Adaptive cluster sampling for estimation of deforestation rates |
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Authors: | Email author" target="_blank">Steen?MagnussenEmail author Werner?Kurz Don?G?Leckie Dennis?Paradine |
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Institution: | (1) Natural Resources Canada, Canadian Forest Service, Pacific Forestry Center, 506 West Burnside Road, Victoria, Canada, V8Z 1M5 |
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Abstract: | 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. |
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Keywords: | Design efficiency Network sampling Expected sample size Predicted efficiency |
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