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A Separable Goal Programming Approach to Optimizing Multivariate Sampling Designs for Forest Inventory

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Describes the application of a separable goal programming approach to stratified random sampling involving multiple objectives. Other attempts at solving this problem are also reviewed. The method is applied to a forest inventory problem in New Mexico involving six objectives and fourteen strata. Eight sampling allocations are presented to illustrate the sensitivity to alternate preference functions. Intercorrelations among goal criteria limit the effects of alternative preferences on resulting sampling allocations. All sampling allocations are guaranteed to be nondominated--something that goal programming does not (in general) provide. Forest Sci. 27:147-162.

Keywords: Stratified random sampling; multiple criteria

Document Type: Journal Article

Affiliations: Associate Professor, College of Forest Resources, University of Washington, Seattle, WA 98195

Publication date: 1981-03-01

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

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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