Lab 12 - Georeferencing and Life Lessons in Frustration

Honest assessment for this lab.

Life, end of semester, and other factors have forced me to complete this lab a week late.  It is what it is and I take full responsibility.  All in all the georeferencing and use of ArcScene was pretty easy to get a grasp of.  Sometimes getting the right links to transform your images can be tedious, but it isnt hard.  It's actually pretty neat how you can get a polygon layer to line up to the raster using this tool.  As you will see in my first map, everything turned out great (or so I think).  However my frustrations and lack of available time to redo pretty much the georeferencing of the south raster has driven me mad.  Here is what happened.  I completed my first map.  Saved my working file and created a final file as well and then closed ArcMap.  I opened up ArcScene and added the necessary files to the layer.  However my south campus raster was nowhere to be found.  It lost my transformation data.  I opened ArcMap back up and sure enough, the south map information was lost.  Due to my time crunch, I then just took the snapshot from ArcScene and you will see how that at least turned out.  Again, I take responsibility for it, but sometimes redoing the entire project isnt worth the frustrations it causes.  So, enough ranting, here are my maps for the week:

Geocoding

This week saw some delays in me completing the lab, but hey, I got it done.  This one was pretty neat in that I got to act a bit like GoogleMaps in creating at routing network for selected EMS Stations.  First we created the network nodes for all the EMS Stations in Lake County.  Then I was able to select a few stations and an additional point to create an optimal route from.  Below is the output.  Enjoy!

Supervised Classification

This lab was actually a fun one.  I enjoyed creating the different feature signatures and determining what was the best way to bring them all together.  So the final exercise that you will see below was a supervised classification of Germantown, MD and its surrounding area.  Many features were created and in the end all similar features were combined into 8 total classes.  The map was then created from this and included a distance image as well.  Suggestions are always welcome for my color schema, with names preferably as I can't always distinguish by color alone. 

Vector Analysis 2 - Finding a Place to Camp

Good evening readers!  This weeks lab focused on creating vector buffers using a couple of tools within ArcGIS.  We even got to learn a very small bit of Python using the AcrPy toolset.  First we started off conducting a buffer analysis on a couple different layers, those being roads and water features.  We did this using the buffer tool in ArcGIS.  After we did this we got an additional lesson on how using Python can help expedite these types of functions when you have to repetitively do the same thing over and over and only one variable changes...HUGE time saver for sure. 

Next we learned how to combine our different buffer outputs using the overlay functions. Primarily we focused on using the union overlay to join our water and road buffers, but guess what, the intersect overlay achieved the same results.  Then we learned how the erase overlay can help delete some features as well.  When all was said and done, I had enough data to develop the map below.  So if you find yourself looking for a camping spot in the Desoto Forest in Mississippi, this just might help!

Unsupervised Classification of the UWF Campus

This week we looked at using tools in both ArcMap and ERDAS Imagine that help conduct unsupervised classifications.  The map below was initially created in ERDAS using the Unsupervised Classification tool and having it assign a pixel value to one of 50 classes.  From there I reclassed the 50 pixel classes down to 5 color classes.  The resulting image was imported into ArcMap and the results are below.  Another element of this map was to determine how much of the land on UWF was water permeable based on the determined land classes.  So here it is:

Thermal & Multispectral Analysis

This week focused on using two different image spectrum to analyze and determine what features we are looking at.  Sometimes you cant determine what something is in one spectrum, so it is helpful to look at it through different lenses to really see what is what.  One example in the lab this week was finding a bright white spot in a thermal image that is surrounded by dull greys and blacks.  Through thermal imaging alone, you cant really tell what it is.  You just know that whatever it is, it's emitting a lot of energy.  By looking at the same point through a multispectral lens, you can then see the smoke coming off the ground and therefore infer that you are looking at fires of some sort.  Just one example of how different spectral bands can help paint a full picture for analysis.  

So for this weeks project I chose to use Spencer Field, a Naval airfield that is just north of Pace.  I chose to show a comparison between the thermal and multispectral images to show just how different things look, but how they also stand out in their own rights.  The full description is in the map below.  Enjoy!!

Data Search Lab

You have to love it when you start to get to a point in your map making process where you end up spending 4-5 times more effort and time finding the data you need than you do making the actual maps.  That was the case for me this week.  Some issues encountered were: simple trial and error to see if the data displayed was what I wanted, inability to find exactly what I wanted, and overall just getting more familiar with what I can find from certain sources.  Once I obtained all of my data, I made the following three maps.  I think the way I grouped the data in each map makes sense.  Feel free to comment below with any suggestions.

Map displays Hillsborough County and tries to highlight a section of Tampa

This one combines elevations and rivers along with wetlands and parks.  I thought these pairings were interesting as the elevation shows were rivers and streams are and the river vectors confirmed this.  I also found it interesting how the state parks in the county were centered around some of the larger wetland concentrations.  

This map simply shows where certain invasive plant species are found in the county.  Unfortunately due to the large number of different species available, they aren't easily distinguished on this map.  The data would be going for highlighting certain plant species if you knew what you wanted to show.