GIS 4035 Module 8: Thermal and Multispectral Analysis

Imagery combining the thermal infrared band (6) with the shortwave infrared bands (5 and 7) to highlight fires.

The eighth module of the course focused on using thermal infrared imagery to extract data unavailable from other EMR wavelengths. The general concepts and tools were similar to previous exercises, yet thermal infrared imagery presents unique challenges. First, thermal infrared EMR is emitted, not reflected; the amount of thermal infrared EMR emitted by a feature is a combination of the amount of energy absorbed, the feature's composition and surface characteristics, and the sensitivity and exposure length of the image (among other factors). In simple terms, thermal infrared EMR reflects temperature, but we cannot assume a direct correlation without calibration.

Our deliverable for the week was more open-ended than usual; we were to choose an area or feature in one of the two composite images created for the module and create an image that highlights the chosen area or feature. The thermal infrared band needed to at least be used to identify the feature even if it wasn't used in the final layout. While we had already identified select fires in the imagery, I was struck by how defined the fires' core extents appeared when the thermal infrared band was combined with the shortwave infrared bands. After applying a Gaussian stretch in Imagine and a minimum-maximum stretch in ArcMap, the fires popped out from the background significantly. This did allow me to identify a third, small fire southeast of the largest fire that I had not noticed previously.

I enjoyed the experimental nature of this module and learning of the unique data one may extract from thermal infrared imagery. I am still not confident in my ability to manually manipulate histogram breakpoints to achieve fruitful results, but this module did help me significantly understand the areas I need to work on.