The high spatial resolution of digitized aerial photographs may offer an accurate and effective means of mapping, inventorying, and monitoring forests. Due to the presence of bi-directional reflectance, however, the pixel values are affected by their location within the photo. Two similar sample plots or vegetation types in different parts of the photo may thus have quite dissimilar pixel values and texture features. It is consequently necessary to correct, or calibrate, pixel values when they are used in numerical interpretation. The effect of location of a window of pixels on various colour-infrared (CIR) aerial photographs corresponding to the field sample plots was analysed. Two calibration methods, regression calibration and ratioing, were derived and tested. Linear regression calibration to the principal-point level of the photos was shown to be the most effective, in which the mean pixel value of the window was modelled as a function of solar and sensor direction at the time of exposure. The results indicated that the effect of location on the window mean values was considerable. Calibration also increased the spectral separability of forest stand-classes.