Abstract
Many attempts to observe changes in terrestrial systems over time would be significantly enhanced
if it were possible to improve the accuracy of classifications of low-resolution historic
satellite data. In an effort to examine improving the accuracy of historic satellite image classification
by combining satellite and air photo data, two experiments were undertaken in which
low-resolution multispectral data and high-resolution panchromatic data were combined and then
classified using the ECHO spectral-spatial image classification algorithm and the Maximum Likelihood
technique. The multispectral data consisted of 6 multispectral channels (30-meter pixel
resolution) from Landsat 7. These data were augmented with panchromatic data (15m pixel resolution)
from Landsat 7 in the first experiment, and with a mosaic of digital aerial photography (1m
pixel resolution) in the second. The addition of the Landsat 7 panchromatic data provided a significant
improvement in the accuracy of classifications made using the ECHO algorithm. Although
the inclusion of aerial photography provided an improvement in accuracy, this improvement was
only statistically significant at a 40-60% level. These results suggest that once error levels associated
with combining aerial photography and multispectral satellite data are reduced, this approach
has the potential to significantly enhance the precision and accuracy of classifications made using
historic remotely sensed data, as a way to extend the time range of efforts to track temporal
changes in terrestrial systems.