A discriminant space-based framework for scalable area-class mapping

Earlier research has introduced the concept of discriminant space, which is spanned by the covariates underlying area-class occurrences, for increased consistency, interpretability, and replicability in area-class mapping and uncertainty characterization. While simple univariate cases with b=1 (b being the dimension of the discriminant space) were investigated previously using simulated data, real world applications are usually multivariate with b>1, thus giving rises to the need for developing discriminant models in spaces of higher dimensionality for increased applicability. This paper describes combined use of generalized linear modeling and kriging for area-class mapping, with the former deterministically predicting mean class responses while the latter making use of spatially correlated residuals in the predictive class models. Scalability in area-class mapping is facilitated by flexible implementation of scale-dependent prediction of mean class responses and point- vs. area-support kriging of the residuals. This is followed by an empirical study concerning land cover mapping in central western Montana, which confirmed the effectiveness of the proposed strategy combining regression and kriging for scale-dependent mapping of area classes.