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Development of Diameter Distribution Yield Prediction Models for Simulation of Acacia Mangium Plantations
Thesis Abstract:
The purpose of this study was to develop diameter distribution yield prediction models for predicting probability density function parameters when age, spacing and number of trees per hectare planted were known. Five distributions, namely: Weibull, Gamma, Johnson SB, Lognormal, and Generalized Normal, were compared in terms of their ability to model diameter date in uneven-aged forest stands. The classical moments were applied as a measure of flexibility of the distribution with regard to their changes in shape. Diameter data were obtained from 16 uneven-age stands of mixed timber species located at Bukit Lagong Forest Reserve, Kepong, Selangor, and 14 even-aged stand of Acacia mangium located a Segaliud Lokan Project, Sandakan, Sabah. The stands were all plantation and the age ranged from 2 to 22 years. The diameter data were fitted to the five distributions by the maximum likelihood estimation method. The Johnson SB distribution showed the best performance in terms of quality of fit to the diameter data based on relative ranking of the log likelihood criterion. The estimation Johnson SB distribution was further investigated and the non-linear regression method was proposed for the estimation of the SB parameters. This method was compared to five other estimation methods, namely: four percentile method, Knoebel-Burkhart method, linear regression method, maximum likelihood method, and modified maximum likelihood method through simulation. The performance of the non-linear regression was confirmed by using the real diameter data. Goodnessof- fit tests based on empirical distribution function (Kolmogorov-Smirnov statistic, Cramer-von Mises statistic, and Anderson-Darling statistic) were used in selecting the most superior parameter estimation method. The results suggested that the nonlinear regression method was superior for estimating parameters of the Johnson SB distribution for the diameter data. In order to simulate the stand characteristics, equations were developed for predicting average height, basal area per hectare and number of trees per hectare surviving when age, spacing and number of trees per hectare planted were known. The predicted stand characteristics were then related to the parameters of the Johnson SB and solving the resulting set of equations for the scale and shape parameters. This study revealed that the parameter prediction method yielded reliable prediction equations of the stand characteristics, but the prediction equations of the scale and shape parameters suggested that further research is needed to improve the model.
