Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models

https://doi.org/10.17221/5/2019-JFSCitation:Nguyen Thanh T., Dinh Tien T., Shen H.L. (2019): Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models. J. For. Sci., 65: 134-143.
download PDF

Korean pine (Pinus koraiensis Sieb. et Zucc.) is one of the highly commercial woody species in Northeast China. In this study, six nonlinear equations and artificial neural network (ANN) models were employed to model and validate height-diameter (H-DBH) relationship in three different stand densities of one Korean pine plantation. Data were collected in 12 plots in a 43-year-old even-aged stand of P. koraiensis in Mengjiagang Forest Farm, China. The data were randomly split into two datasets for model development (9 plots) and for model validation (3 plots). All candidate models showed a good perfomance in explaining H-DBH relationship with error estimation of tree height ranging from 0.61 to 1.52 m. Especially, ANN models could reduce the root mean square error (RMSE) by the highest 40%, compared with Power function for the density level of 600 trees. In general, our results showed that ANN models were superior to other six nonlinear models. The H-DBH relationship appeared to differ between stand density levels, thus it is necessary to establish H-DBH models for specific stand densities to provide more accurate estimation of tree height.

References:
Ahmadi K., Alavi S.J, Kouchaksaraei M.T, Aertsen W. (2013): Non-linear height-diameter models for oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forests, Iran. Biotechnologie, Agronomie, Société et Environnement, 17: 431–440
 
Ahmadi K., SJ Alavi (2016): Generalized height-diameter models for Fagus orientalis Lipsky in Hyrcanian forest, Iran. Journal of Forest Science, 62, 413-421 https://doi.org/10.17221/51/2016-JFS
 
Akaike H. (1974): A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723 https://doi.org/10.1109/TAC.1974.1100705
 
Ashraf M.I, Meng F.R, Bourque C.P.A, MacLean D.A. (2015): A novel modelling approach for predicting forest growth and yield under climate change. PLoS ONE 10: 1–18.
 
Blackard Jock A., Dean Denis J. (1999): Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture, 24, 131-151 https://doi.org/10.1016/S0168-1699(99)00046-0
 
Colbert K.C, Larsen D.R, Lootens J.R. (2002): Height–diameter equations for thirteen midwestern bottomland hardwood species. Northern Journal of Applied Forestry, 19: 171–176.
 
Costa Emanuel Arnoni, Schroder Thomas, Finger César Augusto Guimarães (2016): HEIGHT-DIAMETER RELATIONSHIPS FOR Araucaria angustifolia (BERTOL.) KUNTZE IN SOUTHERN BRAZIL. CERNE, 22, 493-500 https://doi.org/10.1590/01047760201622042182
 
D’Emilio Alessandro, Aiello Rosa, Consoli Simona, Vanella Daniela, Iovino Massimo (2018): Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils. Water, 10, 1431- https://doi.org/10.3390/w10101431
 
Hong-bing Deng, Zhan-qing Hao, Qing-li Wang (2001): Study on height growth model ofPinus koraiensis. Journal of Forestry Research, 12, 192-194 https://doi.org/10.1007/BF02856705
 
Diamantopoulou Maria J., Milios Elias (2010): Modelling total volume of dominant pine trees in reforestations via multivariate analysis and artificial neural network models. Biosystems Engineering, 105, 306-315 https://doi.org/10.1016/j.biosystemseng.2009.11.010
 
Diamantopoulou M. J., Özçelik R. (2012): Evaluation of different modeling approaches for total tree-height estimation in Mediterranean Region of Turkey. Forest Systems, 21, 383- https://doi.org/10.5424/fs/2012213-02338
 
Dorn Lisa A., Pyle Elizabeth Hammond, Schmitt Johanna (2000): PLASTICITY TO LIGHT CUES AND RESOURCES IN ARABIDOPSIS THALIANA: TESTING FOR ADAPTIVE VALUE AND COSTS. Evolution, 54, 1982-1994 https://doi.org/10.1111/j.0014-3820.2000.tb01242.x
 
Costa Emanuel Arnoni, Hess André Felipe, Klein Danieli Regina, Finger César Augusto Guimarães (2018): Height-Diameter Models for Araucaria angustifolia (Bertol.) Kuntze in Natural Forests. Journal of Agricultural Science, 10, 133- https://doi.org/10.5539/jas.v10n8p133
 
Ferraz Filho Antonio Carlos, Mola-Yudego Blas, Ribeiro Andressa, Scolforo José Roberto Soares, Loos Rodolfo Araújo, Scolforo Henrique Ferraço (2018): HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZIL. CERNE, 24, 9-17 https://doi.org/10.1590/01047760201824012466
 
Gomperzt B. (1832): On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society of London, 115: 513 –585.
 
Huang Shongming, Titus Stephen J., Wiens Douglas P. (1992): Comparison of nonlinear height–diameter functions for major Alberta tree species. Canadian Journal of Forest Research, 22, 1297-1304 https://doi.org/10.1139/x92-172
 
Huang S.M. (1999): Ecoregion-based individual tree height–diameter models for lodgepole pine in Alberta. Western Journal of Applied Forestry, 14: 186–193.
 
Jin X.J, Pukkala T., Li F.R, Dong L.H. (2017): Optimal management of Korean pine plantations in multifunctional forestry. Journal of Forestry Research, 28: 1027–1037.
 
Krieger C. (1998): The effects of tree spacing on diameter, height and branch size in white spruce. Management Notes, 13: 1–12.
 
Kingston Greer B., Maier Holger R., Lambert Martin F. (2008): Bayesian model selection applied to artificial neural networks used for water resources modeling. Water Resources Research, 44, - https://doi.org/10.1029/2007WR006155
 
Mehtätalo Lauri, de-Miguel Sergio, Gregoire Timothy G. (2015): Modeling height-diameter curves for prediction. Canadian Journal of Forest Research, 45, 826-837 https://doi.org/10.1139/cjfr-2015-0054
 
Lei Xiangdong, Peng Changhui, Wang Haiyan, Zhou Xiaolu (2009): Individual height–diameter models for young black spruce ( Picea mariana ) and jack pine ( Pinus banksiana ) plantations in New Brunswick, Canada. The Forestry Chronicle, 85, 43-56 https://doi.org/10.5558/tfc85043-1
 
Lumbres Roscinto Ian C., Jin Lee Young, Calora Feliciano G., Parao Marissa R. (2013): Model fitting and validation of six height–DBH equations for Pinus kesiya Royle ex Gordon in Benguet Province, Philippines. Forest Science and Technology, 9, 45-50 https://doi.org/10.1080/21580103.2013.772542
 
Luo Zhaohui, Song Qingmei, Wang Tao, Zeng Huanmu, He Tao, Zhang Hengjun, Wu Wenchen (2018): Direct Impacts of Climate Change and Indirect Impacts of Non-Climate Change on Land Surface Phenology Variation across Northern China. ISPRS International Journal of Geo-Information, 7, 451- https://doi.org/10.3390/ijgi7110451
 
Mäkinen Harri, Isomäki Antti (2004): Thinning intensity and growth of Scots pine stands in Finland. Forest Ecology and Management, 201, 311-325 https://doi.org/10.1016/j.foreco.2004.07.016
 
Mehtatalo L. (2015): Lmfor: Functions for forest biometrics. R package version. 1. Available at https://CRAN.R–project.org/package=lmfor
 
Tsega Mengesha, Guadie Awoke, Teffera Zebene Lakew, Belayneh Yigez, Niu Dongjie (2018): Development and Validation of Height-Diameter Models for Cupressus lusitanica in Gergeda Forest, Ethiopia. Forest Science and Technology, 14, 138-144 https://doi.org/10.1080/21580103.2018.1482794
 
Hassanzad Navroodi I., SJ Alavi, MK Ahmadi, Radkarimi M. (2016): Comparison of different non-linear models for prediction of the relationship between diameter and height of velvet maple trees in natural forests (Case study: Asalem Forests, Iran). Journal of Forest Science, 62, 65-71 https://doi.org/10.17221/43/2015-JFS
 
Nguyen Thanh Tuan, Tai Dinh Tien, Zhang Peng, Razaq Muhammad, Shen Hai-Long (2019): Effect of thinning intensity on tree growth and temporal variation of seed and cone production in a Pinus koraiensis plantation. Journal of Forestry Research, 30, 835-845 https://doi.org/10.1007/s11676-018-0690-x
 
Özçelik Ramazan, Diamantopoulou Maria J., Crecente-Campo Felipe, Eler Unal (2013): Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models. Forest Ecology and Management, 306, 52-60 https://doi.org/10.1016/j.foreco.2013.06.009
 
ÖZÇELİK Ramazan, YAVUZ Hakkı, KARATEPE Yasin, GÜRLEVİK Nevzat, KIRIŞ Rüstem (2014): Development of ecoregion-based height–diameter models for 3 economically important tree species of southern Turkey. TURKISH JOURNAL OF AGRICULTURE AND FORESTRY, 38, 399-412 https://doi.org/10.3906/tar-1304-115
 
Panchal Gaurang, Ganatra Amit, Kosta Y.P., Panchal Devyani (2010): Searching Most Efficient Neural Network Architecture Using Akaike's Information Criterion (AIC). International Journal of Computer Applications, 1, 54-57 https://doi.org/10.5120/126-242
 
Parresol Bernard R. (1992): Baldcypress height–diameter equations and their prediction confidence intervals. Canadian Journal of Forest Research, 22, 1429-1434 https://doi.org/10.1139/x92-191
 
Pearl R., Reed L. J. (1920): On the Rate of Growth of the Population of the United States since 1790 and Its Mathematical Representation. Proceedings of the National Academy of Sciences, 6, 275-288 https://doi.org/10.1073/pnas.6.6.275
 
R Core Team (2018) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. 
 
Ratkowsky D.A. (1990): Handbook of Nonlinear Regression. New York, Marcel Dekker: 120.
 
RICHARDS F. J. (1959): A Flexible Growth Function for Empirical Use. Journal of Experimental Botany, 10, 290-301 https://doi.org/10.1093/jxb/10.2.290
 
Ritchie M., Zhang J., Hamilton T. (2012): Effects of stand density on top height estimation for ponderosa pine. Western Journal of Applied Forestry, 27: 18–24.
 
Shao G., Reynolds K.M. (2006): Computer Applications in Sustainable Forest Management. Including Perspectives on Collaboration and Integration. Dordrecht, Springer: 312.
 
Sharma Mahadev, Yin Zhang Shu (2010): Height–Diameter Models Using Stand Characteristics for Pinus banksiana and Picea mariana. Scandinavian Journal of Forest Research, 19, 442-451 https://doi.org/10.1080/02827580410030163
 
Sharma Mahadev, Parton John (2007): Height–diameter equations for boreal tree species in Ontario using a mixed-effects modeling approach. Forest Ecology and Management, 249, 187-198 https://doi.org/10.1016/j.foreco.2007.05.006
 
Staudhammer Christie, LeMay Valerie (2000): Height prediction equations using diameter and stand density measures. The Forestry Chronicle, 76, 303-309 https://doi.org/10.5558/tfc76303-2
 
Stoffels A., Van Soeset J. (1953): The main problems in sample plots. Nederlands Bosbouw Tijdschrift, 25: 190–199.
 
Strand L. (1959): The accuracy of some methods for estimating volume and increment on sample plots. Meddel. norske Skogfors, 15: 284–392. (in Norwegian with English summary)
 
Sheela K. Gnana, Deepa S. N. (2013): Review on Methods to Fix Number of Hidden Neurons in Neural Networks. Mathematical Problems in Engineering, 2013, 1-11 https://doi.org/10.1155/2013/425740
 
Shi Y., Zhang J.Y. (2012): The Application of Artificial Neural Network Model in Estimation of Single Tree Volume Growth. In: International Conference on Remote Sensing Environment and Transportation Engineering. Nanjing, June 1–3, 2012: 1–6. doi: 10.1109/RSETE.2012.6260764.
 
Vieira G.C, De Mendon A.R, Da Silva G.F, Zanetti S.S, DaSilva M.M, Dos Santos A.R. (2018): Prognoses of diameter and height of trees of eucalyptus using artificial intelligence. Science of The Total Environment, (619–620): 1473–1481.
 
Zang H., Lei X.D., Zhang H.R., Li C.M., Lu J. (2016): Nonlinear mixed-effects height-diameter model of Pinus koraiensis. Journal of Beijing Forestry University, 38: 8–16. (in Chinese with English summary)
 
Zeide B., Vanderschaaf C. (2002): The effect of density on the height-diameter relationship. In: General Technical Report SRS–48. Asheville, NC: US Department of Agriculture, Forest Service, Southern Research Station: 463–466.
 
ZHANG L (): Cross-validation of Non-linear Growth Functions for Modelling Tree Height–Diameter Relationships. Annals of Botany, 79, 251-257 https://doi.org/10.1006/anbo.1996.0334
 
Zhi D.U, Gan S.S. (2017): Height-diameter models for Cunninghamia lanceolata and Pinus massoniana based on bp neural network. Central South Forest Inventory and Planning, 36: 36–39. (in Chinese with English summary)
 
Woodruff David R, Bond Barbara J, Ritchie Gary A, Scott William (2002): Effects of stand density on the growth of young Douglas-fir trees. Canadian Journal of Forest Research, 32, 420-427 https://doi.org/10.1139/x01-213
 
download PDF

© 2019 Czech Academy of Agricultural Sciences