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The influence of sampling unit size and spatial arrangement patterns on neighborhood-based spatial structure analyses of forest stands
Author(s) -
Hongxiang Wang,
Gongqiao Zhang,
Hui Gangying,
Yuanfa Li,
Yanbo Hu,
Zhonghua Zhao
Publication year - 2016
Publication title -
forest systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.455
H-Index - 25
eISSN - 2171-9845
pISSN - 2171-5068
DOI - 10.5424/fs/2016251-07968
Subject(s) - dominance (genetics) , forest structure , sampling (signal processing) , spatial analysis , common spatial pattern , spatial ecology , statistics , tree (set theory) , sample size determination , mathematics , forest management , tree structure , ecology , forestry , geography , computer science , biology , canopy , binary tree , algorithm , combinatorics , biochemistry , filter (signal processing) , gene , computer vision

Aim of the study: Neighborhood-based stand spatial structure parameters can quantify and characterize forest spatial structure effectively. How these neighborhood-based structure parameters are influenced by the selection of different numbers of nearest-neighbor trees is unclear, and there is some disagreement in the literature regarding the appropriate number of nearest-neighbor trees to sample around reference trees. Understanding how to efficiently characterize forest structure is critical for forest management.

Area of study: Multi-species uneven-aged forests of Northern China

Material and methods: We simulated stands with different spatial structural characteristics and systematically compared their structure parameters when two to eight neighboring trees were selected.

Main results: Results showed that values of uniform angle index calculated in the same stand were different with different sizes of structure unit. When tree species and sizes were completely randomly interspersed, different numbers of neighbors had little influence on mingling and dominance indices. Changes of mingling or dominance indices caused by different numbers of neighbors occurred when the tree species or size classes were not randomly interspersed and their changing characteristics can be detected according to the spatial arrangement patterns of tree species and sizes.

Research highlights: The number of neighboring trees selected for analyzing stand spatial structure parameters should be fixed. We proposed that the four-tree structure unit is the best compromise between sampling accuracy and costs for practical forest management

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