A new design for sampling with adaptive sample plots
A new design for sampling with adaptive sample plots
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DOI：
10.1007/s10651-009-0129-9
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发表时间：
2011-06
影响因子：
3.8
通讯作者：
Haijun Yang;C. Kleinn;L. Fehrmann;Shouzheng Tang;S. Magnussen
中科院分区：
文献类型：
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作者：
Haijun Yang;C. Kleinn;L. Fehrmann;Shouzheng Tang;S. Magnussen
自适应整群抽样 (ACS) 是一种对稀有且地理上聚集的群体进行抽样的抽样技术。为了增强 ACS 的实用性，同时保持其一些主要特征，本研究引入了自适应样地设计，与“标准”ACS 相比，该设计有利于现场工作。绘图设计基于条件绘图扩展：如果满足预定义的条件，则在样本点安装较大的绘图（通过预定义的绘图大小因子），而不是较小的初始绘图。这项研究提供了对所提出的自适应绘图设计的统计性能的深入了解。提出了一种无偏设计估计器，并在六张人造树和一张真实树位置图上使用它来估计密度（每公顷的物体数量）。将变异系数方面的性能与没有条件扩展绘图大小的非自适应替代方案进行比较。自适应绘图设计在所有情况下均表现出色，但改进取决于 (1) 抽样总体的结构、(2) 绘图大小因子和 (3) 临界值（触发扩展的最小对象数量）。对于某些空间布置，改进相对较小。自适应设计对于通过适当选择的地块大小因子在稀有且紧密聚集的群体中进行采样可能特别有吸引力。
Adaptive cluster sampling (ACS) is a sampling technique for sampling rare and geographically clustered populations. Aiming to enhance the practicability of ACS while maintaining some of its major characteristics, an adaptive sample plot design is introduced in this study which facilitates field work compared to “standard” ACS. The plot design is based on a conditional plot expansion: a larger plot (by a pre-defined plot size factor) is installed at a sample point instead of the smaller initial plot if a pre-defined condition is fulfilled. This study provides insight to the statistical performance of the proposed adaptive plot design. A design-unbiased estimator is presented and used on six artificial and one real tree position maps to estimate density (number of objects per ha). The performance in terms of coefficient of variation is compared to the non-adaptive alternative without a conditional expansion of plot size. The adaptive plot design was superior in all cases but the improvement depends on (1) the structure of the sampled population, (2) the plot size factor and (3) the critical value (the minimum number of objects triggering an expansion). For some spatial arrangements the improvement is relatively small. The adaptive design may be particularly attractive for sampling in rare and compactly clustered populations with an appropriately chosen plot size factor.