HR: 11:40h
AN: V52C-06    [Abstracts]
TI: Characterizing Thermal features in Norris Basin, Yellowstone National Park, Using Multi- spectral Remote Sensing Data and Dynamic Calibration Procedures
AU: * Hardy, C C
EM: chardy01@fs.fed.us
AF: USDA Forest Service--Rocky Mountain Research Station, Missoula Fire Sciences Laboratory 5775 US Highway 10 West, Missoula, MT 59808, United States
AU: Queen, L P
EM: lpqueen@ntsg.umt.edu
AF: National Center for Landscape Fire Analysis University of Montana College of Forestry and Conservation, CHCB 441, Missoula, MT 59812, United States
AU: Heasler, H P
EM: henry_heasler@nps.gov
AF: Yellowstone National Park Yellowstone Center for Resources, Yellowstone Center for Resources, Yellowstone N.P., WY 82190, United States
AU: Jaworowski, C
EM: charyl_jaworowski@nps.gov
AF: Yellowstone National Park Yellowstone Center for Resources, Yellowstone Center for Resources, Yellowstone N.P., WY 82190, United States
AB: A thermal infrared remote sensing project was implemented to develop methods for identifying, classifying, and mapping thermal features. This study is directed at geothermal features, with the expectation that new protocols developed here will apply to the wildland fire thermal environment. Airborne multi-spectral digital imagery were acquired over the geothermally active Norris Basin region of Yellowstone National Park, USA. Two image acquisitions were flown, with one acquisition near solar noon and the other at night. Raw data from the five sensors were uncalibrated, so a vicarious calibration procedure was developed to compute reflectance for the visible and NIR bands using an independently calibrated hyperspectral dataset. Calibration of the thermal sensor band utilized a dynamic, in-scene calibration procedure that exploited natural, pseudo-invariant thermal reference targets instrumented with in situ kinetic temperature recorders. The calibrated reflectance and radiant temperature data from each acquisition were processed and analyzed to develop a suite of thermal attributes, including radiant temperatures, a daytime-nighttime temperature difference (DeltaT), albedo, an albedo derivative (one minus albedo), and apparent thermal inertia (ATI). The albedo terms were computed using a published weighed-average albedo algorithm based on ratios of the narrowband red and near-infrared (NIR) reflectances to total solar irradiance for the respective red and NIR bandpasses. The weighing factors for each band were the proportion of total solar irradiance incident on the surface within each segment represented by a respective bandpass. In the absence of verifiable "truth," a step-wise chain of unsupervised classification and multivariate analysis exercises was performed, drawing heavily on "fuzzy truth" to assess the quality, efficiency, and efficacy of classification procedures and results. A final classification synthesizes a "geothermal phenomenology" comprised of four analytical components: spectral, statistical, geographical/contextual, and feature space. The four-component phenomenology developed for this geothermal classification provides the "1st-of-its-kind" basis for field-based explorations as well as for image-based analyses utilizing other geospatial information. This study has demonstrated that a classification gradient provides more discriminating information than a ‘hard' classification.
DE: 0350 Pressure, density, and temperature
DE: 8411 Thermodynamics (0766, 1011, 3611)
DE: 8424 Hydrothermal systems (0450, 1034, 3017, 3616, 4832, 8135)
SC: Volcanology, Geochemistry, Petrology [V]
MN: 2007 Fall Meeting