Module 3 introduced us to the fundamentals of electromagnetic radiation and the wide variety of satellite imaging sensors used in remote sensing.
I’ll admit, the reading and lecture material this week felt incredibly overwhelming. There was so much information to take in that I initially thought I hadn’t retained any of it. However, by the time I took the quiz, I realized I had absorbed much more than I thought.
This week’s lab provided a hands-on introduction to ERDAS Imagine and how digital remote sensing data is structured, interpreted, and displayed. Navigating ERDAS Imagine felt familiar since its interface and tools are very similar to ArcGIS Pro.
We learned the basic functions of ERDAS Imagine, including how to prepare and export a classified image of forested land in Washington State. After creating the subset, we imported it into ArcGIS Pro to build a final map layout (shown above).
In Part B, we explored how two of the four different types of resolution (spatial and radiometric) affect image quality and detail. Using a series of images of Pensacola, we compared pixel sizes and bit depths to see how they influence image clarity and contrast. We also examined image metadata to understand how raster data is stored and analyzed. We then worked with a thematic soil raster to calculate area and percent coverage for erosion-prone soils.
Overall, this lab was a lot of fun. I always enjoy being introduced to new tools used in the GIS industry, and ERDAS Imagine is a powerful program with many advanced capabilities. I’m looking forward to exploring more of what this software can do in future labs.
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