A new design for sampling with adaptive sample plots |
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Authors: | Haijun Yang Christoph Kleinn Lutz Fehrmann Shouzheng Tang Steen Magnussen |
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Institution: | (1) Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;(2) Department of Mathematical Sciences, Isfahan University of Technology, 84156-83111 Isfahan, Iran;(3) US Geological Survey, Leetown Science Centre, 11649 Leetown Rd, Kearneysville, WV 25430, USA |
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Abstract: | 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. |
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