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.  

Multispectral Analysis

This week we delved into multispectral analysis primarily focused on using ERDAS Imagine.  We looked at histograms of both greyscale and multispectral images and then used a breakpoint feature to isolate pixels in certain ranges to help identify what features we were looking at.  For the final exercise we were to determine three different features using the skills we learned and then create maps for each feature.  These maps are below along with a description of what I was trying to highlight. 

Feature 1: Water in general.  By adjusting the breakpoints on the greyscale layers, I was able to isolate the target peak on the histogram and verify that it matched up to a single feature.  Map is displayed in TM False Color IR.

Feature 2:  This feature are the snowy areas on the mountain peak.  Again, by adjusting the breakpoints on the greyscale layers, it was easy to see that the snow was the target.  This was especially true for layers 1-4.  However layers 5-6 also included the water features along with the snow.  So therefore, the snow has to be the feature as it was the only one that was visible on all 6 layers.  I displayed the map for this feature in TM False Natural Color as it distinguished between water (dark blues/blacks) and the snow (light/pale blue)

Feature 3: For this I chose the river mouth that feeds into the bay area.  Again I used breakpoints on greyscale.  For layers 1-3, and even 4, the river discharge is distinctly different than that of the water in the bay.  For layers 5-6, you cannot see any different between the discharge and the body of water.  For this map I used an inverted TM False Natural Color.  R:3, G:4, B:5.  This made a distinct looking map and highlighted the discharge in red as it entered the dark blue body of water.   

Spatial Enhancement Techniques

This week's lab focused on applying different filtering techniques to images to achieve certain effects.  Both ERDAS Imagine and ArcGIS were used to achieve these effects.  Some filters would add some blur to the image while others would sharpen edges and pixels.

My map this week applied a few different filters that were aimed at minimizing the striping effect on the base image while preserving as much detail as possible.  My map below illustrates my process of using a Fourier Transformation filter followed by using spatial convolution filters.  The convolution filters were applied in the following order: Sharpen 3x3, High 3x3, and then Low 3x3.  The result can be seen below.

Striping could not be fully eliminated, however I was able to get it to better blend with the medium greys in the image.  I was also able to maintain decent detail in the urban areas as streets and city blocks can still be easily distinguished.  

Projections 2

This week has probably been one of the more frustrating thus far for the course.  However out of frustration comes learning through repetition as you have to go back and redo your work over and over again...lol

So working more with projections and defining projections for map files that are undefined, there is a key order for doing these actions or, as I learned, you mess things up.  My biggest mess up was importing the X/Y data for the STCM information.  I went straight to defining the information as the NAD 1983 (2011) projection.  Problem was is that this placed all the points off the coast of Louisiana and you had to set the scale to 1:1.5 to see all the points.  The key step I had missed was defining the STCM data in WGS 1984 and then projecting to the NAD 1983 (2011).  Doing this fixed the problem and I achieved the result below.

Next week...start working on the dreaded midterm lab...but Im ready for it...so I think.  

Map Projections

The most interesting thing I learned this week with the lab was how much a map differs based on the projection the data is based off of.  While sometimes the difference is faint (visually), there are times that certain projections will cause a significant distortion on the map you are working with.  

So this week we focused on the map projections: Albers, UTM, and State Plane N.  While it is difficult to visualize the differences in the map below, the data that is presented in the table shows how much the square mileage of select counties differs across the three projections.  See for yourself below.  If you have any suggestions on how to make this better, feel free to comment below.  

ERDAS Imagine and Importing Imagine Data Into ArcGIS

This week was a fun introduction into ERDAS Imagine and illustrating what it can do.  We saw the difference in what AVHRR and TM files can show us.  Also with the TM photos, we were able to manipulate the vision spectrum to help identify different land features. 

After working through some of Imagine's features, we created a subset of a TM map and used that within ArcGIS to develop a map which showed how much area in hectares of each land type feature that were highlighted on the map.  One issues I was not able to overcome was reordering the legend on the map.  Personally I would have preferred sorting the legend by most to least hectares.  Definitely a challenge I will need to figure out down the road.  The picture below shows my map from ArcGIS.