珍稀植物细枝岩黄耆自适应群团抽样的比较研究  

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

作　　者：朱光玉[1] 吕勇[1] 

机构地区：[1]中南林业科技大学林业遥感信息工程研究中心,长沙410004

出　　处：《中国农学通报》2011年第19期68-73,共6页Chinese Agricultural Science Bulletin

基　　金：中南林业科技大学人才引进项目"自适应群团抽样在林业中的应用研究"(104-0086);国家自然基金项目"西部干旱地区植被适应性群团抽样技术的研究"(30510103195);科技部社会公益研究专项(2005DIB5J142);国家林业局"948"引进项目"沿海红树林适应性群团抽样技术引进"(2006-4-34)

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。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.

关 键 词：乌兰布和沙漠 细枝岩黄耆 自适应群团抽样 Horvitz-Thompson估计量 Hansen-Hurwitz估计量 

分 类 号：S757[农业科学—森林经理学]