Modeling Individual Biomass of Pinus kesiya var. langbianensis Natural Forests by Geographically Weighted Regression

OU Guang-long; WANG Jun-feng; XIAO Yi-fa; XU Hui

The stem, branch, leaf biomass of 63 sampling trees, and root biomass of 30 trees at Simao pine (Pinus kesiya var. langbianensis) natural forest were investigated in Simao district of Yunnan Province. Based on the model selected by ordinary least square (OLS), the models of the tree stem, branch, leaf biomass, aboveground biomass, root biomass and whole tree biomass were built by geographically weighted regression (GWR). The results showed that: (1) the values of the coefficient of determination (R2) of GWR were greater than that of OLS models, and the R2 of the GWR models were greater than 0.950 except the leaf biomass model. Akaike's information criterions (AIC) of GWR were less than that of OLS models, the absolute value of the mean relative error (EE) and the mean absolute relative error (RMA) were less than that of OLS model except the branch biomass model. So the fitting effect of GWR outperforms OLS models. (2) For individual tree biomass models, GWR overcame the heteroscedasticity of the OLS models at a certain extent.

1. School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China

2. Key Laboratory of Biodiversity Conservation in Southwest China of State Forest Administration, Southwest Forestry University, Kunming 650224, Yunnan, China

Received Date: 2013-10-20

Abstract: The stem, branch, leaf biomass of 63 sampling trees, and root biomass of 30 trees at Simao pine (Pinus kesiya var. langbianensis) natural forest were investigated in Simao district of Yunnan Province. Based on the model selected by ordinary least square (OLS), the models of the tree stem, branch, leaf biomass, aboveground biomass, root biomass and whole tree biomass were built by geographically weighted regression (GWR). The results showed that: (1) the values of the coefficient of determination (R2) of GWR were greater than that of OLS models, and the R2 of the GWR models were greater than 0.950 except the leaf biomass model. Akaike's information criterions (AIC) of GWR were less than that of OLS models, the absolute value of the mean relative error (EE) and the mean absolute relative error (RMA) were less than that of OLS model except the branch biomass model. So the fitting effect of GWR outperforms OLS models. (2) For individual tree biomass models, GWR overcame the heteroscedasticity of the OLS models at a certain extent.

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Reference (18)

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Modeling Individual Biomass of Pinus kesiya var. langbianensis Natural Forests by Geographically Weighted Regression

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