Kriging with External Drift in Model Localization
Kriging with External Drift in Model Localization
复制标题
模型定位中的外部漂移克里金法
DOI：
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发表时间：
2011
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;J. Heikkinen;A. Kangas
正常0 21假FI ZH-CN TH当在较小的子区域中使用大区域模型时，结果可能会有偏差，即使模型通常是无偏的。针对这种偏差调整大面积模型的一种方法是克里金法，即在邻近观测的帮助下对预测进行校正。变异函数表示相邻观测值之间的空间相关性，作为距离的函数。使用所选的变异函数和描述一般均值的漂移模型，然后预测给定对象的变量值。本研究的目的是（1）检验在一个大的研究区域拟合的全球表格高度模型的残差的空间相关性，以及（2）使用这种相关性预测相同的变量。该数据集包括19175苏格兰松（欧洲赤松L.）芬兰第九次国家森林资源清查。选择嵌套球面和贝塞尔变异函数进行克里金计算。在嵌套模型中，林分间的短期相关和长期相关被分别建模。我们使用10折交叉验证来评估所选择的变异函数模型。我们将邻居的数量限制在20到100之间，即在8-17公里半径内的距离。在全球一级，稳定估计需要30个邻值，如果有60个邻值，克里金法的RMSE低于全球拟合模型。在区域一级，我们得到了更好的估计比区域重新拟合模型时，邻居的数量为60变差函数模型。克里格法在区域一级的偏差很小（区域RMSE的0.8%）。总之，残差中存在约6 km的空间相关性，但改进预测所需的克里金邻域的大小大于范围。MCFNS 3（1）：1-14.
Normal 0 21 false false false FI ZH-CN TH When a large-area model is utilized in smaller sub-areas, the results may be biased, even though the model is unbiased in general. One method for adjusting the large-area models for such bias is kriging, in which the predictions are corrected with the help of neighbouring observations. A variogram represents the spatial correlation between neighbouring observations as a function of distance. With the selected variogram and drift model that describes the general mean, the variable values for given objects are then predicted. The aim of this study was (1) to test for a spatial correlation in the residuals of a global form height model fitted over a large study area and (2) to use this correlation in prediction of the same variable. The dataset consisted of 19 175 Scots pines ( Pinus sylvestris L.) from the 9 th National Forest Inventory of Finland. Nested spherical and Bessel variograms were selected for the kriging calculations. In nested models the short-range intrastand correlation and long-range correlation are modelled separately. We used 10-fold cross-validation to evaluate the variogram models selected. We limited the number of neighbours from 20 to 100, i.e. at distances within an 8-17-km radius. At the global level, 30 neighbours were needed for stable estimates, and with 60 neighbours the RMSEs of kriging were lower than the globally fitted model. At the regional level, we obtained better estimates than with regionally re-fitted models when the number of neighbours was 60 for both variogram models. The biases at the regional level in the kriging were small (0.8% of the regional RMSE). In conclusion, there was an app. 6-km spatial correlation in the residuals, but the size of the kriging neighbourhood required for improving prediction was larger than the range. MCFNS 3(1):1-14.