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

Hormoz Sohrabi

Journal of Forest Science, 2018, vol. 64, issue 1, 17-24

Abstract: 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.

Keywords: plotless sampling; distance sampling; bias; efficiency; forest sampling (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://jfs.agriculturejournals.cz/doi/10.17221/111/2017-JFS.html (text/html)
http://jfs.agriculturejournals.cz/doi/10.17221/111/2017-JFS.pdf (application/pdf)
free of charge

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:caa:jnljfs:v:64:y:2018:i:1:id:111-2017-jfs

DOI: 10.17221/111/2017-JFS

Access Statistics for this article

Journal of Forest Science is currently edited by Mgr. Ilona Procházková

More articles in Journal of Forest Science from Czech Academy of Agricultural Sciences
Bibliographic data for series maintained by Ivo Andrle ().