Classifying dry sclerophyll forest from augmented satellite data : Comparing neural network, decision tree and maximum likelihood

Abstract

Detailed maps derived from geographical data are becoming increasingly desirable for use in forest management. Many types of data are available for use in generating maps, for example, soil and vegetation maps.

We look at a method for giving high level classifications that can be used as additional data for the generation of more detailed maps, and compare the results with other currently used techniques. We use multiple techniques to increase the reliability and accuracy of predictions.

We describe a simple method of adjusting the balance of false positive and false negative classifications that are produced by the neural network. This allows better integration with non-neural network techniques.