Thoger Boyum Geography 438 Blog

Background and Goal:     The mail goal of this lab was to gain experience in extracting LC/LU (Land use/ Land Cover) information from remotely sensed images using pixel-based supervised classification. Another goal was to understand how to select and properly add training data to a supervised classification. Methods:     Simply put, the method used was to get straining samples, combine into their separate classifications, and then run the supervised classification.      As for a more detailed method, we collected 12 water training samples, 11 first, 10 agriculture, 5 for urban areas, and 12 for areas of bare soil. These samples were then viewed on the mean data plot windows, and outliers were removed and replaced. Below is the mean plot of all of the signatures used for this exercise. Signature mean plot used for this exercise     From there a separability report was r...

 Background and goal:     The goal of this lab was to gain skills and understanding of extracting land surface temperature information from thermal bands of satellite images and drone imagery. Methods:     Starting off, we brought different types of imagery into Erdas Imagine to look for tonal quality differences, as well as practice identifying relatively warm to cool features.     From there, we began to get from DN(digital numbers) to the radiant surface temperature on 2000 thermal imagery of Eau Claire.. That was done by determining the Grescale & Brescale values of the data, and then using the model maker to run two separate models to get the radiant temperature.     Lastly, we took 2014 thermal imagery of Eau Claire and did a similar process to it as the 2000 data.  The exception was that the data was first trimmed down using an AOI file, leaving the area surround...