The primary challenge in deriving accurate forest cover change information is representative of
the standard remote sensing problem: maximization of the signal-to-noise ratio. Inherent noise will affect
the change detection capabilities of a system or even create unreal change phenomena. Causes of such
unreal changes can be, for example, differences in atmospheric absorption and scattering due to
variations in water vapor and aerosol concentrations of the atmosphere at disparate moments in time,
temporal variations in the solar zenith and/or azimuth angles, and sensor calibration inconsistencies for
separate images (Hame, 1988). Preprocessing of satellite images prior to actual change detection is
essential and has as its unique goals the establishment of a more direct linkage between the data and
biophysical phenomena, the removal of data acquisition errors and image noise, and the masking of
contaminated and/or irrelevant scene fragments