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Adaptive k-tree sample plot for the estimation of stem density: An empirical approach

SOHRABI, Hormoz

Journal of forest science (Praha), 2018, Vol.64 (1), p.17-24 [Periódico revisado por pares]

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Título:
Adaptive k-tree sample plot for the estimation of stem density: An empirical approach
Autor: SOHRABI, Hormoz
Assuntos: Adaptive sampling ; bias ; Density ; distance sampling ; efficiency ; forest sampling ; plotless sampling ; Sampling ; Trees ; Woodlands
É parte de: Journal of forest science (Praha), 2018, Vol.64 (1), p.17-24
Descrição: Available budgets for the inventory of non-commercial woodlands are small. Therefore, there has been increased interest in using distance methods, such as k-tree sampling, which are faster than fixed plot sampling. In low-density woodlands, large search areas for k nearest trees contradict any practical advantage over sampling with fixed area plots. Here, a modification of a k-tree sample plot with an empirical approach to estimating the number of trees per unit area in low-density woodlands is presented. The standard and modified k-tree sample plots have been tested in one actual and three simulated forests with different spatial patterns. The modified method was superior to other combinations of methods in terms of relative bias and relative efficiency. Considering statistical and practical aspects of sampling for tree density, the modified method is more promising than is the standard one.
Editor: Prague: Czech Academy of Agricultural Sciences (CAAS)
Idioma: Inglês;Tcheco;Eslovaco

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