In this study satellite data from five different multispectral sensors were used in a change detection study of vegetation disturbance on an Irish active raised bog. Radiometric normalisation was performed using Temporally Invariant Clusters (TIC) and cross calibration applied using linear regression of radiometrically stable ground-based targets. Erdas Imagine’s Spatial Modeller was used to create a change detection model using pixel-to-pixel based subtraction with a Standard Deviation (SD) threshold. The effectiveness of the cross calibration process was shown with the aid of Kolmogorov Smirnov sample tests which showed a reduced D value between master and slave cumulative distribution curves after cross calibration. The spatial accuracy of various SD threshold levels was assessed, with 1.5 SD producing 0.19% error when compared to actual ground truth boundary data of change. An error matrix of change/ no change verified 1.5 SD as the optimum threshold for change detection, with user, producer, overall and kappa values all above 95%. Vegetation disturbance in the study was predominantly attributed to turf cutting on the boundaries of the bog. However in May 2008 a large burn event occurred on the northeastern side of the bog which removed all surface vegetation, equating to an area of 36ha (or 7.85% of total area).