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Recent work has shown that more research is needed in applying fractal analysis to multi-resolution remote sensing data for landscape characterization. The purpose of this study was to closely examine the impacts that spatial and spectral resolutions have on fractal dimensions using real-world multi-resolution remotely sensed data as opposed to the more conventional single resolution and aggregation approach. The study focused on fractal analysis of forested landscapes in the southeastern United States and Central America. Initially, the effects of spatial resolution on the computed fractal dimensions were examined using data from three instruments with different spatial resolutions. Based on the criteria of mean value and variation within the accepted ranges of fractal dimensions, it was determined that 30-m Landsat TM data were best able to capture the complexity of a forested landscape in Central America compared to 4-m IKONOS data and 250-m MODIS data. Also, among the spectral bands of Landsat TM images of four national forests in the southeastern United States, tests showed that the spatial indices of fractal dimensions are much more distinguishable in the visible bands than they are in the near-mid infrared bands. Thus, based solely on the fractal analysis, the fractal dimensions could have relatively higher chances to distinguish forest characteristics (e.g., stand sizes and species) in the Landsat TM visible wavelength bands than in the near-mid infrared bands. This study has focused on a relative comparison between visible and near-mid infrared wavelength bands; however it will be important to study in the future the effect of a combination of those bands such as the Normalized Difference Vegetation Index (NDVI) on fractal dimensions of forested landscapes

Effects of Spatial and Spectral Resolutions on Fractal Dimensions in Forested Landscapes