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Efficient Sampling Techniques for Timber Sale Surveys and Inventory Updates

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Abstract:

Three different regression estimators, predicting tree or plot volume from tree diameter squared times total height or previous plot volume 5 or 10 years earlier for specific populations, were studied with respect to minimum relative bias, minimum variance, percentage of confidence intervals containing true volume, and consistency in estimating total volume using six sampling rules. The sampling rules were: restricted random sampling (RSRS), sampling from the range of x (psR), pps sampling (standard against which all others are compared), stratified pps sampling (spps), pscx, and spscx procedures. The latter two sampling rules yielded the most efficient volume estimators in all three populations. Stratified pps sampling is less reliable than the pscx procedures but better than pps sampling. Restricted simple random sampling and sampling from the range of x are less reliable than pps sampling. Forest Sci. 31:857-866.

Keywords: Model-based sampling; purposive sampling; simulation; weighted linear models

Document Type: Journal Article

Affiliations: Research Forester, Forest Inventory and Analysis Project, Southern Forest Experiment Station, Starkville, MS 39759

Publication date: December 1, 1985

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
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