This map represents a section of forested land in Washington. It is classified based on land cover data obtained from Landsat TM satellite. Area (in hectares) of each class is added in the legend.
Module 3a:
This section of the module was straightforward. It walked through opening files in ERDAS Imagine (ensuring correct Raster Options and Multiple file selections). Multiple ways to zoom were explained and I preferred the mouse wheel. Holding the mouse wheel to pan was interesting. I found you can also use this to pan in an ArcGIS Pro layout which was really helpful! Changing the default data directory and default output directory (through file-preferences) is important for future projects. Changing the band combinations in the Multispectral tab is useful to identify different elements. 5,4,3 (R,G, B) is also called ™ False Natural Color.
Next, step in this lab was to prepare a section of a classified image and put it in ArcGIS Pro. I added an Area column to the attribute table which was needed in the final layout. I exported a subset of the image from what I selected in the Inquire Box. In ArcPro, I added my tm_subset.img and symbolized it with unique values by Class_Name. Finally, I made a layout with all essential elements. In order to add area to the legend, I changed the class label in the symbology pane and manually entered each ha number from the attribute table.
Module 3b:
First, I opened subset_tm_00.img using the open button on the quick access menu. I then followed the lab instruction in examining the file’s metadata. It was helpful to note that a continuous layer type means it is a raster layer with each cell having its own value. The statistical info section gives brightness value info of each band layer. I thought this was a confusing name. High mean/median/mode means that a lot of the radiation is reflected in that band. Coordinate info is given for each corner based on the projection's units. Pixel size is the spatial resolution. It's important to note that if there is no projection info then it's an unrectified image.
In the next section, I opened pensacola_sra through pensacola_srd to examine spatial resolution. Each image had a different spatial resolution. This could be seen visually and the pixel size in the metadata. I had a bit of an issue switching to 1:3000 scale. Sometimes, when I entered the new scale the view would turn black. I had to switch between “fit to frame” and entering the new scale a couple times for it to work.
The next images and section dealt with radiometric resolution. I opened pensacola_rra through pensacola_rrd. I followed the lab instructions and examined the DNs and the bit relationship.
Section 4 covered thematic rasters and attributes using soils_95.img and hydro_00shp. I was working to examine soil characteristics and calculate area/percent area coverage of different soil types. It took me a minute to figure out how to add the shapefile because I was in “Open Raster Layer” not “Open Vector Layer.” Each color in the image represents a different soil type which can be viewed in the attribute table. I added an area and percent column to the attribute table. Selecting soils based on criteria was more difficult than creating the columns. This was to select soils that are sensitive to erosion (humus-rich and fine texture). I was successful because 2 rows were selected.
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