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Position: Home > Articles > Study on Spatial Autocorrelation of Forest Biomass Forest Engineering 2018 (2) 35-39

森林生物量的空间自相关性研究

作  者：
王维芳;董薪明;董小枫;吕丹阳;苏婷婷;郑安然
单  位：
东北林业大学林学院
关键词：
生物量;空间自相关性;多元线性回归模型;地理加权回归模型
摘  要：
森林生物量是森林生态系统物质循环与能量流动的基础,可以很好的衡量森林生产力。研究森林生物量的空间分布及其变化规律,对揭示地表空间变化规律具有重要意义。本研究以帽儿山林区为主要研究区域,以2004年帽儿山林区固定样地调查数据为基础,对帽儿山地区生物量进行全局自相关性分析和局部自相关性分析,并在此基础上使用R软件建立生物量与各地理因子、生物因子的多元回归模型;使用GWR软件,以每公顷株数、平均胸径和高程为解释变量,建立GWR模型。结果表明:帽儿山地区的森林生物量具有空间正相关性,在本研究区域内地理加权回归模型较多元回归模型AIC值降低了90,决定系数R~2和调整型决定系数R~2均有提升,GWR模型具有更高的拟合精度。
译  名：
Study on Spatial Autocorrelation of Forest Biomass
作  者：
Wang Weifang;Dong Xinming;Dong Xiaofeng;Lv Danyang;Su Tingting;Zheng Anran;College of Forestry, Northeast Forestry University;
关键词：
Biomass;;spatial autocorrelation;;multiple linear regression model;;geographical weighted regression model
摘  要：
Forest biomass is the basis of material circulation and energy flow of forest ecosystem, which can measure forest productivity well. It is of great significance to study the spatial distribution and variation of forest biomass and to reveal and to reveal the law of surface space change. One of the main areas of this research was the region of Mao'er Mountain forest, and the global autocorrelation analysis and local autocorrelation analysis were carried out on the biomass of Mao'er Mountain forest region, according to the survey data of the fixed sample plots in Mao'er Mountain region in 2004. Then, R software was used to establish multiple linear regression model of biomass, geographical factors and biological factors. GWR4.0 software was used to establish the geographical weighted regression model with the number of plants per hectare, the average diameter at breast height and the elevation as explanatory variables. The results showed that the forest biomass in the Mao'er Mountain area had positive spatial autocorrelation. In this study area, the AIC value of Geographical Weighted Regression Model was 90 lower than the multi-regression model, R square value and adjusted R square value were both increased, and the GWR model had higher fitting accuracy.

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