A Generalized Framework for Projecting Forest Yield and Stand Structure Using Diameter Distributions
Abstract:The classical diameter distribution approach for projecting yields and stand structure in even-aged stands is generalized to include stands of indeterminate age. Attention is focused upon the concept of parameter prediction models (PPM) with empirical illustrations for both even-aged and uneven-aged applications. Parameter prediction models are those which directly predict the future values of the parameters of a probability density function (pdf) characterizing a diameter distribution. Stand-average attributes such as volume and basal area are then estimated using the diameter distribution. A parameter recovery model (PRM) is also proposed and numerically illustrated. Parameter recovery models are those in which the stand-average attributes are directly predicted and then used to obtain estimates of the underlying diameter distribution. Examined together with the two models suggest that a mathematical compatability exists between two distinct whole stand/distance independent yield forecasting techniques: diameter distribution models and stand-average models. Forest Sci. 29:85-95.
Document Type: Journal Article
Affiliations: Professor of Forestry Biometry, Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907
Publication date: 1983-03-01
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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.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
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