A Neural Network Growth and Yield Model for Nova Scotia Forests

Abstract

Forest growth models are important to the forestry community because they provide means for predicting future yields and exploring different forest management practices. The purpose of this thesis is to develop an individual tree forest growth model applicable for the province of Nova Scotia. The Acadian forest of Nova Scotia is a prime example a mixed species forest which is best modelled with individual tree models. Individual tree models also permit modelling variable-density management regimes, which are important as the Province investigates new silviculture options. Rather than use the conventional regression techniques, our individual tree growth and yield model was developed using neural networks. The growth and yield model was comprised of three different neural networks: a network for each survivability, diameter increment and height increment. In general, the neural network modelling approach fit the provincial data reasonably well. In order to have a model applicable to each species in the Province, species was included as a model input; the models were able to distinguish between species and to perform nearly as well as species-specific models. It was also found that including site and stocking level indicators as model inputs improved the model. Furthermore, it was found that the GIS-based site quality index developed at UNB could be used as a site indicator rather than land capability. Finally, the trained neural networks were used to create a growth and yield model which would be limited to shorter prediction periods and a larger scale.