Discriminant models for scaling area-class maps

Abstract

Earlier research has discussed the concept of discriminant space and its applications in area-class mapping and uncertainty characterization. Both simple univariate cases with b=1 (b being the dimension of the discriminant space) and multivariate cases with b>1 were analyzed with simulated and real data sets, respectively. This paper describes combined use of generalized linear models and kriging for scalable area-class mapping, with the former deterministically predicting mean class responses and the latter making use of spatially correlated residuals in the predictive class models. Scalability in area-class mapping is facilitated by scale-dependent prediction of mean class responses and kriging of the residuals over specific gridding cells. The methodology was implemented with topographic data and Landsat TM imagery concerning land cover mapping in central western Montana, which confirmed the effectiveness of the proposed strategy combining regression and kriging for scalable mapping of area classes.