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中国农学通报

中国农学通报 ›› 2011, Vol. 27 ›› Issue (19): 68-73.

• 林学 园艺 园林 • 上一篇    下一篇

珍稀植物细枝岩黄耆自适应群团抽样的比较研究

朱光玉 吕勇   

  • 收稿日期:2011-03-02 修回日期:2011-04-02 出版日期:2011-08-05 发布日期:2011-08-05
  • 基金资助:

    国家自然基金项目;科技部社会公益研究专项;国家林业局“948”引进项目;中南林业科技大学人才引进项目

Comparison of Adaptive Cluster Samplings for Inventory of Rare Plant Hedysarum scoparium

  • Received:2011-03-02 Revised:2011-04-02 Online:2011-08-05 Published:2011-08-05

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摘要：

对于稀少、群团状总体的调查，自适应群团抽样（adaptive cluster sampling，简称ACS）被认为是一种有效的抽样方法。针对中国西部森林植被的集聚、稀少的分布特征，以乌兰布和沙漠边缘地区细枝岩黄耆株数密度为研究对象，进行6种抽样方法（简单放回随机抽样、简单不放回随机抽样、最初样本放回基于修正Hansen-Hurwitz估计量的ACS、最初样本不放回基于修正Hansen-Hurwitz估计量的ACS、最初样本放回基于修正Horvitz-Thompson估计量的ACS和最初样本不放回基于修正Horvitz-Thompson估计量的ACS）的重复抽样模拟试验，并对模拟试验的结果进行了比较和分析，指出最初样本不放回基于Horvitz-Thompson估计量的自适应群团抽样的效果最佳，其均值估计相对误差为0.037%，均值方差估计为0.03571。研究结果有助于提高稀少、群团状森林资源的清查的精度和效率。

关键词: 菹草, 菹草, 采割时间, 营养成分

Abstract:

Adaptive cluster sampling (ACS) appears to be an effective method for sampling rare and clustering population. The forest vegetation is most rare and clustering in west China. Based on the density of Hedysarum scoparium in Ulanbuh desert edge, four kinds of adaptive cluster sampling methods and two simple rand sampling methods had been carried out, there were simple random sampling with primary units selected with replacement, simple random sampling with primary units selected without replacement, adaptive cluster sampling based on Hansen-Hurwitz estimator with primary units selected with replacement, adaptive cluster sampling based on Hansen-Hurwitz estimator without primary units selected with replacement, adaptive cluster sampling based on Horvitz-Thompson estimator with primary units selected with replacement, adaptive cluster sampling based on Horvitz-Thompson estimator with primary units selected without replacement, and simulation resampling of six methods had also been conducted, the result of which had been compared. The result showed that the design of adaptive cluster sampling using Horvitz-Thompson estimator with initial sample was selected without replacement was more effective than the others, the mean estimator relative error of which was 0.037%, and the mean variance estimator was 0.03571. These results were propitious to increase the precision and efficiency for forest inventory.