Evaluating Alternative Temporal Survey Designs for Monitoring Wetland Area and Detecting Changes Over Time in California
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Abstract Evaluation of wetland extent and changes in extent is a foundation of many wetland monitoring and assessment programs. Probabilistic sampling and mapping provides a cost‐effective alternative to comprehensive mapping for large geographic areas. One unresolved challenge for probabilistic or design‐based approaches is how best to monitor both status (e.g., extent at a single point in time) and trends (e.g., changes in extent over time) within a single monitoring program. Existing wetland status and trends (S&T) monitoring programs employ fixed sampling locations; however, theoretical evaluation and limited implementation in other landscape monitoring areas suggest that alternative designs could increase statistical efficiency and overall accuracy. In particular, designs that employ both fixed and nonfixed sampling locations (alternately termed permanent and temporary samples), termed sampling with partial replacement ( SPR ), are considered to efficiently and effectively balance monitoring current status with detection of trends. This study utilized simulated sampling to assess the performance of fixed sampling locations, SPR , and strictly nonfixed designs for monitoring wetland S&T over time. Modeled changes in wetland density over time were used as inputs for sampling simulations. In contrast to previous evaluations of SPR , the results of this study support the use of a fixed sampling design and show that SPR may underestimate both S&T.This article gives an overview of the process and logic of sampling. The article begins by describing the basic building blocks of sampling theory. The most common sampling designs that can be used in social science research are discussed and is divided into two broad categories :
Probability sampling which include simple random sampling, systematic sampling, stratified sampling, cluster sampling, multi-stage cluster sampling and probability proportionate to size (PPA) sampling, and
Non-probability sampling which include accidental sampling, purposive sampling, quota sampling and referral sampling which can be divided into network and snowball sampling.
The article also assesses various factors that determine the choice of a sample design, which include the stage of the research process, availability of resources and the data collection methods applied. It concludes with a discussion on selecting the right sample size.
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The paper reviews the developmental application of sampling skills to the inventory of forest resources' planning and design in our country,and introduces application occasion,sampling methods and theoretical basis of afterwards stratified sampling technique.It concerns that the theory of afterwards stratified sampling technique is perfect and operation is simple.Its application can both improve sampling precision and lower expense for the inventory under the circumstance of not enough of general information.Taking the sampling data selected from the inventory of forest resources' planning and design in Yanshan county in 2005 as an example,the comparison between afterwards stratified sampling and systematic sampling reveals that sampling precision is obviously improved.
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This chapter discusses the application of sampling theory and techniques to forest resource evaluation applicable to both timber and nontimber parameters. A sampling design is determined by the kind of sampling units used, the number of sampling units employed, and the manner of selecting and distributing these sample units over the forest area, as well as the procedures for making measurements and analyzing the results. The chapter concentrates on sampling designs using fixed-area plots, although the basic sampling designs are applicable to sampling units of any type. Simple random sampling (SRS) is the fundamental selection method. In systematic sampling (SYS), sampling units are spaced at fixed intervals throughout the population. Selective or opportunistic sampling is an example of nonrandom sampling used in forest inventory work. Clusters are frequently used in forest inventories of large areas, such as regional or national inventories, especially in remote areas with difficult access or long distances between plots.
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The study of fishery community ecology depends on quality and quantity of data collected from well-designed sampling programs. The optimal sampling design must be cost-efficient, and the sampling results have been recognized as a significant factor affecting resources management. In this paper, the performances of stationary sampling, simple random sampling and stratified random sampling in estimating fish community were compared based on computer simulation by design effect (De), relative error (REE) and relative bias (RB). The results showed that, De of stationary sampling (average De was 3.37) was worse than simple random sampling and stratified random sampling (average De was 0.961). Stratified random sampling performed best among the three designs in terms of De, REE and RB. With the sample size increased, the design effect of stratified random sampling decreased but the precision and accuracy increased.
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Sampling is a matter of routine, and the effects of the outcomes can be felt in our day-to-day lives. This chapter discusses four different sample designs: simple random sampling, stratified random sampling, systematic random sampling, and cluster random sampling from a finite population. The primary objective of sampling is to make inferences about population parameters using information contained in a sample taken from that population. Simple random sampling is the most basic form of sampling design. There are two techniques to take a simple random sample from a finite population: sampling with replacement and sampling without replacement. In stratified random sampling, the population is divided into different non-overlapping groups called strata. Systematic random sampling design may be the easiest method of selecting a random sample. Cluster sampling is not only cost-effective but also a time-saver, since collecting data from adjoining units is cheaper, easier, and quicker than if the sampling units are spread out.
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[Selection of sentinel sites for death surveillance, using cluster or unequal probability sampling].
To compare the sampling errors from cluster or unequal probability sampling designs and to adopt the unequal probability sampling method to be used for death surveillance. Taking 107 areas from the county level in Shaanxi province as the sampling frame, a set of samples are drawn by equal probability cluster sampling and unequal probability designs methodologies. Sampling error and effect of each design are estimated according to their complex sample plans. Both the sampling errors depend on the sampling plan and the errors of equal probability in stratified cluster sampling appears to be less than simple cluster sampling. The design effects of unequal probability stratified cluster sampling, such as piPS design, are slightly lower than those of equal probability stratified cluster sampling, but the unequal probability stratified cluster sampling can cover a wider scope of monitoring population.Results from the analysis of sampling data can not be conducted without consideration of the sampling plan when the sampling frame is finite and a given sampling plan and parameters, such as sampling proportion and population weights, are assigned in advance. Unequal probability cluster sampling designs seems to be more appropriate in selecting the national death surveillance sites since more available monitoring data can be obtained and having more weight in estimating the mortality for the whole province or the municipality to be selected.
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