Abstract:
Three k-tree distance and fixed-sized plot designs were used for estimating tree density
in sparse Oak forests. These forests cover the main part of the Zagros mountain area in
western Iran. They are non-timber-oriented forest but important for protection purposes.
The main objective was to investigate the statistical performance of k-tree distance and
fixed-sized plot designs in the estimation of tree density. In addition, the cost (time
required) of data collection using both k-tree distance and fixed-sized plot designs was
estimated. Monte-Carlo sampling simulation was used in order to compare the different
strategies. The bias of the k-tree distance designs estimators decreased with increasing the
value of k. The Moore’s estimator produced the smallest bias, followed by Kleinn and
Vilcko and then Prodan. In terms of cost-efficiency, Moore’s estimator was the best and
Prodan’s estimator was superior to Kleinn and Vilcko’s estimator. Cost-efficiency of ktree
distance design is related to three factors: sample size, the value of k, and spatial
distribution of trees in a forest stand. Moore’s estimator had the best statistical
performance in terms of bias, in all four-study sites. Thus, it can be concluded that
Moore’s estimator can have a better performance in forests with different tree
distribution.
Keyword: Boundary correction, Monte-Carlo simulation, Oak forest, Plot less sampling,
Variable plot sampling
