Yellowstone National Park (YNP) contains the world's largest concentration of geothermal features, and is legally mandated to protect and monitor these natural features. Remote sensing is a component of the current geothermal monitoring plan. Landsat satellite data have a substantial historical archive and will be collected into the future, making it the only available thermal imagery for historical analysis and long-term monitoring of geothermal areas in the entirety of YNP. Landsat imagery from Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors was explored as a tool for mapping geothermal heat flux and geothermally active areas within YNP and to develop a change analysis technique for scientists to utilize with additional Landsat data available from 1978 through the foreseeable future.

Terrestrial emittance and estimates of geothermal heat flux were calculated for the entirety of YNP with two Landsat images from 2007 (TM) and 2002 (ETM+). Terrestrial emittance for fourteen summer dates from 1986 to 2007 was calculated for defined geothermal areas and utilized in a change analysis. Spatial and temporal change trajectories of terrestrial emittance were examined. Trajectories of locations with known change events were also examined. Relationships between the temporal clusters and spatial groupings and several change vectors (distance to geologic faults, distance to large water bodies, and distance to earthquake swarms) were explored. Finally, TM data from 2007 were used to classify geothermally active areas inside the defined geothermal areas as well as throughout YNP and a 30-km buffer around YNP.

Estimations of geothermal heat flux were inaccurate due to inherent limitations of Landsat data combined with complexities arising from the effects of solar radiation and spatial and temporal variation of vegetation, microbes, steam outflows, and other features at each geothermal area. Terrestrial emittance, however, was estimated with acceptable results. The change analysis showed a relationship between absolute difference in terrestrial emittance and earthquake swarms, with 34% of the variation explained. Accuracies for the classifications of geothermally active areas were poor, but the method used for classification, random forest, could be a suitable method given higher resolution thermal imagery and better reference data.