Using quadtree segmentation to support error modelling in categorical raster data

This paper explores the use of quadtree segmentation of a land-cover map to improve error modelling by (1) accounting for variation in classification accuracy among differently sized homogeneous map regions and (2) improving the statistical properties of map realizations generated by sequential indicator simulation (SIS). The latter was accomplished by locating the first simulation nodes—which affect many subsequent local simulations—within the largest quadtree leaves. These represent the largest homogeneous and hypothetically most accurately classified areas on the map. A case study showed that, indeed, the overall accuracy of the land-cover map increased with the quadtree leaf size, ranging from 67% for single cells in heterogeneous areas to 98% for homogeneous blocks of 256 cells. A map of the prior probability of each land-cover class was prepared on the basis of quadtree level-specific confusion matrices. Next, two unconditional SIS algorithms were used to generate sets of 50 realizations of the map, thereby accounting for spatial continuity of the residuals between indicator-transformed reference data and the priors. The proposed quadtree-guided SIS outperformed the more common multiple-steps method as judged by the reproduction of target proportions of map categories, class-specific accuracy levels and variogram reproduction