广东西樵山国家森林公园森林碳储量空间分布研究.

To clarify the influence of different models on the accuracy of spatial distribution of forest carbon storage estimation, this study used geographically weighted regression model (GWR), spatial error model (SEM) and ordinary least squares model (OLS) to analyze the fitting effect of the three models and the factors affecting the forest carbon storage in the study area based on the forest resource management "one map" in Guangdong Xiqiao Mountain National Forest Park in 2020. Global Moran's I and local Moran's I were used to respectively describe the global spatial auto-correlation and the spatial distribution of the model residuals, to illustrate the differences between the models in the action of spatial heterogeneity. GWR was used to map the spatial distribution of forest carbon storage in the study area. The results showed that the influence size of various parameter estimates on forest carbon storage was constantly changing for GWR at different positions. GWR significantly outperformed SEM and OLS in terms of the data fitting, while OLS had the worst fit. GWR yielded a wide range of parameter estimates. Moreover, the range of all parameter estimates of GWR included the parameter estimates of OLS and SEM, which obtained a good localized spatial distribution effect of model residues and high model stability. The central forest of the study area had more forest carbon storage, and the fitting deviation of GWR was 1. 36 t/ hm2, which was minimal in all models. [ABSTRACT FROM AUTHOR]