Document Type

Article

Publication Date

8-9-2017

Abstract

In this study, three methods of estimating parameters of the logistic regression model for predicting tree survival probabilities were evaluated: maximum likelihood; optimized for stand survival (stand optimization); and optimized for both stand and tree survival (combined likelihood). Four methods of disaggregation were considered: no disaggregation; disaggregation based on predictions from a stand-level model; disaggregation based on predictions from a composite estimator; and disaggregation based on observed stand survival. For stand survival prediction, surprisingly, the tree survival model with parameters optimized for stand survival scored better than the stand-level survival model, based on all three evaluation statistics. This method performed even better when it was combined with the stand-level survival model to form a composite estimator. For tree survival prediction without disaggregation, the stand optimization method was edged out by the maximum likelihood method. The disaggregation method slightly degraded the performance of the unadjusted model, based on all evaluation statistics. When adjusted using stand survival values, either observed or predicted, the stand optimization method was clearly the best among the three parameter estimation methods. These results showed that the stand optimization method should be used to estimate parameters of the tree survival model. The decision of using disaggregation should depend on whether or not the stand survival prediction passes a certain minimum threshold. Disaggregation did not improve prediction of tree survival for this data set, maybe because the stand-level prediction did not reach that reliability threshold.

Publication Source (Journal or Book title)

Forest Science

First Page

356

Last Page

361

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