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

Nguyen Thanh Tuan, Tai Tien Dinh and Shen Hai Long
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Nguyen Thanh Tuan: Department of Forestry, Vietnam National University of Forestry, Dong Nai, Vietnam
Tai Tien Dinh: Institute of Resources and Environment, Hue University, Hue, Vietnam
Shen Hai Long: School of Forestry, Northeast Forestry University, Harbin, P.R. China

Journal of Forest Science, 2019, vol. 65, issue 4, 134-143

Abstract: 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.

Keywords: Forest measurement; nonlinear growth functions; artificial intelligence technology; Korean pine plantation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnljfs:v:65:y:2019:i:4:id:5-2019-jfs

DOI: 10.17221/5/2019-JFS

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