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Evaluation of Some Weibull Parameter Estimation Methods for Characterizing Stem Diameter Distribution in a Tropical Mixed Forest of Southern Nigeria 被引量:1
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作者 a.a. adeyemi P.O. Adesoye 《Journal of Statistical Science and Application》 2016年第6期257-275,共19页
Stem diameter distribution information is useful in forest management planning. Weibull function is flexible, and has been used in characterising diameter distributions, especially in single-species planted stands, th... Stem diameter distribution information is useful in forest management planning. Weibull function is flexible, and has been used in characterising diameter distributions, especially in single-species planted stands, the world over. We evaluated some Weibull parameter estimation methods for stem diameter characterisation in (Oban) multi-species Forest in southern Nigeria. Four study sites (Aking, Ekang, Erokut and Ekuri) were selected. Four 2 km-long transects situated at 600 m apart were laid in each location. Five 50m x 50m plots were alternately laid along each transect at 400 m apart (20 plots/location) using systematic sampling technique. Tree growth variables: diameter at breast height (Dbh), diameters at the base, middle and merchantable limit, total height, merchantable height, stem straightness, crown length and crown diameter were measured on all trees 〉 10 cm to compute model response variables such as mean diameters, basal area and stem volume. Weibull parameters estimation methods used were: moment-based, percentile-based, hybrid and maximum-likelihood (ML). Data were analysed using descriptive statistics, regression models and ANOVA at α0.05. Percentile-based method was the best for Weibull [location (a), scale (b) and shape (c)] parameters estimations with mLogL = 116.66±21.89, while hybrid method was least-suitable (mLogL = 690.14±128.81) for Weibull parameters estimations. Quadratic mean diameter (Dq) was the only suitable predictor of Weibull parameters in Oban Forest. 展开更多
关键词 Diameter distribution parameter estimation methods prediction models
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