Remote Sensing and GIS Related Research

While working on my masters at UGA I also completed a certificate program in geographical information science. One of the research projects I was involved with was mapping hydrothermal mineral assemblages in the Uzon Caldera, Kamchatka, Russia with ASTER and Quickbird imagery. I collaborated with researchers in the UGA Department of Geology who were participating in a multi-disciplinary NSF-funded project (NSF MO 023840) to conduct mineral and biological mapping in the active hydrothermal area in Eastern Russia. All previous work was ground/point-based and limited in scope for such a large area due to the difficulties of covering the entire region during field surveys. This provided more complete coverage of the caldera and the potential to quickly map the ever changing hydrothermal system.

The data sets suffer from the limiting factors of spatial and spectral resolution. While ASTER has a higher spectral resolution (14 separate spectral bands, 9 in the VNIR and SWIR range suitable for mineral mapping) it is limited in it's spatial resolution for this particular application. The thermal fields are on the order of a few hundred meters and features within them are on the order of a meter, therefore the 15m (VNIR) and 30m (SWIR) resolution of ASTER is not capable of mapping features at this scale. Quickbird on the other hand has better spatial resolution (0.6m) but is limited by only have 4 spectral bands (in the VNIR range). The main question was whether hydrothermal mineral endmembers could be detected with this limited spectral information. We initially looked at the spectral curves of some mineral from the JPL, USGS, and ASTER spectral libraries, and it appeared that the signatures within the VNIR range were unique enough to distinguish individual endmembers.

The analysis and classification was done the same way for both ASTER and Quickbird. Both images were atmospherically corrected based on a basalt signature from the spectral library. Then using Leica's Imagine Spectral Analysis Workstations, minerals were selected from the spectral library and using the Spectral Angle Mapper technique all likely pixels were identified in the image that matched the mineral signatures. The ASTER classification only provided useful information on the entire region. The high spatial resolution of Quickbird imagery was sufficient to discriminate small (meter-sized) features associated with hydrothermal fields even with the limited spectal sensitivity. The conclusion from this research is minerals commonly found in geothermal environments, including a suite of clay minerals, oxides, sulfates, sulfides, and sulfur, can be readily discriminated from one another using commercially available spectral analysis software in conjunction with Quickbird imagery.