Using Neural Networks to Predict Individual Tree Mortality
نویسنده
چکیده
Within forest growth modeling it is customary to employ LOGIT models to predict individual tree mortality. In this paper we use Learning Vector Quantization and the self-organizing map as diierent formalisms to predict individual tree mortality. The data set for this study came from permanent sample plots in uneven-aged Norway spruce (Picea abies L. Karst) stands in Austria. After parameterizing the LOGIT model and training the two diierent network types we evaluate the diierences in the resulting mortality predictions using an independent test data set. The results indicate that the LVQ performs slightly better than the conventional LOGIT approach as well as the self organizing map.
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