基于空间模型的白河林业局天然红松分布

根据长白山地区白河林业局的772块固定标准地调查数据,分别建立以最小二乘法为基础的全局模型(Logistic和Poisson)和以地理加权回归模型(GWR)为基础的局域模型(GWLR和GWPR)来预估该局天然红松的分布情况。结果表明:天然红松分布受坡度和小班内树木平均胸径的影响最为显著,主要分布在东部和西南部地区,在北部的部分地区也有分布,但数量相对较少。通过比较全局模型和局域模型的AIC值和模型残差的空间相关性指数发现:GWR模型的AIC值明显小于全局模型,并且能够产生更为理想的模型残差,即模型残差的空间相关性明显减小,因此,GWR模型可以有效解决样地间空间异质性问题,有利于提高红松分布的预测精度。本研究将为大区域森林经营中的天然红松分布及其株数估测提供理论依据。 

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

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.