Application of Bayesian Method in Stand Basal Area Prediction of Chinese Fir Plantation

ZHANG Xiong-qing; ZHANG Jian-guo; DUAN Ai-guo

Volume 28 Issue 4

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Application of Bayesian Method in Stand Basal Area Prediction of Chinese Fir Plantation

1.
Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration Research Institute of Forestry, Chinese Academy of Forestry
2.
Beijing 100091, China

Received Date: 2013-10-17

Abstract

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), an endemic tree species in China's subtropical area, is one of the most important fast-growing tree species for timber production in southern China. Based on the periodic data of the Chinese fir in Jiangxi province, three stand basal area models (Korf-based model, Richards-based model, and Hossfeld-based model) were developed using generalized algebraic difference approach (GADA). The results showed that Richards-based model was the best for modeling the stand basal area of Chinese fir in the study. Additionally, Bayesian method and Classical method (nonlinear least squares method) were used to estimate the Richards-based model. Although the precision of Bayesian method was nearly equal to that of the classical method, the model reliability using Bayesian method was better than using classical method.
- Bayesian method,
- stand basal area,
- Chinese fir,
- Cunninghamia lanceolata

References

[1]	张雄清, 雷渊才, 陈新美, 等. 组合预测法在林分断面积生长预估中的应用[J]. 北京林业大学学报, 2010, 32(4): 6-11.
[2]	杜纪山, 唐守正. 杉木林分断面积生长预估模型及其应用[J]. 北京林业大学学报, 1998, 20(4): 1-5.
[3]	张建国, 孙洪刚. 杉木人工林断面积生长规律及动态模拟[M]. 北京: 科学出版社, 2010: 58-75.
[4]	Li R, Stewart B, Weiskittel A. A Bayesian approach for modelling non-linear longitudinal/hierarchical data with random effects in forestry[J]. Forestry, 2012, 85(1): 17-25.
[5]	Clyde M. Model uncertainty and health effect studies for particulate matter[R]. Technical Report Series, NRCSE-TRS No. 027, 1999.
[6]	Ellison A M. Bayesian inference in ecology[J]. Ecology letters, 2004, 7: 509-520.
[7]	李向阳.水文模型参数优选及不确定性分析方法研究[D]. 大连:大连理工大学, 2005.
[8]	Zhang X, Duan A, Zhang J. Tree biomass estimation of Chinese fir (Cunninghamia lanceolata) based on bayesian method[J]. PLOS ONE, 2013, 8(11): 1-7.
[9]	Green E J, Jr.Roesch FAA, Smith F M, et al. Bayesian estimation for the three-parameter Weibull distribution with tree diameter data[J]. Biometrics, 1994, 50(1): 254-269.
[10]	Bullock B P, Boone E L. Deriving tree diameter distributions using Bayesian model averaging[J]. Forest Ecology and Management, 2007, 242: 127-132.
[11]	Clark J S, Wolosin M, Dietze M, et al. Tree growth inference and prediction from diameter censuses and ring widths[J]. Ecological Applications, 2007, 17(7): 1942-1953.
[12]	Clutter J L, Jones E P. Prediction of growth after thinning in old-field slash pine plantations[R]. USDA Forest Service Research Paper, 1980: 217.
[13]	Anta M B, Dorado F C, Aranda U D, et al. Development of a basal area growth system for maritime pine in northwestern Spain using the generalized algebraic difference approach[J]. Canadian Journal of Forest Research, 2006, 36(6): 1461-1474.
[14]	Lindley D V. Bayesian thoughts[J]. Significance, 2000, 1(2): 73-75.
[15]	茹诗松. 高等数理统计[M]. 北京: 高等教育出版社, 1998.
[16]	Gelman A, Carlin J B, Stern H S. Bayesian Data Analysis, 2nd edn[M]. USA: Chapman and Hall/CRC, 2004.
[17]	Spiegelhalter D J, Thomas A, Best N. WinBUGS User Manual[M]. Cambridge: MRC Biostatistics Unit, 2003.
[18]	Chib S. Greenberg E. Understanding the Metropolis-Hastings algorithm[J]. The American Statistician, 1995, 49(4): 327-335.
[19]	Sturtz S, Ligges U, Gelman A. R2WinBUGS: a package for running WinBUGS from R[J]. Journal of Statistic Software, 2005, 12(3): 1-16.
[20]	Spiegelhalter D J, Best N G, Carlin B P, et al. Bayesian measures of model complexity and fit (with discussion)[J]. Journal of Royal Statistical Society, Series B, 2002, 64: 583-616.
[21]	杜纪山, 唐守正. 林分断面积生长模型研究综述[J]. 林业科学研究, 1997, 10(6): 599-606.
[22]	Bailey R L, Clutter J L. Base-age invariant polymorphic site curves[J]. Forest Science, 1974, 20(2): 155-159.
[23]	Cieszewski C J, Bailey R L. Generalized algebraic difference approach: theory based derivation of dynamic equations with polymorphism and variable asymptotes[J]. Forest Science, 2000, 46(1): 116-126.
[24]	Cieszewski C J. Three methods of deriving advanced dynamic site equations demonstrated on inland Douglas-fir site Curves[J]. Canadian Journal of Forest Research, 2001, 31(1): 165-173.
[25]	Cieszewski C J. Comparing fixed-and variable-base-age site equations having single versus multiple asymptotes[J]. Forest Science, 2002,48(1): 7-23.
[26]	McCarthy M A. Bayesian methods for ecology[M]. Cambridge UK:Cambridge University Press, 2007.
[27]	Davidian M, Giltinan D M. Nonlinear Models for Repeated Measurement Data[M]. New York: Chapman and Hall, 1995.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Application of Bayesian Method in Stand Basal Area Prediction of Chinese Fir Plantation

1. Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration Research Institute of Forestry, Chinese Academy of Forestry

2. Beijing 100091, China

Received Date: 2013-10-17

Abstract: Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), an endemic tree species in China's subtropical area, is one of the most important fast-growing tree species for timber production in southern China. Based on the periodic data of the Chinese fir in Jiangxi province, three stand basal area models (Korf-based model, Richards-based model, and Hossfeld-based model) were developed using generalized algebraic difference approach (GADA). The results showed that Richards-based model was the best for modeling the stand basal area of Chinese fir in the study. Additionally, Bayesian method and Classical method (nonlinear least squares method) were used to estimate the Richards-based model. Although the precision of Bayesian method was nearly equal to that of the classical method, the model reliability using Bayesian method was better than using classical method.

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Reference (27)

[1]	张雄清, 雷渊才, 陈新美, 等. 组合预测法在林分断面积生长预估中的应用[J]. 北京林业大学学报, 2010, 32(4): 6-11.
[2]	杜纪山, 唐守正. 杉木林分断面积生长预估模型及其应用[J]. 北京林业大学学报, 1998, 20(4): 1-5.
[3]	张建国, 孙洪刚. 杉木人工林断面积生长规律及动态模拟[M]. 北京: 科学出版社, 2010: 58-75.
[4]	Li R, Stewart B, Weiskittel A. A Bayesian approach for modelling non-linear longitudinal/hierarchical data with random effects in forestry[J]. Forestry, 2012, 85(1): 17-25.
[5]	Clyde M. Model uncertainty and health effect studies for particulate matter[R]. Technical Report Series, NRCSE-TRS No. 027, 1999.
[6]	Ellison A M. Bayesian inference in ecology[J]. Ecology letters, 2004, 7: 509-520.
[7]	李向阳.水文模型参数优选及不确定性分析方法研究[D]. 大连:大连理工大学, 2005.
[8]	Zhang X, Duan A, Zhang J. Tree biomass estimation of Chinese fir (Cunninghamia lanceolata) based on bayesian method[J]. PLOS ONE, 2013, 8(11): 1-7.
[9]	Green E J, Jr.Roesch FAA, Smith F M, et al. Bayesian estimation for the three-parameter Weibull distribution with tree diameter data[J]. Biometrics, 1994, 50(1): 254-269.
[10]	Bullock B P, Boone E L. Deriving tree diameter distributions using Bayesian model averaging[J]. Forest Ecology and Management, 2007, 242: 127-132.
[11]	Clark J S, Wolosin M, Dietze M, et al. Tree growth inference and prediction from diameter censuses and ring widths[J]. Ecological Applications, 2007, 17(7): 1942-1953.
[12]	Clutter J L, Jones E P. Prediction of growth after thinning in old-field slash pine plantations[R]. USDA Forest Service Research Paper, 1980: 217.
[13]	Anta M B, Dorado F C, Aranda U D, et al. Development of a basal area growth system for maritime pine in northwestern Spain using the generalized algebraic difference approach[J]. Canadian Journal of Forest Research, 2006, 36(6): 1461-1474.
[14]	Lindley D V. Bayesian thoughts[J]. Significance, 2000, 1(2): 73-75.
[15]	茹诗松. 高等数理统计[M]. 北京: 高等教育出版社, 1998.
[16]	Gelman A, Carlin J B, Stern H S. Bayesian Data Analysis, 2nd edn[M]. USA: Chapman and Hall/CRC, 2004.
[17]	Spiegelhalter D J, Thomas A, Best N. WinBUGS User Manual[M]. Cambridge: MRC Biostatistics Unit, 2003.
[18]	Chib S. Greenberg E. Understanding the Metropolis-Hastings algorithm[J]. The American Statistician, 1995, 49(4): 327-335.
[19]	Sturtz S, Ligges U, Gelman A. R2WinBUGS: a package for running WinBUGS from R[J]. Journal of Statistic Software, 2005, 12(3): 1-16.
[20]	Spiegelhalter D J, Best N G, Carlin B P, et al. Bayesian measures of model complexity and fit (with discussion)[J]. Journal of Royal Statistical Society, Series B, 2002, 64: 583-616.
[21]	杜纪山, 唐守正. 林分断面积生长模型研究综述[J]. 林业科学研究, 1997, 10(6): 599-606.
[22]	Bailey R L, Clutter J L. Base-age invariant polymorphic site curves[J]. Forest Science, 1974, 20(2): 155-159.
[23]	Cieszewski C J, Bailey R L. Generalized algebraic difference approach: theory based derivation of dynamic equations with polymorphism and variable asymptotes[J]. Forest Science, 2000, 46(1): 116-126.
[24]	Cieszewski C J. Three methods of deriving advanced dynamic site equations demonstrated on inland Douglas-fir site Curves[J]. Canadian Journal of Forest Research, 2001, 31(1): 165-173.
[25]	Cieszewski C J. Comparing fixed-and variable-base-age site equations having single versus multiple asymptotes[J]. Forest Science, 2002,48(1): 7-23.
[26]	McCarthy M A. Bayesian methods for ecology[M]. Cambridge UK:Cambridge University Press, 2007.
[27]	Davidian M, Giltinan D M. Nonlinear Models for Repeated Measurement Data[M]. New York: Chapman and Hall, 1995.

Application of Bayesian Method in Stand Basal Area Prediction of Chinese Fir Plantation

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通讯作者: 陈斌, bchen63@163.com

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Application of Bayesian Method in Stand Basal Area Prediction of Chinese Fir Plantation

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