Up-scaling methods based on variability-weighting and simulation for inferring spatial information across scales

Appropriate up-scaling methods to infer spatial information from a finer to a coarser spatial resolution are required when remote sensing and geographical information systems (GIS) are used to generate multi-scale maps that are needed for agriculture, forestry, natural resources, environmental systems, and landscape ecology. The existing methods used in commercial GIS and image analysis packages such as Window Averaging (WA) often do not work well because of different limitations. In this study we developed and compared five widely used WA methods including three spatial variability-weighted methods and two simulation methods. These methods were assessed in a case study for aggregating and using Landsat Thematic Mapper (TM) images for mapping vegetation covers and for inferring a topographical factor related to soil erosion from finer to coarser resolutions. The results showed that the Beta Distribution Simulation (BDS) method was better than WA regardless of the distributions of the spatial data, while the Arithmetic Average Variability-Weighted method (AAVW) performed better than WA for normal distributions. BDS is flexible for variable distributions and AAVW is only suitable for normal distributions. Because of their simplicity, efficiency, and flexibility, it is expected that these two methods can be programmed into commercial GIS and image analysis packages.