Localizing general models with classification and regression trees
Localizing general models with classification and regression trees
复制标题
使用分类和回归树本地化通用模型
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
A. Kangas
中科院分区：
文献类型：
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作者：
M. Räty;A. Kangas
摘要 通常，在森林清查中，统计树的体积是通过国家层面估计的体积模型来预测的。如果由于一个或多个未知预测变量导致树形式存在空间变化，则这种全局模型在区域上就不是无偏的。如果模型局限于每个区域或分区，则可以减少或消除这种区域偏差。如果该区域可以根据茎形状分为均匀区域，则定位是最容易的。本研究测试了定位结果是否取决于划分方式和分区大小。研究区域在空间上被划分为具有全局模型残差或局部空间指数 G i * 的同质子区域，或同时具有分类树和回归树，其叶子形成子区域。此外，还创建了另外两个空间分区：行政森林中心和空间大小相等的分区。将局部模型与全局模型进行比较。局部模型的均方根误差 (RMSE) 中值和平均值较小，但最大值超过了整体全局模型 RMSE。本地化使当地 RMSE 平均降低了 1-6%。尽管回归树中的总标准误差和 RMSE 稍小，但空间划分之间的差异很小。回归树划分中只有50±8%的分区在空间上是同质的，这表明划分标准或划分方法不充分。
Abstract Typically, in forest inventory the volume of tally trees is predicted with a volume model estimated at national level. Such a global model is not unbiased regionally if there is spatial variation in the tree form due to one or more unknown predictors. This regional bias could be reduced or removed if the models were localized to each region or subarea. The localization is easiest if the area can be divided into homogeneous areas with respect to stem form. This study tested whether the localization results depend on the way the division is made and on the size of the subareas. The study area was divided spatially into homogeneous subareas with residuals of the global model or with the local spatial index, G i *, or both with classification and regression trees, the leaves of which formed the subareas. In addition, two other spatial divisions were created: an administrative forest centre and spatially equal-sized subarea divisions. The localized models were compared with the global model. The root mean squared errors (RMSEs) of localized models were smaller in median and in mean, but maximum values exceeded the overall global model RMSE. The localization reduced local RMSEs on average by 1–6%. The differences between the spatial divisions were small, although the aggregate standard errors and RMSEs were slightly smaller in regression trees. Only 50 ± 8% of the subareas were spatially homogeneous in regression tree divisions, which suggests that either the division criteria or the division method were inadequate.