Forest regeneration survey with an angle-distance method: study case of naturally regenerated Chamaecyparis trees
2012
Trifković, Stanko (Graduate School of Informatics, Kyoto (Japan)) | Sakai, Tetsuro (Graduate School of Informatics, Kyoto (Japan)) | Yamamoto, Hirokazu (Graduate School of Frontier Sciences, Tokyo (Japan))
Success of forest regeneration is a one of the most important prerequisites in attaining sustainability of forest management. Spatial distribution and density of juvenile trees are very important parameters to evaluate a success of forest regeneration. The mean-of-angles method is proven as a rapid approach to index spatial distribution of trees. The c-tree sampling method is known as a rapid method to estimate density of forest trees. We propose to use the combined angle-distance methodology in forest regeneration surveys. Several theoretical point populations were simulated. Also, the combined angle-distance methodology was tested in a population of naturally regenerated Chamaecyparis saplings in Kiso area of Japan. Maximum-likelihood estimator is applicable in random and the GM estimator in regular populations. The (c-1) estimator can be used in clustered populations. However, an increased degree of clustering and an increase in c-values will increase the amount of bias; the true density is overestimated in highly clustered populations and with higher c-values. Therefore, using the (c-1) estimator with small c-values, such as 2-tree or 3-tree sampling, can be more reliable to estimate density of clustered populations with unequal size and shape of clusters. Although a great variety of tree-spatial-patterns may occur in nature, the angle-distance method has proved as fast and reliable for the use in forest regeneration surveys
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