This week our lab assignments focused on learning the basics of electromagnetic radiation (EMR) and using ERDAS Imagine to work with this data. In the Part A of the lab, we calculated wavelength, frequency, and energy of EMR. The formulas we used included Maxwell's Wave Theory (C = λv) and the Planck Relation (Q = hv). The big take away on how EMR works is that longer waves have lower frequencies and less energy while shorter waves have higher frequencies and more energy. I think I managed not to get lost amongst the zeros and decimal places!
After these calculations, we did a few simple activities in ERDAS Imagine to get us familiarized with the software. This included opening files, saving files, zooming and panning, changing the band settings for visualizations, calculating areas, and exporting a subset of data to create a map. Apparently Imagine can have some issues with map preparation, so we exported a raster and finished the map layout in ArcGIS Pro. I choose a subset of the data that included some lovely mountain ranges and had all pixels classified. Take a look at my map below!
In Part B of the lab, we did a deep dive into exploring raster data. We looked at three datasets to explore Metadata. The first was one file with multiple layers. In this exercise, we learned how to open the Metadata menu and what each part of the information means. In the second exercise, we looked at spatial resolution by comparing four images with various resolutions, from 2m up to 16m. The file with each pixel representing the smallest area (2m) had the highest resolution which means it had the most detail. The 16m image was super pixelated and hard to see what was being depicted! The third exercise involved looking at the radiometric resolution of four different images. We examined the max digital umber, the data type (how many bits), and how many wavelengths or bands each image had. While the difference in this resolution was less noticeable to the eye, the files with higher radiometric resolution have more information documented within the pixels so software can get more detail from the data.
The last exercise for Part B included using some simple tools to calculate and mask data. We learned how to change the colors of features in the Attribute Table, as well as how to add columns of data. We completed a simple task of calculating the area and the percentage of the total area for each soil type. Many of these tools were very similar to those in ArcGIS.
ERDAS seems like a pretty cool program to help with exploring and manipulating raster data. I'm excited to see what else we do with it!
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