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GIS 4035, Lab 4: Spatial Enhancement and Multispectral Analysis

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This was one of the more interesting labs that we have done. The first part was primarily concerned with fundamental skills in ERDAS Imagine, like using the chip image and subset tool, opening histograms, using the enquire cursor, and exporting data for use in ArcGIS Pro. We also accessed public domain US satellite data to analyze in Imagine. In the later part of the lab, we identified features according to criteria on the lab guide, such as features that caused a spike in pixels in a certain range of brightness in specific EMF bands, and had to use visual analysis and the enquire tools in Imagine to find them. Then, we created maps with combinations of RGB colors in different bands to show off which feature met the criteria. This was interesting, in part, because we had to think about color theory a little bit and why something bright in certain bands, when assigned colors in those bands, would produce the map effects that we got (i.e. if we assigned blue and green to bands where...
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GIS4035 Lab 2, Land Use/Land Cover

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This was not a particularly hard lab, but the main challenge was time management. We were given an aerial photo of Pascagoula, MS and had to classify the entirety of it to level II land use/cover categorizations. This took a lot of inferences and assumptions - we were discouraged from using other sources in the initial part, then checked our accuracy with Google Street View. I actually had a fairly high accuracy.
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GIS4930 T2 M1

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In this lab, I worked through a series of exercises designed to compare and apply two fundamental elevation data models: TINs (Triangulated Irregular Networks) and DEMs (Digital Elevation Models). The workflow highlighted how these models can be visualized, analyzed, and compared to better understand terrain. Part A: Draping imagery over terrain This part of the lab had us looking at Death Valley with a remotely sensed image draped over a TIN, which was used as an elevation layer to produce a 3d image. We added vertical exaggeration to reveal surface features that weren't noticeable in the flat imagery. Part B: Ski run suitability modeling Next, I built a suitability model using a DEM. I reclassified elevation, slope, and aspect into categories of suitability, then combined them in a Weighted Overlay. The resulting raster identified areas best suited for ski runs, with greens showing higher suitability and reds showing lower suitability (see screenshot below). I also got to u...
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