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An Improved Estimator for Merchantable Basal Area Growth Based on Point Samples

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The computation scheme for merchantable growth based on remeasured point samples (prism plots) is presented. Emphasis is placed on defining the population attributes which are to be estimated. Two different unbiased estimators of gross merchantable growth including ingrowth are considered: the compatible estimator, and the noncompatible. The former is based on ingrowth and ongrowth sample trees, while the latter is based on ingrowth and survivor sample trees. An estimator is proposed which is a weighted sum of the two currently used estimators. The selection of the most suitable estimator is considered for both random sampling, and cluster or satellite sampling. The weighted estimator is unbiased and in most situations can be expected to have a lower variance than either of the estimators it replaces. In a numerical example from the Southern Forest Experimental Station Renewable Resources Evaluation survey, the weighted estimator is found to be effective. Applied to a seven-point cluster, it has approximately the same variance as would otherwise be achieved using a ten-point cluster. Forest Sci. 30:813-821.

Keywords: Random sampling; cluster sampling

Document Type: Journal Article

Affiliations: Research Forester, Southern Forest Experiment Station, P.O. Box 906, Starkville, MS 39759

Publication date: 1984-09-01

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.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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