Georeferencing the UWF Campus

     As with each of the labs in this course, there were multiple different exercises to undertake. They all centered around the UWF main campus outside Pensacola Florida. While previous labs worked toward building data layers, this one focused on taking unreferenced or unregistered raster (imagery) layers and applying point features that could give specific spatial reference for the image. For example, we had a known buildings and roads feature set which were used as the reference layers. And then we took satellite imagery of UWF, and established control points to link the imagery to known features. 

Once the imagery was registered to the reference layer with the control points, new features needed to be added or digitized to the buildings and roads layers. The main frame on the left side of the image below illustrates the combination of two satellite images, which were registered through two different transformation methods, then building 72 and Campus Lane were added and specifically highlighted. Additionally, a significant feature adjacent to the campus is a local Eagle nest. Buffer easements have been applied to that location as well. 

Overall there is a significant amount of information in this map, but hopefully, it can all be understood. Additionally, for a look at the eagle nest itself, you can reference this link: UWF Eagle Nest

Taking the georectified imagery a step further, we wanted to add elevation data to be able to do some 3D modeling of the campus region.  Light Detection and Ranging, or LiDAR, is a way in which to generate elevation data for a particular area by using emitted radiation from a laser to catalog and organize received ranging information to generate a 3D surface based on processed ranging data. In this case, the data was supplied by the National Map Viewer and clipped to our scene area for us. With it though, I created a Digital Elevation Model, and that underlays the imagery below. The image has been rotated to provide enhanced perspective but is otherwise much the same as the above, just laid on top of a new surface which highlights the changes in elevation. 

Georectifying imagery seems to be the more common place of the two tasks. But they were absolutely extensions of each other, with multiple lessons learned. Ultimately the accuracy of the process is based off of how good the technician can enter or associate points. Though there are numerous transformation options depending on the distortion of the imagery being used. 

This was an excellent exercise, which appropriately started bogging down ArcGIS Pro from all of the layers and shifts, and display features presented. Thank you. 

v/r

Brandon