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Local Modeling of Tree Growth by Geographically Weighted Regression

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The spatial heterogeneity of multivariate relationships between tree growth and diameter is explored using geographically weighted regression (GWR). GWR attempts to capture spatial variation by calibrating a multiple regression model fitted at each tree in a sample plot, weighting all neighboring trees by a function of distance from the subject tree. GWR produces a set of parameter estimates and model statistics (e.g., model R2) for each tree in the sample plot. It is evident that the GWR model not only predicts individual tree growth better than the traditional ordinary least-squares model, but also provides useful information on the nature of the growth variation caused by neighboring competitors and surrounding environmental factors. The parameter estimates and model statistics of the GWR model can be mapped using visualization tools, such as geographic information systems (GIS), to illustrate local spatial variation in the regression relationship under study. Consequently, the influence of microsite variation, competition status, growth potential, and the impacts of management activities on trees can be evaluated, tested, modeled, and readily visualized. GWR is a useful tool that provides much more information on spatial relationships to assist in model development and further our understanding of spatial processes. FOR. SCI. 50(2):225–244.
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Keywords: Spatial autocorrelation and heterogeneity; environmental management; forest; forest growth and yield modeling; forest management; forest resources; forestry; forestry research; forestry science; geographic information systems (GIS); natural resource management; natural resources; tree competition

Document Type: Regular Article

Affiliations: 1: Associate Professor Faculty of Forest and Natural Resources Management State University of New York, College of Environmental Science and Forestry One Forestry Drive Syracuse NY 13210 Phone: (315) 470-6558;, Fax: (315) 470-6535, Email: [email protected] 2: Research Assistant Faculty of Forest and Natural Resources Management State University of New York, College of Environmental Science and Forestry One Forestry Drive Syracuse NY 13210 Phone: (315) 426-0290 haijin_, Email: [email protected]

Publication date: 2004-04-01

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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