Remote sensing of forest change using artificial neural networks

A prolonged drought in the Lake Tahoe Basin in California has resulted in extensive conifer mortality. This phenomenon can be analyzed using (multitemporal) remote sensing data. Prior research in the same region used more traditional methods of change detection. The present paper introduces a third approach to change detection in remote sensing based on artificial neural networks. The neural network architecture used is a multilayer feedforward network. The results of the study indicate that the artificial neural network (ANN) estimates conifer mortality more accurately than the other approaches. Further, an analysis of its architecture reveals that it uses identifiable scene characteristics-the same as those used by a Gramm-Schmidt transformation. ANN models offer a viable alternative for change detection in remote sensing.