Emily's GIS Blog

  In this week's assignment, we were familiarized with ERDAS Imagine and using digital data within it. I created this map layout with Imagine and ArcGIS Pro. It is a subset of a digital image from the Pacific Northwest in the US. Below is my process summary.  First, I opened the file tm_class.img to the viewer within ERDAS Imagine. I then created a new field in the attribute table that contained the area, in acres, of each class. In order to view a subset of the original image, I selected the Create Subset Image tool in the Raster tab. I then selected the area I wanted, designated my output file, and selected ok to create the subset. I then opened the subset file in ArcGis Pro to create my map layout. I changed the symbology of the image to unique values in order to symbolize the layer according to the classes. I included all map elements and the area of each class in the legend. I did this by editing the name of each class in the symbology tab.

  In this assignment, we were required to assess the land use and cover of an area in Pascagoula, MS. We used polygons to distinguish the various land use and cover codes, of which we used levels I and II. All of my polygons were within a single feature class. I used the snapping tool to align my polygons with each other. After I finished creating all of my polygons, I used the symbology tool to change the color of each code.  We were also asked to place 30 random points to conduct determine ground-truthing accuracy. To determine this, I created a new feature class with a single polygon over the entire subject area. Then I used the Create random points tool to create 30 random points within the project area. I used Google maps to determine whether or not the code for each point was accurate. Each point was marked as accurate or inaccurate. The codes on my map were 97% accurate. 

I began by uploading the tif file for this map and removed the base map. I created a feature class with the create feature class tool. The first feature class was titled “Tone,” which identified the various tones (very light, light, medium, dark, very dark) of the tif file. This feature class has a polygon geometry, which I used to outline the various toned areas. I created a new field in the attribute table titled “name” in which I labeled the tones. I then labeled each polygon and adjusted the font, size, and color. I also adjusted the symbology of the polygons. I then repeated this process with a feature class for “texture,” which included very fine, fine, mottled, coarse, and very coarse. I outlined the texture areas, added the attribute tables data, labeling, and symbology. I used this map to create a layout with a title, a legend, and credits.     For this map, I began by opening a new map and added the Tif file. I also removed the basemap from my map. Next, I used the c...

This week I was tasked with conducting a damage assessment to an area in New Jersey after Hurricane Sandy hit. Below is the result of my analysis. The points are symbolized with a color ramp, with Green representing No Damage and red representing Destroyed.       The assignment called for an analysis of the number of points within 100, 200, and 300 meters of the coastline. In order to conduct this analysis, I first created a coastline feature. After setting up the coastline feature, I then created three buffers for 100, 200, and 300 meters, all on the right side of the line. From there I simply manually counted how many of each category fell into the buffer layers. I did use the “Select layer by attribute” tool to help highlight the different categories.      I found that every building within 100 meters of the coastline was destroyed or received major damage. The parcels that were unaffected were parking lots and did not have structures. This was the ...