GIS5027 Module 3: ERDAS Imagine & Digital Data

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Oh we meet again, ERDAS Imagine. I haven't touched that program in quite some time -- probably at least a year or two at this point. We seldom use it for work; we mostly rely on it for creating and cleaning up large mosaics, like in the countywide historical aerial initiative that the GIS team was working towards a while back. Frankly, we haven't been keeping up with that legacy project, due to the workload and overall demand for GIS support increasing; however, I will admit my time with ERDAS was not the most pleasant back then when I first joined the GIS team. Lots of crashing and confusion occurred back then, especially when compared to ArcGIS Pro, the primary program that is utilized in our workflows.

Thankfully, I was grateful for the kind and easy to digest introduction into ERDAS Imagine this second time around. Taking this course, I'm definitely seeing other ways of analyzing and processing imagery through this program, which is really cool! I've warmed back up to ERDAS again and it doesn't seem so daunting or frustrating as before.

Module 3's lab involved working in mainly ERDAS, with ArcPro being used to create the map layout. The first half of the lab was all about calculating EMR properties using Maxwell's wave theory and Planck Relation. We then got to get our feet wet in navigating around in ERDAS Imagine that ended with creating a subset of a LANDSAT Thematic Mapper satellite image for us to use in ArcGIS Pro when making the map layout. You can see that subset image in the screenshot below. It features the area around the Olympic Mountains of Washington state, showcasing the land cover classification found throughout and the hectare amounts associated with each.

Screenshot of the subset of the LANDSAT Thematic Mapper satellite image from part 1 of Module 3's lab. 6 different classifications are shown within my selected area of interest.

Finally, part 2 of the lab involved us getting used to viewing the Metadata of different images, understanding spatial resolution and how to spot different levels of it, as well as how that compares to radiometric resolution and seeing examples of that too. Spatial resolution relates to pixel size -- the smaller the pixel size, the more distinguishable and clear the image details will be at closer extents. On the other hand, radiometric resolution is about the brightness value range -- the greater the bit data type or the maximum digital number, the greater that brightness range.

Overall, seeing these different images and resolutions really reminded me of playing games on the SNES, like Super Mario World. The image in the map layout especially gave me so much nostalgia of those old computer games' art style, due to the pixel limitations of that time.

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