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Using the Non-Parametric Classifier CART to Model Forest Tree Mortality

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A binary classification tree (CART) was used to predict forest tree mortality for two conifer species. CART models were fitted using binary recursive splitting of the data set into increasingly homogeneous subsets. Models were compared in terms of improvement of prediction accuracy, representativeness in average size of the predicted mortality trees, and interpretability of the results. For shade intolerant ponderosa pine, crown ratio, diameter increment prediction, or variables indicating the relative position of a tree in a stand were used for splitting. For shade tolerant white fir, height increment prediction and stand density were selected for splitting. The prediction accuracies for mortality trees of the best CART models were between 28-36% for ponderosa pine and between 11-17% for white fir. CART was also compared with logistic regression using a stochastic and a deterministic assignment of mortality. Efficiencies similar to those achieved with CART were reached with deterministic logistic models using thresholds probabilities. However, CART and the logistic model tended to utilize different predictor variables, especially for white fir. CART uncovered additional factors for white fir important for predicting mortality not identified by the logistic regression. For. Sci. 44(4):507-516.
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Keywords: Tree mortality; classification trees; logistic regression

Document Type: Journal Article

Publication date: 1998-11-01

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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