This map shows a supervised classification of the land use in Germantown, MD. Class signatures were created for the features in the legend above and colored with the R4 G5 B6 color combination. A supervised classification image file was then created and recoded to exhibit only 8 classes. Area was calculated in hectares for each class and displayed in the legend. The fallow fields are the brightest of the features because they represent where I would recommend that Maryland’s "Smart, Green and Growing Initiative" expands to next if I were to present my map to the Governor.
This lab gave me a lot more issues than I anticipated. The concepts weren't that complicated, but I just seemed to keep making little mistakes that led to a bad map. I had to restart Exercise 5 about four times and I still think it could be more accurate. I was able to tinker around a lot and figure out how to fix small issues like being able to get more accurate signatures by drawing polygons at a 1:5000 scale instead of using the "grow" tool, and how to fix the number of classes that transferred over to ArcGIS Pro from ERDAS Imagine (it brought over all 18 instead of the 8 that I recoded the image to). The bright side is that I have gotten plenty of quality practice with unsupervised and supervised classification.
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