Nicole's Geographic Journal

      For lab 5, I learned the difference between unsupervised and supervised classification in ERDAS Imagine.  In exercise 1, I completed an unsupervised classification. I identified individual pixels and assigned them to a feature (trees, roads, shadows, etc.). I did this by using the attribute table to change the colors of the pixels to colors that are easier to identify for each feature .  In exercise 2, I created an AOI image to collect spectral signatures. First, I used the inquire cursor to identify different coordinates and create polygons to highlight them. I used the signature editor to name them properly. I then used region growing properties to create points of interest, and used the inquire (legacy) tool to type in exact coordinates. In exercise 3, I used histograms to evaluate signatures.      I enjoyed this lab because I was able to label specific coordinates with specific features, such as la...

      This week's lab covered enhancing satellite images and how to interpret them via spectral bands. We continued to use ERDAS Image this week, using many of the tools discussed last week. This week's lab as a bit easier than last week; by revisiting ERDAS and applying last week's lab knowledge, I had an easier time navigating through the  software. I used the bands of the satellite image to identify different features within the image. In reference to the image above, bands are referred to as layers in the histograms.      The histograms in the image above denote the distribution of pixels in each band, and state the mean. Layer 1's mean is 57.8, layer 2 42.9, and layer 3 37. In color images, pixel values range from 255 (white) to 0 (black). Layer 1 has the highest value, and therefore is the brightest band. Layer 3 has the lowest value, and therefore is the darkest band. 

      Today's lab was an introduction to ERDAS Imagine, which is software used for  satellite imagery. In this lab, I used ERDAS to determine pixel size, percent area coverage of soil types, calculating percent area of soils that have the highest potential for erosion, and the difference in vegetation. I found ERDAS pretty difficult to use; the labeling of different buttons and functions was poor and I had to restart some steps a few times. Otherwise, it was interesting to see how different softwares can analyze satellite images, and how it can be useful in ArcGIS.