Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China
Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China
复制标题
东北栎阔叶天然混交林林分最高与林分平均高度关系的空间自回归模型
作者：
娄明华;张会儒;雷相东;李春明;臧灏
林分顶部和林分平均高度的关系对于森林生长和产量建模很重要，但尚未针对天然混交林进行探索。对立柱顶部和立柱平均高度的观测可能呈现空间依赖性或自相关性，在建模时应考虑到这一点。利用九个空间权重矩阵内的联立自回归（SAR）模型，包括空间滞后模型（SLM）、空间杜宾模型（SDM）和空间误差模型（SEM），对混合蒙古栎的林分顶部和林分平均高度关系进行建模。前莱德布。以普通最小二乘法（OLS）为基准模型，对东北地区阔叶自然林分进行了研究。结果表明，林分最高与林分平均高度之间存在高度线性关系，OLS模型残差存在正空间自相关模式。此外，无论使用哪种空间权重矩阵，SEM和SDM在减少模型残差的空间依赖性和模型拟合方面都比OLS表现更好。 SEM 优于 SDM。 SLM 几乎没有降低模型残差的空间自相关性。在SEM的9个空间矩阵中，车连续矩阵在模型拟合中表现最好，其次是反距离的二次方（1/d2）和局部统计模型矩阵（LSM）。
The relationship of stand top and stand mean height is important for forest growth and yield modeling, but it has not been explored for natural mixed forests. Observations of stand top and stand mean height can present spatial dependence or autocorrelation, which should be considered in modeling. Simultaneous autoregressive (SAR) models, including spatial lag model (SLM), spatial Durbin model (SDM) and spatial error model (SEM), within nine spatial weight matrices were utilized to model the stand top and stand mean height relationship in the mixed Quercus mongolica Fisch. ex Ledeb. broadleaved natural stands of Northeast China, using ordinary least squares (OLS) as a benchmark model. The results showed that there was a high linear relationship between stand top and stand mean height and that there was a positive spatial autocorrelation pattern in model residuals of OLS. Moreover, SEM and SDM performed better than OLS in terms of reducing the spatial dependence of model residuals and model fitting, regardless of which spatial weight matrix was used. SEM was better than SDM. SLM scarcely reduced the spatial autocorrelation of model residuals. Among nine spatial matrices in SEM, rook contiguous matrix performed best in model fitting, followed by inverse distances raised to the second power (1/d2) and local statistics model matrix (LSM).
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影响因子：
3.1
作者：
L. Zhang;J. Gove;L. Heath
通讯作者：
L. Zhang;J. Gove;L. Heath
影响因子：
5.9
作者：
J. Lennon
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1.5
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M. Watt;A. Meredith;P. Watt;Aaron Gunn
影响因子：
2.8
作者：
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A. Foggie
影响因子：
1.4
作者：
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通讯作者：
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