The bivariate distribution characteristics of spatial structure in natural Korean pine broad-leaved forest
Article first published online: 28 MAY 2012
© 2012 International Association for Vegetation Science
Journal of Vegetation Science
Volume 23, Issue 6, pages 1180–1190, December 2012
How to Cite
Li, Y., Hui, G., Zhao, Z., Hu, Y. (2012), The bivariate distribution characteristics of spatial structure in natural Korean pine broad-leaved forest. Journal of Vegetation Science, 23: 1180–1190. doi: 10.1111/j.1654-1103.2012.01431.x
- Issue published online: 7 NOV 2012
- Article first published online: 28 MAY 2012
- Manuscript Accepted: 22 APR 2012
- Manuscript Received: 30 NOV 2011
- National Sci-Tech Support Plan of China. Grant Number: 2012BAD22B03
- Broad-leaved forest;
- Uniform angle index
Spatial structure is important in describing forest stand structure and change. We present a new method for the quantitative analysis of forest spatial structure based on the relationship of nearest neighbour tree groups.
Six hundred m a.s.l., Dongdapo Natural Reserve, Jiaohe, Jilin Province, China
Six plots in three common stand types of natural Korean pine broad-leaved forest in northeast China were used to validate the method. Each plot measured 100 × 100 m, and all trees with DBH ≥5 cm were marked and located using a Total Station. We calculated bivariate distribution of the structural parameters, uniform angle index, mingling and dominance using Winkelmass and Excel software.
Most trees in the forest were highly mixed by species and randomly distributed. Individuals with high DBH values were typically surrounded by other species; trees within stochastic distribution patterns were usually surrounded by different species; and medium-sized trees were randomly distributed.
The bivariate distribution of structural parameters can provide more direct and useful information about the heterogeneity of spatial structure than can univariate distributions or other conventional stand descriptors. This could be helpful for selective thinning in continuous cover forest management and in modelling and restoring forests.