2014 GSA Annual Meeting in Vancouver, British Columbia (19–22 October 2014)

Paper No. 324-1
Presentation Time: 9:00 AM


HUBBARD, Bernard E., US Geological Survey, 12201Sunrise Valley Drive, Reston, VA 20192, DESZCZ-PAN, Maria, U.S. Geological Survey, Box 25046, M.S. 964, Denver Federal Center, Denver, CO 80225-0046, SMITH, Bruce D., U.S. Geological Survey, Crustal Geophysics and Geochemistry Science Center, Denver Federal Center, Bld 20, ms964, Lakewood, CO 80225, DAY, Warren, U.S. Geological Survey, P.O. Box 25046, MS 973, Denver, CO 80225-0046, GOUGH, Larry, U.S. Geological Survey, 12201 Sunrise Valley Dr, MS954, Reston, VA 20192, KASS, M. Andy, Crustal Geophysics and Geochemistry Science Center, US Geological Survey, Denver Federal Center, MS 964, Denver, CO 80225, EMOND, Abraham, Division of Geological & Geophysical Surveys, Department of Natural Resources, 3354 College Road, Fairbanks, AK 99709 and CAINE, Jonathan Saul, U.S. Geological Survey, Box 25046, DFC, MS 964, Denver, CO 80225-0046, bhubbard@usgs.gov

Geological mapping of the bedrock in much of Alaska is very difficult, not only because of the large size and remoteness of much of the state, but also because most of the potential bedrock exposure is covered either by forest canopy, muskeg soils, and understory vegetation or coated by lichens and moss. The active layer above discontinuous permafrost and weathered rock are additional impediments in locating outcrops. Spotting outcrops from helicopters to provide support for geologists can also be difficult because brown-colored moss is often mistaken for rock. Outcrops are crucial for lithologic and structural observations and measurements needed for evaluation of mineral resources and for collecting samples for laboratory analytical work. In this study, we show that digital video data collected during airborne electromagnetic (AEM) geophysical surveys, combined with processed and visually interpreted Landsat images, can be used to interpret the location of natural outcrops and particularly bedrock exposed after large wildfires. Assuming that bedrock outcrops are electrically resistive, we correlate areas of high resistivity derived from AEM data with Landsat false-color images and known bedrock outcrops to vegetation abundance maps derived from linear spectral unmixing of the imagery. This method was tested in an unburned area near Mount Veta, Alaska in the western Fortymile mining district, where new geologic mapping was recently completed at 1:63,360-scale. AEM data was processed to estimate the electrical resistivity, and it’s resolution, of layers from the surface to a depth of 50 m along flight lines. The estimated shallow resistivity (<5m) was correlated with high-resolution video imagery and Landsat fractional abundances showing highest rock to vegetation proportions per pixel (>90% rock after normalization to shade and other featureless components). High resistivity permafrost zones can be distinguished in unburnt areas based on their high proportions of vegetation and spectral signatures dominated by white spruce and sphagnum moss. In recently burned areas, Landsat spectral unmixing allows bedrock exposures to be distinguished from dry and burnt vegetation dominated by charcoal and cellulose; as well as recovering green vegetation types such as fireweed.