两阶段自适应群团抽样在沙漠边缘植被调查中的比较

两阶段自适应群团抽样(two-stage ACS)是一种新兴自适应群团抽样(ACS)设计,它能在一定程度上解决自适应群团抽样最终样本量不确定的缺陷.概述两阶段自适应群团抽样方法的技术思路和原理.以实地调查的花棒数据为研究对象,进行4种两阶段自适应群团抽样方法的比较和分析,指出不跨越边界的基于Horvitz-Thompson估计量的两阶段自适应群团抽样的效果最佳.     

适应性群团抽样技术在森林资源清查中的应用

介绍一种新的不等概率抽样技术--适应性群团抽样(ACS).概略地介绍了其概念、特点、基本估计方法和研究概况.举例比较适应性群团抽样技术和随机抽样技术在稀疏群团植被调查中的精度和效率.     

两阶群团抽样在森林调查中的估计效率研究

从两阶群团抽样的概念入手，根据两阶群团抽样当群团之间不存在差异成分时与系统抽样或简单随机抽样具有相同的抽样效率的性质，定义了误差扩大因子，提出了效率系数的概念，并推导出了效率系数等于１的临界状态的相关系数ｒ的表达式，为群团抽样设计和效率评估提供了重要依据。     

适用于水平样点群的一个新估计量

采用水平样点群抽样时,需要估计林分的各种属性,本文对此问题提出了一种新的估计方法。本方法的方差比传统的水平样点群无偏估计量的方差小,而且也是无偏的。本文导出了等距离3-点群团所必需的一些公式。采用本文提出的新估计量,能够在不改变样本设计的情况下,减小方差。     

全国森林资源宏观监测抽样设计改进方案探索

针对全国森林资源宏观监测20km×20km的抽样框架设计及包含25个样地的群团抽样设计,以2015年首次宏观监测结果为基础,研究提出了满足各省抽样精度要求、进行适度加密并将群团内样地数减少至9个的改进方案,为优化2016年的全国森林资源宏观监测方案提供了依据。     

适应性群团抽样技术方法和应用研究进展

从3个方面概述适应性群团抽样技术的研究现状和发展趋势:1)分析影响适应性群团抽样技术效率的主要因素--最初抽样方法的设计、网络的大小和数量、标准值的大小和邻域形式的设计;2)介绍适应性群团抽样的不同设计、试验模拟研究和估计方法;3)概述适应性群团抽样技术在稀疏群团分布的鱼类、鸟类和植被资源的调查应用研究.最后,指出适应性群团抽样技术在生态因子和资源调查应用研究方面的发展趋势.     

基于广东省大样地不同群团抽样方案的对比分析

在广东省2012年大样地试点成果的基础上,采用不同群团抽样方案与遥感判读相结合的方式,产出全省森林覆盖率,并与2012年试点成果和连清结果进行对比分析,结果表明大样地群团抽样方法是一种可行高效的方法,且工作量显著低于图斑区划判读的工作量。     

全国森林资源宏观监测的抽样设计与估计方法探索

阐述了全国森林资源宏观监测的抽样框架设计及群团抽样设计;研究提出了针对图斑区划判读和群团样地判读2种方案的抽样估计方法,包括系统抽样、整群抽样和两阶抽样,其中整群抽样更适合于图斑区划判读,而两阶抽样更适合于群团样地判读;9个省的抽样估计结果对比表明,不等群团的整群抽样估计方法对图斑区划判读和群团样地判读都是合适的。     

森林资源监测地面固定样地优化研究

地面固定样地是大区域尺度范围内开展森林资源监测的基本抽样调查单元。地面固定样地对林分的预估精度将直接影响到整体区域监测结果的准确性。从样地类型、空间排列方式、样地尺度大小出发,在1 hm²大样地中采用随机抽样法,通过抽样精度验证在不同类型林分中开展森林资源监测的最优样地类型。结果表明：1)在不同类型林分中开展蓄积量监测的最优样地面积尺度大小不一,在布设地面固定样地时,应当根据林分起源、龄组的不同,确定样地面积、间距;2)在样地面积相同情况下,单一圆形样地抽样监测精度略高于单一方形样地,群团样地的蓄积抽样监测精度明显高于单一样地,四点圆形群团样地抽样监测精度略高于方阵式群团样地。     

群团抽样对西藏森林资源宏观监测结果的影响分析

森林资源监测是国情国力调查的重要组成部分,是林业重要的基础性工作。遥感技术弥补了传统人工抽样调查的不足,然而遥感样地的布设方式、数量及遥感判读精度是宏观森林资源监测值得关注的问题。本研究在西藏自治区2015年森林资源宏观监测成果的基础上,采用不同群团抽样方案与遥感判读相结合的方式,得出全区森林覆盖率,并分析群团内样本数量变动对西藏森林资源宏观监测结果的影响。研究结果表明,当样地内群团数量达到25个时,估计均值与实际森林覆盖率相差最小,变动系数最稳定,抽样精度达到最高94.49%。由此可知,大样地群团抽样方法对森林资源宏观监测来说是一种可行高效的方法,且工作量显著低于图斑区划判读的工作量。     

Estimating the volume of forest growing stock using auxiliary information derived from relascope or ocular assessments

The aim of this study is to demonstrate the potential of integrating probabilistic sampling and estimation with the conventional technique referred to as forest inventory by compartments. The objective of this paper is to propose two strategies for the assessment of growing stock volume using two-phase sampling, namely: (i) relascope basal area estimation performed on first-phase sampling points followed by volume estimation performed on a sub-sample of points selected in the second phase; (ii) ocular evaluation of growing stock volume performed on first-phase sampling plots of fixed size followed by volume estimation performed on a sub-sample of plots selected in the second phase. The effectiveness of using the auxiliary information gathered in the first phase is assessed by comparing the double-expansion estimator of total volume which depends solely on the second-phase sample with the two-phase ratio estimator which instead calibrates the double-expansion estimator on the basis of first-phase information. Conservative estimators of sampling variances and confidence intervals are derived for both the estimators. As is usual in forest inventories, first-phase sampling is assumed to be performed on a systematic random grid while three different schemes are considered for drawing the second-phase sample: simple random sampling without replacement, stratified sampling and 3-P sampling. The performance of double-expansion and ratio estimators under the three schemes adopted in the second phase is empirically checked by means of a simulation study performed on a real compartment in a beech forest of Central Italy. Simulation results show that the use of auxiliary information generated in the first phase constitutes a very effective way of increasing the accuracy of volume estimation at the compartment level, with a moderate increase of fieldwork.     

林地面积空间抽样方法研究

论文采用空间简单随机抽样、空间分层抽样和三明治空间抽样模型,利用两种布样方式,随机和分层布样。通过对总体样本量和空间相关性的估算,进行样本分配和空间分层,结果表明：三明治空间抽样模型的抽样精度最高,其次是空间分层抽样、简单随机抽样;布样时采用分层抽样方式,将地理区域上分布的对象依照相似的属性值划分到不同的区域里,可以明显提高抽样调查效率及估计精度。     

用点抽样和乘积估计值法测定林分蓄积量

用点抽样和乘积估计值法测定林分蓄积量宋新民关键词点抽样，乘积估计值，次级样本用角规点抽样，林分每公顷蓄积量为：Ｍ＝ＦＺ－Ｒ（式中，Ｆ为角规断面积因子，Ｚ为样点计数木株数，－Ｒ为林木平均形高）。为了获得－Ｒ值，必须采用角规控制检尺和一元立木材积表。目前...     

森林分层随机抽样调查

本文首先介绍了分层随机抽样的基本概念和基本原理,其次又给出了关于总体平均数分层随机抽样的方法和分层随机抽样的公式,例如,绝对误差限、相对误差限、精度和抽样单元数目,特别是引进了最优配置和效率的概念。最后,本文又列举了如何利用分层随机抽样的实例,该例具有一定的参考价值。总之,分层随机抽样可以提高估计精度,减少抽样误差。     

Model-calibrated k-nearest neighbor estimators

A generalized difference, a model-calibrated (MC), and a pseudo-empirical likelihood (PEMLE) kNN estimator of a population mean and its sampling variance was assessed with simulated simple random (SRS) and one-stage cluster sampling (CLU) from three artificial and one actual multivariate populations. The number of nearest neighbors (k) for imputing values of a target variable varied from one to eight. The design-based MC estimator had the lowest bias, but bias varied among populations and target variables. In terms of root mean squared errors (RMSEs), the estimators had similar performance, yet RMSEs of MC and PEMLE were less variable. Results were uneven across populations and target variables. The value of k had little effect on RMSE suggesting an advantage of choosing a low value that retains most of the attribute variance in a map. Nominal confidence intervals computed from MC estimators of variance achieved overall the best coverage rate. Rankings of the estimators in SRS and CLU designs were similar. We recommend MC for practical kNN applications in forest inventories for pixel-level predictions and derived estimates.     

Adaptive cluster sampling for estimation of deforestation rates

National estimates of deforestation rates may be based on a survey. Precise estimation requires an efficient design. When deforestation rates are low (<1%) large sample sizes are required with traditional sampling designs to meet a precision target. This study explores the efficiency of adaptive cluster sampling (ACS) for this estimation problem. The efficiency is assessed by simulated ACS sampling from 18,200 × 200 km populations with 78–10,742 deforestation polygons (DFP) of different shape and size and average 10-year deforestation rates between 0.2% and 1.0%. Each population is composed of four million square 1 ha population units (PU) in a regular grid. Relative root mean square errors (RMSE) of ACS were, depending on sample size, 30–50% lower than comparable errors with simple random sampling (SRS) designs. ACS achieves this advantage by adaptively adding PUs to an initial SRS sample of size n. Realized ACS sample sizes were, on average, twice the nominal size (n). Three measures of ACS efficiency indicated that the costs of adaptively increasing the sample size are critical for the effectiveness of ACS. Population effects were manifest in all estimators. Estimates of the abundance, size, and shape of DFPs will allow a prediction of these effects. Populations dominated by a few large DFPs were clearly unsuited for ACS. The performance of ACS relative to that of SRS was similar across plot sizes of 1, 10, and 40 ha. The general conclusion of this study is that the lower RMSE of ACS remains attractive when the average cost of adaptively adding a PU to the initial sample is low relative to the average cost of sampling a PU at random.     

Estimators of Tree Species Richness: An Assessment for Central and Eastern Canada

Abstract

A total of 11 sample-based estimators of tree species richness (S) are evaluated in terms of accuracy and precision in a Monte Carlo simulated simple random sampling from 39,779 forest inventory plots with 7.8 million trees belonging to 85 species. The plots represent a 108 million hectare forested region in central and eastern Canada. Sample sizes varied from 50 to 800. A weighted index combining estimates of accuracy and precision identified Chao's first estimator (CHAO1) as overall best with an estimator based on the assumption of a gamma mixed Poisson distribution of species occurrence as a close runner-up. The observed sample species richness was almost always the most negatively biased estimate. A sample size of 400-700 conventional fixed area forest inventory plots are needed to produce results with bias <20%.     

思茅区森林生物量分层抽样调查设计

基于普洱市思茅区森林资源二类调查数据,以森林起源、地类、龄组、优势树种作为分层变量,在95%的可靠性下,设置95%,90%和85%的抽样设计精度分层抽样进行森林生物量估测,将抽样调查结果与系统抽样方法进行比较。结果表明:1)分层抽样具有样本数量少、抽样精度高的明显优势,在普洱市思茅区,按85%的设计精度,按优势树种的分层抽样比系统抽样的效率约提高了52%;2)采用单变量进行分层抽样,优势树种具有显著优势。按85%的设计精度,在样本数量为44个的前提下,其实际抽样精度达到87.82%。     

事后分层抽样技术在森林资源二类调查中的应用

回顾抽样技术在我国森林资源二类调查中的发展应用,介绍事后分层抽样技术的应用场合、抽样方法及理论基础.认为事后分层抽样理论完善、操作简便,在总体信息掌握不全的情况下应用,既可提高抽样精度,又可降低调查成本.以2005年砚山县森林资源二类调查中抽取的1293个角规控制检尺样地的样本数据为例,以事后分层抽样与系统抽样作比较,抽样精度明显提高.