A completely data-driven, design-based sampling strategy is proposed for mapping a forest attribute within the spatial units tessellating a survey region. Based on sample data, a model is selected, and model parameters are estimated using least-squares criteria for predicting the attribute of interest within units as a linear function of a set of auxiliary variables. The spatial interpolation of residuals arising from model predictions is performed by inverse distance weighting. The leave-one-out cross validation procedure is adopted for selecting the smoothing parameter used for interpolation. The densities of the attributes of interest within units are estimated by summing predictions and interpolated residuals. Finally, density estimates are rescaled to match the total estimate over the survey region obtained by the traditional regression estimator with the total estimate obtained from the map as the sum of the density estimates within units. A bootstrap procedure accounts for the uncertainty. The consistency of the strategy is proven by incorporating previous results. A simulation study is performed and an application for mapping wood volume densities in the forest estate of Rincine (Central Italy) is described.

中文翻译：

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从模型选择到地图：用于映射森林资源的完全基于设计的数据驱动推理

提出了一种完全由数据驱动、基于设计的采样策略，用于在镶嵌调查区域的空间单元内映射森林属性。基于样本数据，选择模型，并使用最小二乘准则估计模型参数，以将单位内感兴趣的属性预测为一组辅助变量的线性函数。由模型预测产生的残差的空间插值是通过反距离加权来执行的。采用留一法交叉验证程序来选择用于插值的平滑参数。单元内感兴趣的属性的密度是通过对预测和插值残差求和来估计的。最后，重新调整密度估计值以匹配由传统回归估计器获得的调查区域的总估计值与从地图获得的总估计值作为单位内密度估计值的总和。引导程序解释了不确定性。通过结合以前的结果证明了该策略的一致性。进行了一项模拟研究，并描述了在 Rincine（意大利中部）的森林庄园中绘制木材体积密度的应用程序。