Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) — ##plugins.generic.mcfnsReadingTools.readingTools##

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Nearest-tree and Variable Polygon Sampling

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1.Title##plugins.generic.mcfnsReadingTools.titleLabel##Nearest-tree and Variable Polygon Sampling
2.Creator##plugins.generic.mcfnsReadingTools.creators##Kim Iles; Kim Iles & Associates Ltd.; Sjedinjene Američke Države
3.Subject##plugins.generic.mcfnsReadingTools.keywords##n-tree sampling; nearest tree; Voronoi polygons; unbiased estimates
3.Subject##plugins.generic.mcfnsReadingTools.disciplines##Mensuration; sampling
4.Description##plugins.generic.mcfnsReadingTools.abstract##Sampling a nearest neighbor is often presented as a Hansen-Hurwitz or Horvitz-Thompsonestimation process. This may not be the most informative viewpoint, and measuring the probability ofselection is not necessary. The measurement of the nearest object as a “depth” over the selection area canbe done by a sampling process, and the total estimated without knowing the polygon areas. The process isunbiased, quite general, and easy to understand. It can be extended to more than just the nearest objectto a sample point and to many different polygon shapes. This paper is an extension, simplification andgeneralization of an earlier paper in this journal (Iles, K. 2009. “Nearest-tree” estimations—A discussionof their geometry, MCFNS 1(2), pp. 47–51.), but does not require a random orientation or weighting forthe direction of measurement from the tree to the polygon border.
5.Publisher##plugins.generic.mcfnsReadingTools.publisher##Contemporary Journal Concept Press
6.Contributor##plugins.generic.mcfnsReadingTools.contributors##
7.Date##plugins.generic.mcfnsReadingTools.date##2023-04-30
8.Type##plugins.generic.mcfnsReadingTools.type##Text.Serial.Journal; Analytical
9.Format##plugins.generic.mcfnsReadingTools.format##PDF (engleski); application/pdf
10.Identifier##plugins.generic.mcfnsReadingTools.identifier##https://mcfns.com/index.php/Journal/hr/article/view/15.1
11.Source##plugins.generic.mcfnsReadingTools.source##Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Svezak 15 Br. 1: MCFNS April 30, 2023; 1-7 (7)
11.Source##plugins.generic.mcfnsReadingTools.onlineIssn##1946-7664
12.Language##plugins.generic.mcfnsReadingTools.language##en
13.Relation##plugins.generic.mcfnsReadingTools.suppFiles##
14.Coverage##plugins.generic.mcfnsReadingTools.coverageGeo##
15.Rights##plugins.generic.mcfnsReadingTools.rights##Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) 2023