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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 10 - Supervised Classification

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Exercise 4 required me to combine the skills I learned in the previous exercises to create a map detailing the land use for an area in Germantown, Maryland. To create this map, I needed to go back through each of the previous steps to choose the best course of action for this type of map. I created classes for each of the different land uses, addressed any spectral confusion, and then consolidated like-classes. I chose colors for each class that stood out from each other. Finally I opened my map in arcpro, added essential elements, and exported my map.
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Module 8 Lab: Isarithmic Mapping

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This week we created two isarithmic maps, one with continuous tones and the other with hypsometric tints. It was important to explore both of these mapmaking styles as they display the same data presented in different ways.  The first important thing to note is how this data is collected and interpolated.  By implementing data prepared using the PRISM method, the maps we created are interpolated using the relationship of  location, elevation, coastal proximity, topographic orientation, vertical atmospheric layer, topographic position,  and orthographic effectiveness of the terrain to precipitation. This algorithm, created by Chris Daly, takes  into account variants such as rain shadows that would otherwise skew data on a map focusing strictly on the  relationship of elevation to precipitation. The first map we created was one with continuous symbology. Since the data was already presented with a stretched color scheme, there was not much ...
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