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 regio n used more traditional methods of change detection [8], [30]3. This p aper 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 ind icate that the artificial neural network (ANN) estimates conifer morta lity more accurately than the other approaches. Further, an analysis o f its architecture reveals that it uses identifiable scene characteris tics--the same as those used by a Gramm-Schmidt transformation. ANN mo dels offer a viable alternative for change detection in remote sensing .