Automated Forest Area Estimation Using Iterative Guided Spectral Class Rejection

Abstract

USDA Forest Service Forest Inventory and Analysis (FIA) forest area estimates were derived from 4 Landsat ETMimages in Virginia and Minnesota classified using an automated hybrid classifier known as Iterative Guided Spectral Class Rejection (IGSCR). Training data were collected using region- growing initiated at random points within each image. The classified images were spatially post-processed using five different techniques. Image accuracy was assessed using the center land-use of all available FIA plots and subsets contain- ing plots with 50, 75 and 100 percent homogeneity. Overall accuracy (81.9 to 95.4 percent) increased with homogeneity of validation plots and decreased with frag- mentation (estimated by percent edge; r 2 = 0.932). Filter- ing effects were not consistently significant at the 95 per- cent level; however, the 3 � 3 majority filter significantly improved the accuracy of the most fragmented image. The now-automated IGSCR is a suitable candidate for operational forest area estimation, with strong potential for use in other application areas.