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Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch : Volume 124, Issue 2, June 2005

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Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch : Volume 123

Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch : Volume 122

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Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch : Volume 116

Adaptive cluster sampling for estimation of deforestation rates

Content Provider	Springer Nature Link
Author	Magnussen, Steen Kurz, Werner Leckie, Don G. Paradine, Dennis
Copyright Year	2005
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.
Starting Page	207
Ending Page	220
Page Count	14
File Format	PDF
ISSN	16124669
Journal	Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch
Volume Number	124
Issue Number	3
e-ISSN	16124677
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-08-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Design efficiency Network sampling Expected sample size Predicted efficiency Plant Ecology Plant Sciences Forestry
Content Type	Text
Resource Type	Article
Subject	Plant Science Forestry

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