Remote Sensing Module: 6

Remote Sensing Module 5:

I sometimes have a hard time differentiating one land type from another. In this case grass/agriculture land covers gave me a lot of trouble. In the Unsupervised map I took that cluster of fairly regular shapes in the north west portion to be an area of High Urban Density. I didn't even include a land cover category for agriculture because I didn't think any sizable farm land existed on the image. Part of my issue was not paying strict attention to the area I was viewing and making assumptions about the image based on past experience. I thought the main body of water running through the center of the map was Escambia Bay, hence I assumed that area in the North West portion to be Pensacola. Nope. That central water body in the image is Perdido Bay making that North West portion of the image a sparsely developed section of Alabama. Upon further review, I took those North West sections to be tracts of farm land. I added the Agriculture land cover category for the Supervised image. I still ended up with a confusing mish mash of land cover types in portions of both maps and agreement between land type categories between each map is sketchy.

Remote Sensing Module 4:

Usefulness/Pitfalls of Image Rectification: Once an image has been rectified with a reference, locating points of interest become much easier. In the Pensacola image (above at left) it could be difficult to identify specific buildings, a school for instance. But, after rectifying, the coordinates of a particular school could be taken from the reference map (above at right) and the school could then be pinpointed on the image. A particular issue I had with rectification arose after I placed the first check point. The next one would vanish. I assumed my control points were bad, so I started over with a new set. I ran into the same issue: vanishing check points. I tried them in several places and noticed a distinct pause before rejecting the potential check point. I tried several more until the next one found a permenant home. It required quite a bit of trial and error with check point placement until I had the requisite five sited. My final check point error was 2.27.

Remote Sensing Module 3:

Roads: The roads are bright because the asphalt paved roads absorb more heat and remain warmer overnight. In addition, the streets have already begun to absorb heat from the early morning (6:45 am) sun.

Natural and man-made vegetation: Overnight cooling of vegetation combined with its higher thermal inertia results in the vegetation being cooler, i.e., darker, in the early morning as it requires a longer period of sun exposure to heat.

Sidewalks and patios: The lower thermal inertia of the concrete allows it to more rapidly heat in the morning light. If the sidewalks and patios are made of certain materials, overnight heat retention may play a role in its brighter appearence, much like the asphalt roads.

Storage sheds in back yards: The sheds appear uniformly dark (cooler), much like the houses. The material from which the sheds were built, wood for instance, would have a higher thermal inertia requiring longer sun exposure for warming. Possibly the materials stored in the sheds may contribute to their cooler thermal appearence. A quantity of stored water or building material within a shed could impact the image. The constant shade provided by the shed would lower the temperature variations the material within experienced thereby increasing solar heating times.

Automobiles: The cars that have been sitting all night appear cooler as the early morning sun has not begun heating them yet. Though, cars that are idling or have recently been operated show hot spots from the heat of the internal combustion engines.

Bright spots on many of the roof tops: Ground Temperature is in the lower fifties (Fahrenheit), so the hot spots are either furnace vents from heating units or chimnies above active fireplaces.

Remote Sensing Module 2:

The bright blue areas in the multi-spectral image are very hard to recognize in the panchromatic image. When viewed as a color infrared composite the areas stand out in bright red suggesting it must be some type of vegetation. Given this and the fact that the blue colored areas are located in the water, I would guess that they represent a algae bloom, a very dense concentration of seaweed, or some other type of sea plant.

Remote Sensing Module 1:

What problems might you infer or identify in using this type of photograph?

Plants and foliage showing in red tones in not at all intuitive and could cause misinterpretation of the photo. Visually, the dark contrast between the bright beach and the ocean gives the impression of rapidly deepening water, which isn't at all accurate to the area. Seeing a familiar area from the overhead vantage offered by an aerial photo may cause problems as the bird's eye view of a location looks quite different from our regular line of sight experience of it. Also, at a certain height, details become too small leaving a lot of different areas looking much like any other in the absence of large features like mountains, lakes, rivers, and seas. Without a stereoscopic image the photo lacks the dimension of depth, making it impossible to get a sense of the relative difference in elevation between different locations.

Module 11: Google Earth

Average wind speed at Station MKGM4 at Muskegon, MI (to the northeast of site) is 11 knots (12.7 mph)with gusts of 15 knots (17.3 mph). It's beyond the BERR recommended 200 to 300 meter distance fromdwellings to minimize noise pollution. It is also beyond the BERR recommended 400 to 800 meter distance to avoid adverse reactions to Shadow Flicker. Major shipping routes out of Chicago and Milwaukee are to the southwestand west, respectively. The site is a good compromise with regards to major bird migration corridors,and should be minimally impactful to most species. Its proximity to land should help minimize buildingand transportation costs.