Predicting forest attributes In southeast Alaska using artificial neural networks

SA Corne*, SJ Carver, WE Kunin, JJ Lennon, WWS van Hees

*Corresponding author for this work

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)259-276
Number of pages18
JournalForest science
Volume50
Issue number2
Publication statusPublished - Apr 2004

Keywords

  • interpolation
  • land use
  • Al
  • GIS
  • temperate rainforest
  • REMOTE-SENSING DATA
  • PATTERN-RECOGNITION
  • TROPICAL FOREST
  • CLASSIFICATION
  • MODEL
  • BEHAVIOR
  • DENSITY
  • ECOLOGY
  • COVER
  • AREAS

Cite this

Corne, SA., Carver, SJ., Kunin, WE., Lennon, JJ., & van Hees, WWS. (2004). Predicting forest attributes In southeast Alaska using artificial neural networks. Forest science, 50(2), 259-276.