Distribution of natural Korean pines in Baihe Forestry Bureau based on spatial models.

WANG Shuo; LI Feng-ri; ZHAO Ying-hui; ZHEN-zhen.

doi:10.13332/j.1000-1522.20150105

Journal of Beijing Forestry University > 2015 > 37(10): 73-85. > DOI: 10.13332/j.1000-1522.20150105

WANG Shuo, LI Feng-ri, ZHAO Ying-hui, ZHEN-zhen.. Distribution of natural Korean pines in Baihe Forestry Bureau based on spatial models.[J]. Journal of Beijing Forestry University, 2015, 37(10): 73-85. DOI: 10.13332/j.1000-1522.20150105

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Distribution of natural Korean pines in Baihe Forestry Bureau based on spatial models.

College of Forestry, Northeast Forestry University, Harbin, Heilongjiang, 150040, P. R. China.

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Received Date: April 02, 2015
Published Date: October 30, 2015

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Abstract

Based on field data of 772 permanent plots in Baihe Forestry Bureau, Changbai Mountains area of northeastern China, we established global models, including Logistic and Poisson, using least square method, and local models (GWR, geographically weighted regression), including GWLR (geographically weighted logistic regression) and GWPR (geographically weighted Poisson regression), to predict distribution of natural Korean pines (Pinus koraiensis). The results showed that slope and average DBH (diameter at breast height) of trees in sub-compartment had significant influence on the distribution of the natural Korean pines, which were mainly found in the eastern and southwestern area of the bureau, and few in the north. A comparison of AICs and spatial autocorrelation of global and local model residuals showed that GWR had obviously smaller AICs and more desirable model residuals (significant decrease of spatial autocorrelation) than global models. Thus, GWR could efficiently solve spatial heterogeneity of plots and improve the accuracy of predicting occurrence probability and count of natural Korean pines. This study would provide theoretical basis for predicting distribution of natural Korean pines in large scale forest management.
- natural Korean pine,
- Logistic,
- Poisson,
- geographically weighted regression (GWR)

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References

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