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Continuous Forest Inventory Using a Linear Filter

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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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Keywords: Bayesian estimation; Kalman filter; sampling with partial replacement; stoachistic optimal control

Document Type: Journal Article

Affiliations: Assistant Professor of Agricultural Economics at the University of California at Davis

Publication date: 1979-12-01

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.

    2016 Impact Factor: 1.782 (Rank 17/64 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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