SAMPLING AND MAPPING OF HETEROGENEOUS SURFACES - MULTIRESOLUTION TILING ADJUSTED TO SPATIAL VARIABILITY

Mapping by sampling and prediction of local and regional values of two -dimensional surfaces is a frequent, complex task in geographical info rmation systems. This article describes a method for the approximation of two-dimensional surfaces by optimizing sample size, arrangement an d prediction accuracy simultaneously. First, a grid of an ancillary da ta set is approximated by a quadtree to determine a predefined number of homogeneous mapping units. This approximation is optimal in the sen se of minimizing Kullback-divergence between the quadtree and the grid of ancillary data. Then, samples are taken from each mapping unit. Th e performance of this sampling has been tested against other sampling strategies (regular and random) and found to be superior in reconstruc ting the grid using three interpolation techniques (inverse squared Eu clidean distance, kriging, and Thiessen-polygonization). Finally, the discrepancy between the ancillary grid and the surface to be mapped is modelled by different levels and spatial structures of noise. Concept ually this method is advantageous in cases when sampling strata cannot be well defined a priori and the spatial structure of the phenomenon to be mapped is not known, but ancillary information (e.g., remotely-s ensed data), corresponding to its spatial pattern, is available.