Recent advances in remote sensing technologies through the availability of more sensitive instruments, and improvements in geo-location technologies and analysis software have resulted in significant strides in the successful application of high resolution remote sensing to feature identification such as soil disturbances. These strides have been achieved by field-oriented remote sensing experts, including researchers from HyPerspectives, who have demonstrated the capability to (1) precisely identify small discrete landscape features over large spatial areas and (2) detect changes in those features, both necessary requirements for accurate and precise soil disturbance detection. Hyperspectral sensors excel in discriminating subtle changes in landscape conditions. The second active technology is synthetic aperture radar (SAR) capable of measuring return rates from a host of different wavelengths. The weaknesses of SAR data are the strengths of hyperspectral imagery and analysis. Using enhanced high-resolution spectral analysis, and innovative spatial analysis; the two technologies can be combined to precisely discriminate small area landscape disturbances. The proposed project recognizes the complimentary strengths of hyperspectral and SAR imagery. The project identifies, tests fusion algorithms and demonstrates a new set of tools to more precisely identify soil disturbances at the pixel and sub-pixel level
