海南岛马占相思生物量模型构建方法研究
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摘要
基于84株马占相思伐倒木实测生物量数据,构建了马占相思总量及各组分生物量估测模型,然后利用非线性度量误差方法,建立总量生物量与各组分(地上、树干、树根、树枝、树叶、树冠)的相容性生物量模型,拟采用可加性总量直接控制和分级联合控制2种方案对上述模型进行拟合和评价。结果表明:1)由胸径、树高变量构建的总量及各组分二元生物量独立模型预估效果较好,其确定系数均在0.85以上,最高达0.97;2)就可加性总量直接控制法而言,拟合的对数模型进一步提高了生物量模型的稳定性;3)这2种方案所建的二元模型拟合效果以分级联合控制方案略优,这2种模型在准确性上没有明显的差别;4)可认为利用度量误差法构建的马占相思生物量模型拟合精度高,实用性好。
Based on the measured biomass data of 84 fallen trees of Acacia mangium artificial forest, the basic models for estimating total biomass and biomass of acacia were established. Then, by using the method of non-linear measurement error, the compatibility biomass models of total biomass and components(above ground, trunk, root, branch, leaf and crown) were built. Two schemes, additive total direct control and hierarchical joint control, are proposed to fit and evaluate the above models. The results show that 1) Independent models of total biomass and binary biomass of each component constructed by DBH and tree height variables had better predictive effects, and their determinant coefficients were above 0.85 and the highest was 0.97. 2) As far as additive total direct control method is concerned, the logarithmic model fitted further improves the stability of biomass model. 3) Of the two schemes,the fitting effect of the binary model scheme was slightly better than that of the hierarchical joint control scheme. There is no obvious difference in the accuracy between the two models. 4) It can be considered that the Acacia biomass model constructed by measuring error method has high fitting accuracy and good practicability.
Based on the measured biomass data of 84 fallen trees of Acacia mangium artificial forest, the basic models for estimating total biomass and biomass of acacia were established. Then, by using the method of non-linear measurement error, the compatibility biomass models of total biomass and components(above ground, trunk, root, branch, leaf and crown) were built. Two schemes, additive total direct control and hierarchical joint control, are proposed to fit and evaluate the above models. The results show that 1) Independent models of total biomass and binary biomass of each component constructed by DBH and tree height variables had better predictive effects, and their determinant coefficients were above 0.85 and the highest was 0.97. 2) As far as additive total direct control method is concerned, the logarithmic model fitted further improves the stability of biomass model. 3) Of the two schemes,the fitting effect of the binary model scheme was slightly better than that of the hierarchical joint control scheme. There is no obvious difference in the accuracy between the two models. 4) It can be considered that the Acacia biomass model constructed by measuring error method has high fitting accuracy and good practicability.
引文
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[20]曾伟生.3种异速生长方程对生物量建模的对比分析[J].中南林业调查规划,2014(1):1-3.
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[22]林开淼.亚热带常绿阔叶林生物量模型及其分析[J].中南林业科技大学学报,2017,37(11):115-120,126.
[23]周华,孟盛旺,刘琪璟.亚热带常绿阔叶林幼树与灌木的地上生物量模型[J].南京林业大学学报(自然科学版),2017,41(6):79-86.
[24]STEVEHU FF,MART IRITCHIE,TMESGE NH.Allometricequationsforestimatingabovegroundbiomassfor common shrubs in northeastern California[J]. Forest Ecology and Management,2017,398:48-63.
[25]KOZAK A. Methods for ensuring additivity of biomass components by regression analysis[J]. For. Chron., 1970,46:402-405.
[26]杨众养,薛杨,刘宪钊,等.海南沿海木麻黄可加性生物量模型[J].东北林业大学学报,2015,43(1):36-40.
[27]DONGL,ZHANGL,LIF. Acompatiblesystemofbiomass equations for three conifer species in Northeast, China[J]. Forest Ecology and Management,2014, 329:306-317.
[28]王金池,邓华锋,黄国胜,等.天然云杉相容性生物量估算模型[J].应用生态学报,2017,28(10):3189-3196.
[29]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.
[2]刘斌.贺兰山天然油松单株生物量及分配模式的研究[D].杨凌:西北农林科技大学,2010.
[3] PARRESOL B R. Additivity of nonlinear biomass equations[J].Canadian Journal of Forest Research, 2001,31(5):865-878.
[4] BI H, TURNER J, LAMBER M J. Additive biomass equations for native eucalypt forest trees of temperale in Australia[J].Trees,2004,18(4):467-479.
[5] FEHRMANN L, LEHTONEN A, KLEINN C, et al.Comparison of linear and mixed-effect regression models and a k-nearest neighbor approach for estimation of single-tree biomass[J]. Canadian Journal of Forest Research,2008,38(1):1-9.
[6] TEE-MIKAELIAN M T, KORZUKHIN M D. Biomass equations for sixty-five North American tree species[J]. Forest Ecology and Management,1997,97:1-24.
[7]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.
[8]孙雪莲,熊河先,胥辉,等.高山松天然林单木生物量因子模型构建[J].林业资源管理,2016(3):49-53,60.
[9]甘世书.利用度量误差模型建立海南省松树和橡胶树质量与材积相容模型[J].中南林业调查规划,2015,34(4):45-48, 66.
[10]邢海涛,陆元昌,刘宪钊,等.海南岛东北部木麻黄立木生物量建模[J].南京林业大学学报(自然科学版),2017,41(2):103-110.
[11]曾伟生,张会儒,唐守正.立木生物量建模方法[M].北京:中国林业出版社,2011:10-13.
[12]周玉杰,李建华,王春燕,等.海南岛3种森林类型的土壤特性及水源涵养功能[J].安徽农业科学,2017,45(36):165-167,172.
[13]何春,李祖毅,方慧鑫,等.巨尾桉、马占相思纯林及混交林土壤酚酸与酶活性的差异[J].西部林业科学,2017,46(3):103-108,120.
[14]刘宪钊,薛杨,王小燕,等.海南省东北部沿海地区更新造林实验研究[J].生态科学,2017,36(3):130-134.
[15]唐守正,李勇.一种多元非线性度量误差模型的参数估计及算法[J].生物数学学报,1996,11(1):23-27.
[16]唐守正,郎奎建,李海奎.统计与生物数学模型计算教程[M].北京:科学出版社,2009.
[17]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):42-55.
[18]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.
[19]刘薇祎,邓华锋,黄国胜.云南松不同区域相容性生物量模型的构建[J].西北农林科技大学学报,2018,46(7):1-8.
[20]曾伟生.3种异速生长方程对生物量建模的对比分析[J].中南林业调查规划,2014(1):1-3.
[21]高亚琪,张绘芳,地力夏提·包尔汉,等.西伯利亚落叶松天然林立木生物量估算模型研究[J].新疆农业科学,2016, 53(4):655-662.
[22]林开淼.亚热带常绿阔叶林生物量模型及其分析[J].中南林业科技大学学报,2017,37(11):115-120,126.
[23]周华,孟盛旺,刘琪璟.亚热带常绿阔叶林幼树与灌木的地上生物量模型[J].南京林业大学学报(自然科学版),2017,41(6):79-86.
[24]STEVEHU FF,MART IRITCHIE,TMESGE NH.Allometricequationsforestimatingabovegroundbiomassfor common shrubs in northeastern California[J]. Forest Ecology and Management,2017,398:48-63.
[25]KOZAK A. Methods for ensuring additivity of biomass components by regression analysis[J]. For. Chron., 1970,46:402-405.
[26]杨众养,薛杨,刘宪钊,等.海南沿海木麻黄可加性生物量模型[J].东北林业大学学报,2015,43(1):36-40.
[27]DONGL,ZHANGL,LIF. Acompatiblesystemofbiomass equations for three conifer species in Northeast, China[J]. Forest Ecology and Management,2014, 329:306-317.
[28]王金池,邓华锋,黄国胜,等.天然云杉相容性生物量估算模型[J].应用生态学报,2017,28(10):3189-3196.
[29]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.