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Predicting Forest Attributes in Southeast Alaska Using Artificial Neural Networks

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Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy. FOR. SCI. 50(2):259–276.
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Keywords: AI; GIS; Interpolation; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; land use; natural resource management; natural resources; temperate rainforest

Document Type: Regular Article

Affiliations: 1: School of Geography University of Leeds Leeds United Kingdom LS2 9JT Phone: 44 (113) 343 3309;, Fax: 44 (113) 343 3308, Email: [email protected] 2: School of Geography University of Leeds Leeds United Kingdom LS2 9JT 3: Centre for Biodiversity and Conservation, School of Biology University of Leeds Leeds United Kingdom LS2 9JT 4: Pacific Northwest Research, Forest Inventory and Analysis, Forestry Sciences Laboratory USDA Forest Service Anchorage AK 99503

Publication date: 01 April 2004

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