Comparison of estimators of variance for forest inventories with systematic sampling - results from artificial populations
Comparison of estimators of variance for forest inventories with systematic sampling - results from artificial populations
复制标题
森林清单方差估计量与系统抽样的比较——人工种群的结果
DOI：
10.1186/s40663-020-00223-6
复制
发表时间：
2020
影响因子：
4.1
通讯作者：
S. Schnell
中科院分区：
文献类型：
--
作者：
S. Magnussen;R. McRoberts;J. Breidenbach;T. Nord‐Larsen;G. Ståhl;L. Fehrmann;S. Schnell
大面积森林资源清查经常使用规则网格（从一个随机开始）的抽样地点，以确保在整个调查人口空间内有统一的抽样密度。此设计不存在方差的设计无偏估计量。通常情况下，使用适用于简单随机抽样（SRS）的准默认估计量，即使它带有高估方差的风险。为了更好地利用系统抽样的精度，我们评估了五个方差估计量的性能，包括准默认值。在这项研究中，模拟系统抽样适用于人工人口与对比的协方差结构和有或没有线性趋势。我们比较了SRS，Matérn的，连续的差异复制，里普利的，和D 'Orazio的方差估计得到的结果。结果与SRS方差估计量的四种替代方法获得的方差强相关，并且在所有研究设置中始终比SRS估计量更接近目标设计方差。后者总是产生最大的高估。在具有接近零空间自相关性的群体中，所有估计量表现相同，并提供接近实际设计方差的估计值。结论：在没有线性趋势的情况下，SDR和DOR估计量在方差估计量更窄地分布在基准点周围时最好;但在最小平均绝对偏差方面，Matérn估计量领先。对于强或中等线性趋势，Matérn估计量是首选。在大的人口，和一个低的抽样强度，性能的调查估计变得更加相似。
Background Large area forest inventories often use regular grids (with a single random start) of sample locations to ensure a uniform sampling intensity across the space of the surveyed populations. A design-unbiased estimator of variance does not exist for this design. Oftentimes, a quasi-default estimator applicable to simple random sampling ( SRS ) is used, even if it carries with it the likely risk of overestimating the variance by a practically important margin. To better exploit the precision of systematic sampling we assess the performance of five estimators of variance, including the quasi default. In this study, simulated systematic sampling was applied to artificial populations with contrasting covariance structures and with or without linear trends. We compared the results obtained with the SRS , Matérn’s, successive difference replication, Ripley’s, and D’Orazio’s variance estimators. Results The variances obtained with the four alternatives to the SRS estimator of variance were strongly correlated, and in all study settings consistently closer to the target design variance than the estimator for SRS . The latter always produced the greatest overestimation. In populations with a near zero spatial autocorrelation, all estimators, performed equally, and delivered estimates close to the actual design variance. Conclusion Without a linear trend, the SDR and DOR estimators were best with variance estimates more narrowly distributed around the benchmark; yet in terms of the least average absolute deviation, Matérn’s estimator held a narrow lead. With a strong or moderate linear trend, Matérn’s estimator is choice. In large populations, and a low sampling intensity, the performance of the investigated estimators becomes more similar.