title: [Stand density estimation based on the measurement of spatial structure].
abstract: This study estimated stem density by combining distance sampling with stand spatial structure investigation techniques. We tested the statistical performance of two investigative methods (selecting the fourth- or sixth-nearest tree to the sample location) and three different density estimators (Prodan, Persson and Thompson). Different spatial distribution patterns influenced the performance of these estimators. Prodan's estimator was unbiased for uniform patterns, and it produced increasing bias with increasing spatial clustering. Persson's estimator produced consistent positive bias for uniform and random patterns, with the smallest bias for clustered patterns. Thompson's estimator was robust for random or near-random patterns, and it produced positive and negative bias for uniform and clustered patterns, respectively. No significant performance difference was found between selecting the fourth- and the sixth-nearest trees with the same sample size of 49. Thus, we could combine distance sampling with spatial structure investigation techniques.
authors.forename: Hong-Xiang
authors.initials: HX
authors.lastname: Wang
authors.forename: Gang-Ying
authors.initials: GY
authors.lastname: Hui
authors.forename: Gong-Qiao
authors.initials: GQ
authors.lastname: Zhang
authors.forename: Yuan-Fa
authors.initials: YF
authors.lastname: Li
authors.forename: En
authors.initials: E
authors.lastname: Liu
mesh_terms: D065928:Forests; D018547:Plant Stems; D062206:Spatial Analysis; D014197:Trees / Q000254:growth & development*