Nathan Berger Portfolio: Geographic Information Systems

  LULC Classification and Ground Truthing In this exercise, we are using imagery located at Pascagoula, MS, to digitize the land cover surrounding the area and classify it by land use and description.  In order to do this, I decided to use a land cover legend to digitize the different land types using a polygon feature class. The different land types were given different colors to differentiate the symbology of the land cover.  I then used 30 sample points called "truthing points" to test the accuracy of my land cover results. I simply dropped 30 points on my image and then exported them to google earth using a kmz tool. I then zoomed in on each sample point and viewed the google Earth Image in 3D to see fi my land cover was classified accurately or not. I then coded my results in my truthing feature class to identify which points were accurate and wh9icvh ones were not.  Close Up View Of one Sample Point The final map below shows the sample points in accuracy in comparison t

 Aerial Photography and Identifying Features Texture, Tone In any remote sensing analysis, we need to identify features in imagery. In order to do just that, we use a few tools and tricks to help us along the way. The tone and texture of objects in an image help us to identify different areas based on light vs. dark, and even fine objects vs. coarse or choppy. This allows us to make out areas of dense vegetation, bodies of smooth or still water, and manmade structures such as runways of an airfield.  Size, Shape, Patterns, Association, Shadows The size and shape of objects help us to identify structures, such as a house or building. We can indeitfy manmade structures such as the pier in the image below due to its rectangular shape and length, but thin width running out into the water.  Take a look at the images below True Color                                                                                  False Color  Infrared The land features on the left are colored by their true c

 Special Topics In GIS Today we are going to talk about 3 topics in GIS. One is vector data and its scalability. The second topic is going to be raster data and resolution. And the third topic is going to be a special topic in regards to elections... aka gerrymandering.  Vector Data and scaling As we already know, Vector data is made up of points, lines and polygons. Vector data is directly affected by the scale used to map the vector data. The scale is important because in order to "digitize" the data on a map, we want the data scaled appropriately. This can cause boundaries of lakes and rivers to not be accurate, or even the location of hospitals or residential homes. Maps have different scales in which the data is displayed, so we want to ensure the spatial accuracy of that data when projecting it on a map.  Below is an example of mapping waterways (rivers) of an area at three different scales. The red is at a 1,200 scale, the blue is at a 100,000 scale, and the yellow is