Evaluating of height-diameter nonlinear models for Alnus specie in Hyrcanes forest (Case Study: Golestan Rezaeian Forest)

Subject Areas : forest

anoshirvan alemi ¹ , jafar oladi ² , asghar fallah ³ , yaser maghsoudi ⁴

1 - Ph.d student, faculty of forestry, Agriculture science and natural Resources, Sari University, Iran
2 - Associate Prof., Faculty of forestry, Agriculture science and natural Resources, Sari University,I.R. Iran
3 - Associate Prof., Faculty of forestry, Agriculture science and natural Resources, Sari University,I.R. Iran
4 - Assistant Prof., College of Engineering, Khajeh Nasiroddin Tosi University of technology, Tehran., I.R. Iran

Received: 2018-04-30 Accepted : 2018-08-24 Published : 2018-08-23

Keywords: Inventory, non-linear regression, Aliabad forests, height-diameter model,

Abstract :

Projection of stand development over time relies on accurate height-diameter functions. In this study, we evaluated the capability of 43 nonlinear models to estimate Alnus subcordata heights in a portion Rezaeian experimental forest in Gorgan, Golestan province. We applied a systematic random sampling method to collect field data within a 150×200 meter network (3.33% intensity). It resulted in 200 circular plots with 17.84 m (0.1 ha) radius. In each plot tree species, height and diameter at breast height (DBH) of all trees with DBH>7.5 cm were measured. From the available dataset, we included 70% in the model development and the remaining 30% to validate the models. The relationship between height (dependent variable) and DBH (independent variable) was analyzed using 43 non-linear regression models. The results showed no significant difference between the applied model diagnostics, and the applied t-test showed non-significant mean stand height estimation using all models and actual height at 99% confidence level. In addition, the results of Geometric, Geometric two, Hyperbolic three, Morgan-Merser-Florin and Logarithmic models with R2 of 0.88 and RMSE% of 7.81%, 7.86%, 7.88%, 7.90 and 7.92% , respectively were almost similar in that they were better predictors of forest height. Based on the results, we conclude that these models can be used for predicting forest height in similar broadleaved stands of northern Iran, provided that comparative studies are conducted elsewhere to approve the results obtained here.

References:

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