Using Logistic Regression to Model Tree Selection Preferences for Harvesting in Forests in Conversion

Wien

2001

S. 203-216

8 Abb., 5 Tab., 17 Lit. Ang.

Artikel aus einer ZeitschriftUnselbständiges Werk

200151289

A harvesting model for forests in conversion was developed to model tree selection preferences. Data came from a continuous forest inventory of a forest management area, where target diameter harvesting is the preferred management system. The Logit-function was used to model tree selection preferences using maximum likelihood methods for parameter estimation. Tree size, tree damage, stem quality and tree species were found to have a strong impact on tree selection preferences. Two different strategies in selecting trees for removal could be detected depending on whether the stand was older or younger than 100 years. Examination of observed versus predicted removed basal area, for relative DBH-classes, reveals that the model is well behaved and matches the observations quite well. Finally, the harvesting model passed a validation test using an independent data set.