Misclassification error propagation in land cover change categorization

It is important to describe misclassification errors in land cover maps and to quantify their propagation through geo-processing to resultant information products, such as land cover change maps. Geostatistical simulation is widely used in error modeling, as it can generate equal-probable realizations of the fields being considered, which can be summarized to facilitate error propagation analysis. To fix noninvariance in indicator simulation, discriminant space-based methods were proposed to enhance consistency in area-class mapping and replicability in uncertainty modeling, as the former is achieved by imposing means while the latter is ensured by projecting spatio-temporal correlated residuals in discriminant space to geographic space through a mapping process. This paper explores discriminant models for error propagation in land cover change detection, followed by experiments based on bi-temporal remote sensing images. It was found that misclassification error propagation is effectively characterized with discriminant covariate-based stochastic simulation, where spatio-temporal interdependence is taken into account.