Lab 8 - Thermal and Multispectral Analysis

       In this lab, we learn to interpret radiant energy, to create composite multispectral imagery in both ERDAS Imagine and ArcMap. We also learn to change band combinations, and we further investigate adjusting imagery by adjusting breakpoints.

   This exercise demonstrates the basic principles of thermal energy and radiative properties, specifically the concepts associated with Wien’s Law and the Stefan-Boltzmann Law. The radiant energy is proportional to the fourth power of an object’s temperature, so the Sun emits more radiant energy than the Earth. We also see that the energy peak moves to shorter wavelengths as the temperature increases. This is why we call incoming solar radiation “shortwave” radiation and outgoing terrestrial radiation as “longwave” radiation.

.      The second exercise of the lab is a really good introduction to combining multiple layers into a single image file both by using Layer Stack in ERDAS and Composite Bands in Arcmap. As pointed out in the lab, it is much quicker and easier in Arcmap, but it’s good to know how to do this in ERDAS as well.

.      We also learned to analyze the image, both in panchromatic and multispectral bands, and manipulated the different spectral bands being displayed by manipulating the breakpoints to better distinguish certain features. We saw the difference between images taken in winter versus images taken in summer by looking at the thermal infrared band. It’s really interesting to see the diurnal pattern and the differences in specific heat capacity, with features that heat up quickly during the day also cooling quickly at night or in winter.

       The final portion of the lab tasked us with displaying different bands of multispectral imagery I decided to use the Pensacola composite image and to identify urban features. I still am learning how to adjust breakpoints, but this part gave me a lot of practice with it. I also used a lot of trial and error regarding which bands of the imagery would best display the feature that I wanted, which was rather tricky. Often, the urban areas were not displayed well; they were usually too light and although you could see the general area, you couldn’t really distinguish any features. Using the bands I chose, there is good contrast between the urban areas and adjacent areas, such as the water and the vegetation, and the urban areas are pretty well defined. Below is my image of Pensacola. The feature I wanted to identify was the urban areas, and they are clearly defined as the pink/lavender area, mainly west of the river that runs north-south through the middle of the image. For this image, I am displayed bands 1, 4, and 6. Band 1 shows blue wavelengths, and is often used to display man-made features. Adjusting the breakpoints of this band made the image clearer. Band 4, or near-IR, is often used to identify vegetation, but here after adjusting breakpoints to limit the amount of red in the image, the urban areas stood out. The band that completes the image is band 6, which is thermal IR. Urban areas are often warmer than surrounding areas, and that is the case here. Additionally, with the thermal IR layer, you can clearly see the fire in the far northeast portion of the image.