Compatible Estimation of Components of Forest Growth from Remeasured Point Samples with Restricted Generalized Least Squares
Abstract:Certain of the estimators which have been proposed for estimation of components of forest growth from remeasured horizontal point samples are not restricted to ensure that the initial volume estimate plus the growth estimate is equal to the final volume estimate. By viewing the initial volume, the final volume, and the components of growth as parameters in a linear model within the framework of restricted generalized least squares, estimates can be obtained which satisfy logical restrictions relating to forest growth. The model should be fitted to data obtained from a relatively large sample of points. Estimates of growth components are obtained on each point in the usual way. However, rather than simply averaging these estimates over all points, restricted generalized least squares can be used to estimate growth components in the linear model described above. Since the estimation technique can handle missing or extra points at the two measurement times, the technique can be applied to data obtained by sampling with partial replacement. For. Sci. 41(3):611-628.
Document Type: Journal Article
Affiliations: Department of Forestry, Oklahoma State University, Stillwater, OK 74078
Publication date: August 1, 1995
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- 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
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