Geometric correction and accuracy assessment of Landsat-7 ETM+ and Landsat-5 TM imagery used for vegetation cover monitoring in Queensland, Australia from 1988 to 2007

A range of programs exist globally that use satellite imagery to derive estimates of vegetation-cover for developing vegetation-management policy, monitoring policy compliance and making natural-resource assessments. Consequently, the satellite imagery must have a high degree of geometric accuracy. It is common for the accuracy assessment to be performed using the root mean square error (RMSE) only. However the RMSE is a non-spatial measure and more rigorous accuracy assessment methods are required. Currently there is a lack of spatially explicit accuracy assessment methods reported in the literature that have been demonstrated to work within operational monitoring programs. This paper reports on the method used by the Statewide Landcover and Trees Study (SLATS) to georegister and assess the registration accuracy of Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) imagery in Queensland, Australia. A geometric baseline with high accuracy (a statewide mean RMSE of 4.53 m) was derived by registering Landsat-7 ETM+ panchromatic imagery acquired in 2002 to a database of over 1600 control points, collected on the ground using a differential global positioning system. Landsat-5 TM and Landsat-7 ETM+ imagery for 12 selected years from 1988 to 2007 was registered to the baseline in an automated procedure that used linear geometric correction models. The reliability of the geometric correction for each image was determined using the RMSE, calculated using independent check points, as an indicator of model fit; by analysing the spatial trends in the model residuals; and through visual assessment of the corrected imagery. The mean RMSE of the statewide coverage of images for all years was less than 12.5 m (0.5 pixels). Less than 1 percent of images had non-linear spatial trends in the model residuals and some image misregistration after applying a linear correction-model; in those cases a quadratic model was deemed necessary for correction. Further research in the development of automated spatially explicit accuracy assessment methods is required.