Continuous Forest Inventory Using a Linear Filter
Timber management may be conceptualized as a stochastic optimal control problem because of uncertainty about forest dynamics and the sequential nature of the decisionmaking process. Given this point of view, it follows that decisions will be based on the conditional distributions of unknown model parameters where the distributions are derived recursively. By making various approximations, the management actions become a function of the conditional mean of timber inventories. This conditional mean and the conditional covariance of the inventories are generated by a Kalman filter. The conditional mean can alternatively be interpreted as an estimate of the unknown timber inventories. This estimate thus has the virtue of being optimal with respect to the overall timber management problem. The partial replacement estimator of Ware and Cunia is shown to be a special case of the Kalman estimator. The variance of the Ware and Cunia estimator is always greater than or equal to the variance of the corresponding Kalman estimator. Numerical results show that the variance of the Kalman estimator is almost always less than the variance of the Ware and Cunia estimator. In some cases when using the Kalman filter simple random sampling is shown to yield estimates with lower variance than partial replacement sampling. Forest Sci. 25:675-689.
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Document Type: Journal Article
Affiliations: Assistant Professor of Agricultural Economics at the University of California at Davis
Publication date: 1979-12-01
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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.
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