ESTIMATING TREE BOLE HEIGHT WITH BAYESIAN ANALYSIS.

Elias Milios, Kyriaki Kitikidou, Elias Pipinis, Athanasios Stampoulidis, Melina Gotsi

Abstract


Aim of study: The classical method for estimating a height-diameter model is based on the Least Squares Method (LSM) and the fit of a regression line. The Bayesian method has an exclusive advantage, compared with the classical method, in that the parameters to be estimated are considered as random variables. In this study, the Simple Linear Regression (SLR) model and the Bayesian model were used to estimate bole height from breast height diameter. Area of study: We used data of the forest stands of Rhodope (north-eastern Greece).  Materials and methods: The variables that we used were the tree bole height and the diameter at breast height. Main results: The results showed that there is an improvement in prediction accuracy with the Bayesian model; however, this didn't lead to narrower confidence intervals of the predicted value, compared to SLR. Research essentials: Narrower confidence intervals are not necessarily achieved with Bayesian methods; confidence intervals' width is related to both statistical analyses and nature of data (in this case, species ecology and structure - composition of the stands where the sampled trees belong).


Keywords


Bayes; regression; tree height; tree bole.

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References


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