There and Back Again

This was an interesting lab. Most of it was completed in ERDAS Imagine. We took a high resolution aerial photo of the UWF campus, and used an Imagine function to break it into 50 image classifications. It was "unsupervised" because Imagine chose the classes and class breaks. We then manually went through the attributes table and used the inquire cursor to change every class to one of 5 classes: forest, grass, buildings/roads, shadows, and mixed. We then merged all the rows into these 5 categories and used the recode function to produce a new raster. Using the recoded raster, we then added an area column to the attributes, and calculated the total permeable and impermeable surfaces in the map. In the next part, we did supervised classification, meaning that we selected map features manually by both drawing polygons, and using the grow from seed function and adjusting euclidean distance and neighbrorhood settings to use the program to infer the extent of features. From ther...

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...

This was an interesting lab. Almost one year into the program, and this is one of the few times using a program that is not ARCPro. (Off the top of my head, we've also used Google Earth Pro and FieldMaps, as well as a Python shell). A lot of the lab was spent using Maxwell and Planck's Equations to find wavelength and energy values for different problems. Then we used a new program, ERDAS Imagine, to compare rasters of different data and explore resolution values and how color schemes effect what's visible. Then, we practiced exporting ERDAS data to ARCPro and creating a map. Finally, we returned to ERDAS Imagine and examined multiple raster sets in 4 simultaneous viewers, and played with using formulas to calculate values for area.

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.