刘胜刚, 余哲修, 欧光龙, 等. 基于GWR思茅松单木含碳量模型的构建及空间分布研究[J]. 西南林业大学学报(自然科学), 2022, 42(6): 105–113 . DOI: 10.11929/j.swfu.202108069
引用本文: 刘胜刚, 余哲修, 欧光龙, 等. 基于GWR思茅松单木含碳量模型的构建及空间分布研究[J]. 西南林业大学学报(自然科学), 2022, 42(6): 105–113 . DOI: 10.11929/j.swfu.202108069
Liu Shenggang, Yu Zhexiu, Ou Guanglong, Liu Chang. The Study on Spatial Distribution and Spatial Model Establishing Based on GWR of Carbon Content of Individual Tree for Pinus kesiya var. langbianensis in Natural Forest[J]. Journal of Southwest Forestry University, 2022, 42(6): 105-113. DOI: 10.11929/j.swfu.202108069
Citation: Liu Shenggang, Yu Zhexiu, Ou Guanglong, Liu Chang. The Study on Spatial Distribution and Spatial Model Establishing Based on GWR of Carbon Content of Individual Tree for Pinus kesiya var. langbianensis in Natural Forest[J]. Journal of Southwest Forestry University, 2022, 42(6): 105-113. DOI: 10.11929/j.swfu.202108069

基于GWR思茅松单木含碳量模型的构建及空间分布研究

The Study on Spatial Distribution and Spatial Model Establishing Based on GWR of Carbon Content of Individual Tree for Pinus kesiya var. langbianensis in Natural Forest

  • 摘要: 以云南澜沧县思茅松天然林单木为研究对象,测定了解析木各个器官的含碳量,采用地理加权回归模型,以地形因子和测树学因子作为变量,对思茅松单木树枝、树叶、树干、树皮、树根和全树含碳量进行模型构建,同时构建最小二乘模型作为比较,分析模型优劣。结果表明:思茅松单木在250 m区域范围内不同器官的含碳量存在空间自相关,因此在研究思茅松单木含碳量的问题时,不能忽略空间自相关的影响。半变异函数能够定量对思茅松各器官含碳量的空间异质性进行分析,200 m范围的空间异质性主要是由随机部分引起的,在研究类似的问题时要确定适当的研究尺度。地理加权回归模型具有较好的拟合效果,各维度含碳量模型的R2均在0.8以上,虽然部分器官含碳量模型的AIC比OLS的略大,但地理加权回归模型的均方根误差均小于最小二乘模型,地理加权回归模型的残差也小于最小二乘模型的残差,模型精度优于最小二乘模型,同时说明地理加权回归模型能够有效解释最小二乘模型无法解释的空间异质性。

     

    Abstract: The artical takes individual tree from the Pinus kesiya var. langbianensis natural forest survey in Lancang County, Yunnan Province as the research object, the carbon content of different organs of the analytical trees was measured, and the branches, leaves, trunks, and bark of the individual P. kesiya var. langbianensis were constructed using a geographically weighted regression model. The topographic factor and dendrometric factor model of the carbon content of the root and the whole tree, and compare the least square model to analyze the pros and cons of the model. The results show that there is spatial autocorrelation in the carbon content of each organ of P. kesiya var. langbianensis within 250 m. Therefore, when studying the carbon content of P. kesiya var. langbianensis, the influence of spatial autocorrelation cannot be ignored. The semivariogram can quantitatively analyze the spatial heterogeneity of the carbon content of each organ of P. kesiya var. langbianensis. The spatial heterogeneity in the 200 m range is mainly caused by the random part. When studying similar problems, appropriate research should be determined scale. The geographically weighted regression model has a good fitting effect. The coefficient of determination of the carbon content model in each dimension is above 0.8. Although the AIC of the carbon content model for some organs is slightly larger than that of the OLS, the RMSE of geographically weighted regression model is less than that of the least squares model. The residual error of the geographically weighted regression model is also less than that of the least squares model. Obviously, the model accuracy is better than that of the least squares model. At the same time, it shows that the geographically weighted regression model can effectively explain the spatial heterogeneity that the least squares model cannot explain.

     

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