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Aggregation Procedures in Forest Management Planning Using Cluster Analysis

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Abstract:

Typical linear programming models used in forest planning can be very large. It is often of interest to analyze more compact, less detailed versions. One form of reducing the size of the problem is through an aggregation process. One way in which this has been done is through a column aggregation process, where sets of similar columns are replaced by one representative. A second alternative is to aggregate the original data, in which case the stands and management alternatives are grouped before building a model. Typical approaches for the aggregation processes have been analytical. We present an alternative approach for aggregation based on cluster analysis. Computational results for both types of aggregation show that using cluster analysis can be advantageous. For. Sci. 43(2):274-285.

Keywords: Linear programming; hierarchical planning; statistical analysis

Document Type: Journal Article

Affiliations: Associate Professor, Department of Probability and Statistics, Faculty of Mathematics, Catholic University, P.O. Box 6177-Santiago 22-Chile (56-2-6864507;, Fax: 56-2-5525916

Publication date: 1997-05-01

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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 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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