A Robust Estimator of the Density of Trees Exhibiting Regular Spatial Patterns

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  • Trifkovic Stanko
    Laboratory of Forest Ecosystem Science and Management (University Forests), Graduate School of Agricultural and Life Science The University of Tokyo
  • Yamamoto Hirokazu
    Graduate School of Frontier Sciences The University of Tokyo

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Abstract

The c-tree sampling method has been proposed in the past as a rapid approach to estimate forest stand parameters. However, the use of c-tree sampling is not as simple as previously thought. In order to use it, we should have a broad knowledge on statistical methodology and estimators as well as on spatial patterns and their indices. Otherwise, the use of c-tree sampling could lead into wrong conclusions. Depending on exhibited spatial patterns and the type of estimator, c-tree sampling can yield a relatively high bias. Difficulties associated with accessing information on spatial patterns have been a reason to favoring robust estimators and there was a need to derive a new estimator of tree density which would be applicable in forest plantations. Density estimates with a newly derived estimator, named to as the "GM estimator", are compared with those obtained with the (c-0.5) estimator and the (c-1) estimator. The results have shown that the GM estimator can be used in a wide range of populations exhibiting regular spatial patterns; spatial patterns ranging from highly regular to those of random. This study is a step forward in promoting the use of c-tree sampling in forestry.

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