Verbyla, D. L. and S. H. Boles. 2000.
Bias in land cover change estimates due to misregistration.
International Journal of Remote Sensing. 21:3553-3560.
Abstract. Land cover change may be overestimated
due to positional error in multi-temporal images. To assess the potential
magnitude of this bias, we introduced random positional error to identical
classified images and then subtracted them. False land cover change
ranged from less than 5% for a 5-class AVHRR classification, to more than
33% for a 20-class Landsat TM classification. The potential for false
change was higher with more classes. However, false change could
not be reliably estimated simply by number of classes, since false change
varied significantly by simulation trial when class size remained constant.
Registration model root mean squared (rms) error may underestimate the
actual image co-registration accuracy. In simulations with 5 to 50
ground control locations, the mean model rms error was always less than
the actual population rms error. The model rms error was especially
unreliable when small sample sizes were used to develop second order rectification
models. We introduce a bootstrap resampling method to estimate false
land cover change due to positional error. Although the bootstrap
estimates were unbiased, the precision of the estimates may be too low
to be of practical value in some land cover change applications.
Email: D.Verbyla@uaf.edu
Last updated: August 2001