,2016,36(3):121-125.
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[10] 孙雪莲,舒清态,欧光龙,等.基于随机森林回归模型的思茅松人工林生物量遥感估测[J].林业资源管理,2015,(1) :71-76.
[11] 王立海,邢艳.秋基于人工神经网络的天然林生物量遥感估测[J].应用生态学报,2008,19 (2) :261-266.
[12] 刘镜婷,姜立春.大兴安岭不同区域落叶松相容性材积方程及异方差研究[J].林业科学研究,2016,29(3):317-323.
[13] 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.
[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] Fu L Y,Zeng W S,Tang S Z,et al.Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales-a case study for masson pine in southern China[J].Journal of Forest Science,2012,58(3) :101-115.
[16] 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.
[17] 符利勇,唐守正,张会儒,等.东北地区两个主要树种地上生物量通用方程构建[J].生态学报,2015,35(1):150-157.
[18] 董利虎,李凤日.大兴安岭东部天然落叶松林可加性林分生物量估算模型[J].林业科学,2016,52(7):13-21.
[19] 马克西,曾伟生,李智华.新疆云杉一体化立木生物量模型系统研建[J].林业科学研究,2018,30(6):105-113.
[20] 符利勇,雷渊才,曾伟生.几种相容性生物量模型及估计方法的比较[J].林业科学,2014,50(6):42-54.
[21] 莫德祥.桂东南不同密度柳杉人工林生态系统生物量及碳氮格局研究[D].南宁:广西大学,2013.
[22] 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.
[23] 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.
[24] 黄兴召,孙晓梅,张守攻,等.辽东山区日本落叶松生物量相容性模型的研究[J].林业科学研究,2014,27(2):142-148.
[25] 刘琼阁,彭道黎,黄国胜,等.东北云杉相容性立木材积和地上生物量模型研建[J].北京林业大学学报,2015,37 (2):8-15.
[26] 曾伟生,唐守正.立木生物量方程的优度评价和精度分析[J].林业科学,2011,47(11):106-113.
[27] 黄兴召,陈东升,孙晓梅,等.基于异速参数概率分布的立木地上生物量估算[J].林业科学,2014,50(6):34-41.
[28] 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.
[29] 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.
[30] Dietze M C,Wolosin M S,Clark J S.Capturing diversity and interspecific variability in allometries:a hierarchical approach[J].Forest Ecology and Management,2008,256(11):1939-1948.
[31] McRoberts R E,N?sset E E,Gobakken T.Inference for lidar-assisted estimation of forest growing stock volume[J].Remote Sensing of Environment.2013,128(1):268-275.
[2] Houghton R A.Aboveground forest biomass and the global carbon balance[J].Global Change Biology,2005,11(6):945-958.
[3] Lambert M C,Ung C H,Raulier F.Canadian national tree aboveground biomass equations[J].Canadian Journal of Forest Research,2005,35 (8) :1996-2018.
[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] Wang C K.Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests[J].Forest Ecology and Management,2006,222 (1/3):9 -16.
[6] 郭含茹,张茂震,徐丽华,等.基于地理加权回归的区域森林碳储量估计[J].浙江农林大学学报,2015,32(4):497-508.
[7] 欧光龙,胥辉,王俊峰,等.思茅松天然林林分生物量混合效应模型构建[J].北京林业大学学报,2015,37(3):101-110.
[8] 王永刚,舒清态,李圣娇,等.香格里拉高山松天然林林分蓄积混合效应模型构建[J].西南林业大学学报,2016,36(3):121-125.
[9] 罗恒春,张超,魏安超.气候对云南松林分生物量的影响研究[J].西南林业大学学报:自然科学,2017,37(6):99-104.
[10] 孙雪莲,舒清态,欧光龙,等.基于随机森林回归模型的思茅松人工林生物量遥感估测[J].林业资源管理,2015,(1) :71-76.
[11] 王立海,邢艳.秋基于人工神经网络的天然林生物量遥感估测[J].应用生态学报,2008,19 (2) :261-266.
[12] 刘镜婷,姜立春.大兴安岭不同区域落叶松相容性材积方程及异方差研究[J].林业科学研究,2016,29(3):317-323.
[13] 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.
[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] Fu L Y,Zeng W S,Tang S Z,et al.Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales-a case study for masson pine in southern China[J].Journal of Forest Science,2012,58(3) :101-115.
[16] 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.
[17] 符利勇,唐守正,张会儒,等.东北地区两个主要树种地上生物量通用方程构建[J].生态学报,2015,35(1):150-157.
[18] 董利虎,李凤日.大兴安岭东部天然落叶松林可加性林分生物量估算模型[J].林业科学,2016,52(7):13-21.
[19] 马克西,曾伟生,李智华.新疆云杉一体化立木生物量模型系统研建[J].林业科学研究,2018,30(6):105-113.
[20] 符利勇,雷渊才,曾伟生.几种相容性生物量模型及估计方法的比较[J].林业科学,2014,50(6):42-54.
[21] 莫德祥.桂东南不同密度柳杉人工林生态系统生物量及碳氮格局研究[D].南宁:广西大学,2013.
[22] 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.
[23] 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.
[24] 黄兴召,孙晓梅,张守攻,等.辽东山区日本落叶松生物量相容性模型的研究[J].林业科学研究,2014,27(2):142-148.
[25] 刘琼阁,彭道黎,黄国胜,等.东北云杉相容性立木材积和地上生物量模型研建[J].北京林业大学学报,2015,37 (2):8-15.
[26] 曾伟生,唐守正.立木生物量方程的优度评价和精度分析[J].林业科学,2011,47(11):106-113.
[27] 黄兴召,陈东升,孙晓梅,等.基于异速参数概率分布的立木地上生物量估算[J].林业科学,2014,50(6):34-41.
[28] 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.
[29] 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.
[30] Dietze M C,Wolosin M S,Clark J S.Capturing diversity and interspecific variability in allometries:a hierarchical approach[J].Forest Ecology and Management,2008,256(11):1939-1948.
[31] McRoberts R E,N?sset E E,Gobakken T.Inference for lidar-assisted estimation of forest growing stock volume[J].Remote Sensing of Environment.2013,128(1):268-275.
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