Department of Economics and Statistics, University of Siena, Piazza S. Francesco, Siena, Italy.
CREA Research Centre for Forestry and Wood, Viale Santa Margherita, Arezzo, Italy.
Biom J. 2020 Nov;62(7):1810-1825. doi: 10.1002/bimj.201900377. Epub 2020 Jun 28.
The estimation of individual values (marks) in a finite population of units (e.g., trees) scattered onto a survey region is considered under 3P sampling. For each unit, the mark is estimated by means of an inverse distance weighting interpolator. Conditions ensuring the design-based consistency of maps are considered under 3P sampling. A computationally simple mean squared error estimator is adopted. Because 3P sampling involves the prediction of marks for each unit in the population, prediction errors rather than marks can be interpolated. Then, marks are estimated by the predictions plus the interpolated errors. If predictions are good, prediction errors are more smoothed than raw marks so that the procedure is likely to better meet consistency requirements. The purpose of this paper is to provide theoretical and empirical evidence on the effectiveness of the interpolation based on prediction errors to prove that the proposed strategy is a tool of general validity for mapping forest stands.
在 3P 抽样下,考虑对散布在调查区域上的有限单元(例如,树木)的个体值(标记)进行估计。对于每个单元,标记通过反距离加权插值器进行估计。考虑了在 3P 抽样下确保地图基于设计一致性的条件。采用了计算简单的均方误差估计器。由于 3P 抽样涉及对总体中每个单元的标记进行预测,因此可以对预测误差而不是标记进行插值。然后,通过预测加上插值的误差来估计标记。如果预测良好,则预测误差比原始标记更平滑,因此该过程更有可能满足一致性要求。本文的目的是提供基于预测误差的插值有效性的理论和经验证据,以证明所提出的策略是一种普遍有效的森林林分制图工具。