MAPPING ACTIVE THERMAL FEATURES IN YELLOWSTONE NATIONAL PARK USING AIRBORNE MULTI-SPECTRAL REMOTE SENSING DATA WITH DYNAMIC CALIBRATIONS

A thermal infrared remote sensing project was implemented to identify, classify, and map active thermal features over the Norris-Mammoth corridor of Yellowstone National Park, USA. The study was performed in support of Yellowstone's geothermal monitoring program. Two airborne multi-spectral digital image acquisitions were flown in October, 2002, with one near solar noon and the other at night. The five-band image data included thermal infrared (TIR), near-infrared (NIR), and three visible bands. Pixel resolution for the TIR, NIR, and visible bands was 2.33m, 0.7m, and 0.7m, respectively. While focused on TIR, the study relied on the multi-spectral visible and NIR data as well as on an ancillary hyperspectral data set. The raw, five-band data were uncalibrated, requiring implementation of two calibration protocols. First, a vicarious calibration procedure was implemented for the visible and NIR bands using an independently calibrated hyperspectral dataset; second, a dynamic, in-scene calibration procedure was used for the thermal sensor that exploited natural, pseudo-invariant thermal reference targets instrumented with kinetic temperature recorders. A suite of thermal attributes was derived, including daytime and nighttime radiant temperatures, a temperature difference (DeltaT), albedo, and apparent thermal inertia (ATI). In the absence of verifiable “truth,” a step-wise chain of unsupervised classification and multivariate analysis exercises was performed.

A final classification synthesizes a “thermal phenomenology” comprised of four components: spectral, statistical, geographical/contextual, and feature space. In situ measurements paired with image data provided effective calibrations, although results were strongly influenced by detrimental effects of requisite pre-processing routines. The classification gradient utilizing cluster-busting provided more discriminating information than a ‘hard' classification. The final classification was highly coincident with an existing Yellowstone data layer of geothermal features, where over 97% of pixels classified in the present study as “geothermal” were within polygons classified by the Park as thermal areas.