Modeling individual tree survialThis article is part of a larger document. View the larger document here.
Information provided by growth and yield models is the basis for forest managers to make decisions on how to manage their forests. Among different types of growth models, whole-stand models offer predictions at stand level, whereas individual-tree models give detailed information at tree level. The well-known logistic regression is commonly used to predict tree survival probability. In addition to the maximum likelihood approach, a new approach called CDF regression was introduced here to estimate parameters of the tree survival equation.
Each of the two above approaches was evaluated as follows: (1) unadjusted, (2) disaggregated from the wholestand model, and (3) disaggregated from the combined estimator. Results from this study showed that the tree survival model, when adjusted from the combined estimator, produced the best-ranked two alternatives. The new method, CDF Regression, coupled with the combined estimator, was better than the Maximum Likelihood method in estimating parameters of the logistic regression equation.