Change detection for monitoring forest defoliation

Abstract

Monitoring of environmental conditions such as forest defoliation by insects over large areas is facilitated by automated approaches to change detection using remotely sensed data. This study evaluated four change detection techniques using multispectral, multitemporal SPOT data for identifying changes in hardwood forest defoliation caused by gipsy moth, Lymantria dispar L. The change detection techniques considered were principal components analysis, image differencing, spectral-temporal (layered temporal) change classification, and post-classification change differencing. The study area comprised approximately 148 square kilometres in Warren and Shenandoah Counties, Yirginia. Reference information of defoliation were aerial sketch maps developed by the U.S. Forest Service