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Prediction of Stand Volume and Carbon Stock for Quercus variabilis Using Weibull Distribution Model  

Son, Yeong Mo (Center for Forest and Climate Change, Korea Forest Research Institute)
Pyo, Jung Kee (Center for Forest and Climate Change, Korea Forest Research Institute)
Kim, So Won (Center for Forest and Climate Change, Korea Forest Research Institute)
Lee, Kyeong Hak (Center for Forest and Climate Change, Korea Forest Research Institute)
Publication Information
Journal of Korean Society of Forest Science / v.101, no.4, 2012 , pp. 599-605 More about this Journal
Abstract
The purpose of this study is to estimate diameter distribution, volume per hectare, and carbon stock for Quercus variabilis stand. 354 Quercus variabilis stands were selected on the basis of age and structure, the data and samples for these stands are collected. For the prediction of diameter distribution, Weibull model was applied and for the estimation of the parameters, a simplified method-of-moments was applied. To verify the accuracy of estimates, models were developed using 80% of the total data and validation was done on the remaining 20%. For the verification of the model, the fitness index, the root mean square error, and Kolmogorov-Smirnov statistics were used. The fitness index of the site index, height, and volume equation estimated from verification procedure were 0.967, 0.727, and 0.988 respectively and the root mean square error were 2.763, 1.817, and 0.007 respectively. The Kolmogorov-Smirnov test applied to Weibull function resulted in 75%. From the models developed in this research, the estimated volume and above-ground carbon stock were derived as $188.69m^3/ha$, 90.30 tC/ha when site index and stem number of 50-years-old Quercus variabilis stand show 14 and 697 respectively. The results obtained from this study may provide useful information about the growth of broad-leaf species and prediction of carbon stock for Quercus variabilis stand.
Keywords
Carbon stock; Parameter recovery; Quercus variabilis; Weibull distribution model;
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