MODELING SENSITIVITY TO ACCURACY IN CLASSIFIED IMAGERY - A STUDY OF AREAL INTERPOLATION BY DASYMETRIC MAPPING

Areal interpolation is the process by which data collected from one se t of zonal units can be estimated for another zonal division of the sa me space that shares few or no boundaries with the first. In previous research, we outlined the use of dasymetric mapping for areal interpol ation and showed it to be the most accurate method tested. There we us ed control information derived from classified satellite imagery to pa rameterize the dasymetric method, but because such data are rife with errors, here we extend the work to examine the sensitivity of the popu lation estimates to error in the classified imagery. Results show the population estimates by dasymetric mapping to be largely insensitive t o the errors of classification in the Landsat image when compared with the other methods tested. The dasymetric method deteriorates to the a ccuracy of the next worst estimate only when 40% error occurs in the c lassified image, a level of error that may easily be bettered within m ost remote sensing projects.