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Module 5a - Intro to ERDAS Imagine and Digital Data

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This map shows images in classes by area in a forested section of Washington state. It was  created  by a combination of ERDAS Imagine and ArcPro. Step 3 caused me the most problems, but it was not an extremely difficult task. I opened my tm_class.img to the viewer, opened the attribute tab, and added an area field. It was necessary that I change the name of the field so that it would show up in arcpro (I missed this step the first time). Then I went through the process of removing the layer so that the save dialog box would appear with no  errors. Then I reopened the .img and created a subset image of the map by using an inquire box named “tm_subset.img”. The errors began when I tried to run ARCPro. The program gave me a “loading map…” screen no matter how many times I force-quit, logged off, or even restarted my computer. After that issue was resolved, I was able to add my new  subset image to the map and add a legend and essential elements. I then...
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Module 11 Lab: 3D Mapping

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This week, we were instructed to use the ESRI training module to learn about 3D maps. I studied their background, the data required to create them, and their applications.  Three dimensional mapping uses TINs, raster data, and z-values to create a map that shows depth. The values can portray proportional elevations of terrain and manmade features such as buildings.  In my process summary, I described why a 3D building map is advantageous: A 3D building layer reveals patterns that are not visible in 2D. As noted in ESRI training, 3D modules can help one visualize how buildings will interact and merge with other buildings and landscaping. They can also help one visualize a route more accurately as the buildings they will encounter can act as landmarks, a feature that is unavailable in a 2D map. There were three major parts of the lab. The first was the focused  solely  on ESRI training using ArcScene which was composed of six exercises.  Exerc...
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Module 6 - Spatial Enhanceme

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I created a map deliverable that utilized the best method of reducing visibility of the lines I could find. I tried many different types of kernel filters and determined that a 3x3 high pass filter looked the best to me. I also tried edge detect, edge enhance, other sharpen filters, and running the fourier transformation again on the fourier1.img layer, but none of the enhancements made the image as clear. I also attempted to create a custom kernel like the example, but I was unable to run this successfully.
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