Performance of a spatio-temporal error model for raster datasets under complex error patterns

Abstract

The CLC (Combined Location Classification) error model provides indices for overall data uncertainty in thematic spatio-temporal datasets. It accounts for the two major sources of error in such datasets, location error and classification error. The model assumes independence between error components, while recent studies revealed various degrees of correlation between error components in actual datasets. The goal of this study is to determine if the likely violation of model assumptions biases model predictions. A comprehensive algorithm was devised to simulate the entire process of error formation and propagation. Time series thematic maps were constructed, and modified maps were derived as realizations of underlying error patterns. Error rate and pattern (positive autocorrelation) were controlled for location error and for classification error. The magnitude of correlation between errors from different sources and correlation between error at different time steps was also controlled. A very good agreement between model predictions and simulation results was found in the absence of correlation in error between time steps and between error types, while the inclusion of such correlations was shown to affect model fit slightly. Given our current knowledge of spatio-temporal error patterns in real data, the CLC error model can be used reliably to assess the overall uncertainty in thematic change detection analyses.