A per-segment approach to improving aspen mapping from remote sensing imagery and its implications at different scales

A per-segment classification system was developed to map aspen (Populus tremuloides) stands on Winter Ridge in central Oregon from remote sensing imagery. A 1-meter color infrared (CIR) image was segmented based on its hue and saturation values to generate aspen "candidates", which were then classified to show aspen coverage according to the mean values of spectral reflectance and multi-resolution texture within the segments. For a three-category mapping, an 88 percent overall accuracy with a K-hat statistic of 0.82 was achieved, while for a two-category mapping, a 90 percent overall accuracy with a K-hat statistic of 0.78 was obtained.; In order to compare these results to traditional per-pixel classifications, an unsupervised classification procedure based on the ISODATA algorithm was applied to both pixel-based and segment-based seven-layer images. While differences among various per-pixel classifications were found to be insignificant, the results from the per-segment system were consistently more than 20 percent better than those from per-pixel classifications.; Both the per-segment and per-pixel classifications were applied at various spatial resolutions in order to study the effect of spatial resolution on the relative performance of the two methods. The per-segment classifier outperformed the per-pixel classifier at the 1--4-m resolution, performed equally well at the 8--16-m resolution and showed no ability to classify accurately at the 32-m resolution due to the segmentation process used. Overall, the per-segment method was found to be more scale-sensitive than the per-pixel method and required some tuning to the segmentation algorithm at lower resolutions. These results illustrate the advantages of per-segment methods at high spatial resolutions but also suggest that segmentation algorithms should be applied carefully at different spatial resolutions.