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- W2356004810 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 R2of 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." @default.
- W2356004810 created "2016-06-24" @default.
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- W2356004810 date "2014-01-01" @default.
- W2356004810 modified "2023-09-24" @default.
- W2356004810 title "Modeling Individual Biomass of Pinus kesiya var. langbianensis Natural Forests by Geographically Weighted Regression" @default.
- W2356004810 hasPublicationYear "2014" @default.
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