LIDAR!

Image Classification

Supervised

Unsupervised

Image Rectification

Image Rectification allows an image to be shown in X Y coordinates and matched to a specific location on a map. It is important when rectifying an image that an analyst use as many GCP points as neccessary to produce a low RMS error, if not enough GCP points are used and the RMS error is high, this will most likely result in image distortion and incorrect placement of features.

Thermal Imagery

In this image, the asphalt roads, sidewalks, and patios absorb heat throughout the day and continue to radiate heat into the evening and morning hours when the surrounding areas cool off, therefore they appear lighter in color. Vegetation appears dark because it cools off in the evening hours. Storage sheds, and cars appear darker due to the fact that they are made of materials that cool down. With the exception of cars with a lighter spot in the front, this occurs due to the heat of the engine under the hood. Bright spots that appear on the rooftops of homes can be caused by possible heating units for the home, or steam vents on the roof.

Multispectral vs Panchromatic

Some of the differences I can see between the panchromatic and multispectral images is the difference in resolution, the panchromatic image has a higher resolution and the image is also a lot clearer, making it easier to make out specific features. Another obvious difference is the color, the panchromatic image is in grey scale while the multispectral shows 3 different color bands. The high amounts of red coloring in the multispectral image shows that there is a high concentration of vegetation along the coastal areas, and also in the water.

Natural vs Color Infrared imagery

CIR imagery can be difficult to interpret mainly because the colors are unnatural to us relative to what we see normally. In some cases it is exactly opposite of what we are used to, and this makes typical areas more difficult to identify. But there is also an advantage to CIR imagery, it allows us to identify features we could not normally see with the naked eye. This is advantageous to identifying vegetation and also in determining the health of vegetation.

Google map of potential wind farm

This location approximately 10 miles off the coast of Muskegon, near Snug Harbor and Muskegon Lake, would be a good spot for an offshore wind farm. This area receives a daily average wind speed of 11.5 miles or 10 knots, making it a pretty good choice. Due to the fact that this would be an offshore wind farm, noise and shadow flicker would have little to no impact on local residents. This site would only affect local boaters and smaller vessels, since the east coast of Lake Michigan does not contain any major shipping routes. Migratory birds will most likely not be an issue, while they are common to this area, there does not appear to be any major migratory routes near the east coast of Lake Michigan. According to The American Wind Energy Association, http://www.awea.org/pubs/factsheets.html Avian studies have been carried out at many wind farm sites, they show that bird kills per megawatt (MW), average one to six per year or less. These studies included sites passed by millions of migrating birds each year.

Isohyet Map

Flow Map

Dot Density Map

Cartogram

Proportional circle map

I chose Europe Albers Equal Area Conic for my projection. I used a 65% opacity, but chose not to use that in my legend, I didn't like the look of the opacity in the legend but felt I needed it in order to see the country outlines and see the names. I hope that is not one of the 'important things not to do on your map'! I chose to make my legend linear with a horizontal orientation, and went from smaller to larger symbols because to me, it is the most logical way to read the legend.

Percentage change by Division

Again with this map, I feel there are obvious holes in the map space and I feel a horizontal legend would've been better suited and given the map a more even flow. Something to remember for next lab I guess!

Percentage change by state

After trying several options, I chose to go with North American Albers Equal Area Conic for my projection. I felt since we were looking at Alaska, along with the contiguous states, a N. American projection would be better suited than a Canadian or Alaska projection. I also wanted to preserve area, so the other N. American projections did not seem like good choices to me. I decided to modify my natural breaks manually to make them more equal and I also liked the idea of making them whole numbers, I felt it made them easier to read. Looking back now, I wish I had used a horizontal legend, there are some open spaces on my map that I'm unhappy with, and I feel like I could've centered the map elements better with a different legend layout. Unfortunately time just doesn't permit. I have learned a lot in Illustrator so far and I like the effects that it allows you to add to your map features.

Map Lettering Lab

Map Compostition Lab

Data Classification

I chose equal interval as the best choice for this lab, because the breakdown in percentages is more evenly divided.

Bad map

Okay, so this map was just plain funny to me when I saw it! According to the title (which isn't shown here) it is supposed to show all category 1 to 5 hurricanes whose centers have passed within 10 nautical miles of the state of Florida's boundary during the period of 1851 to 2005, but good luck making any sense out of it. First, while the map does appear to achieve it's goal to show storms within the 10 nautical miles of FL's boundary, although we cannot be sure without something to identify scale and also better use of symbology to show said 10 nautical mile area, the only real label anywhere on the map is for the entire state of FL. So if you were interested in how close any of these storms were in relation to say, the Tampa Bay area, if you were unfamiliar with FL, you would have no idea where to look. Second, while it is somewhat easy to distinguish what each line represents in the legend, it is much more difficult to determine this on the actual map due to the fact that the lines intersect and overlap so much. Also, one thing important to note is the fact that the title fails to mention that this map is not only tracking category 1-5 hurricanes, but it is also tracking other tropical and subtropical weather phenomena such as, depressions, waves, and storms.

One possible better way to represent this data, would be to break it down into 10, or possibly even 25 year intervals. Then break that down further into each tropical storm/hurricane category using a variation of different line colors and symbols. It would also be helpful to add labels for major populated areas in the state of FL, to better determine the location of these storms.

Good map

Even though there is no title shown for this map, I would still consider this a good map. It represents the residential average price for electric power in kilowatts, which the legend clearly states. The color scheme is easily readable and each state is clearly labeled with the state abbreviation and kilowatt average.

mental map

Well, I currently work and live in Florida, so that's my number one choice, but I am originally from Illinois, so that's makes the list of obvious first choices. I worked in CA for a couple of weeks and have also travelled there and love it, same with VA, NC and the DC area, so that's makes them first choices also. I would also consider Hawaii and Alaska as first choices. My second choices are places that I have never been, with the exception of MA, and I just think they would be really cool places to live and work. I made the rest of the country a third choice because I would love to travel to all the states and would consider working in any of them even if just as a temporary job.

Hi everyone! I'm Georgina and I've been working in the GIS field for a while now without formal training. I'm hoping to learn a lot from this class!