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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/13886 |
| Date | 09 June 2011 |
| Creators | Higgins, Jenna |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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