Application of Geostatistical Simulation to Enhance Satellite Image Products

With the deployment of Earth Observing System (EOS) satellites that provide daily global imagery, there is increasing interest in defining the limitations of the data and derived products due to their coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery provided by systems such as the EOS MODerate Resolution Imaging Spectroradiometer (MODIS). Higher spatial resolution data such as the EOS Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat Thematic Mapper and airborne sensor imagery provide more detailed information but are less frequently available.

There is, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We present example EOS products, an analysis to investigate self-similarity and a discussion of simulation approaches.