Lab 6: Terrain Analysis

Deliverable 3: This map shows volume of sand change (in detail) by using both pre and post DEMS. The pre_Hurricane_DEM is subtracted from the post-Hurricane_DEM using the Spatial Analyst raster calculator to see the volume change. This is more detailed then the Cut/Fill map shown in deliverable 2.

Deliverable 2: This is post hurricane Ivan DEM for the study area showing the volume of sand eroded during the storm. The Cut/Fill tool from surface analysis in 3D Analyst tools is used for the volume change calculations. Red areas showing the gain and blue areas indicating the loss. The total volume change for the entire study area is 25467.189874 cubic meters.

Deliverable 1: It is pre Hurricane Ivan LIDAR image, covering University of West Florida property and a portion of Gulf National Shores area . The total amount of sand above the '0' plane in the study area is 1717118.08 cubic meters.

Module 5: Supervised/Unsupervised Classification

Comparison of recoded image (supervised classification) with original image of Pensacola, FL. The original image had 15 classes and it was recoded to 7 classes based on the landcover/landtype after supervised classification. All the classes are mentioned in the legend. Supervised classification is based on the knowledge, familarity with the place and pattern recogination skills which results in accurate selection of the training samples. But it was difficult to select the some landtypes especially, grass type which resulted in some errors.

This is an unsupervised classification which is more computer automated where software identifies the statistical patterns in the data without using any ground truth data. Here also, the recoded classes are 7, mentioned in the legend.

Overall, unsuperived classification resulted in better output image than supervised classification. This is because of the reason that it was difficult to identify some classes (grass, forest) in the original image and therefore more errors in the supervised classified image.

Module 4:Geometric Correction: Image to Map Rectification

Usefulness of Rectification process: Image rectification is a preprocessing operation that intent to correct the distorted or degraded image data hence creating a more reliable representation of the original scene. It involves geo referencing. The random distortions and residual unknown systematic distortions are corrected by analyzing the Ground Control Points (GCP) in an image and then accurately locating the known ground locations in the map. The image with unknown coordinate system can be projected into a known coordinate system. The transformation equations involved, inter-relate the geometrically correct coordinates and distorted image coordinates. Hence once this process is done, the distorted image coordinates for any map position can be precisely estimated.
Pitfalls in Rectification process: Locating GCPs identifiable in both the images and pinpointing the exact location can be difficult sometimes if the image is not very clear, making the entire process of rectification challenging and difficult.
For the lab in particular, it was somehow difficult to place the checkpoints initially, error was quite high but finally I got to place the check points and got an error of 3.1.

Module3: Thermal Infrared Image Interpretation

Roads: The roads in the picture look very bright. The warmer objects tend to appear brighter than the cooler objects in the aerial images. The roads being asphalt paved absorb more heat and remain warmer in the night compared to other objects which are cooler because there is no heat source (Sun) at that hour. It is dawn image (taken at 6:45 am) just around the sun rise time and hence cooler temperature. Roads are still warm that time because of its absorption characteristics mentioned above and hence appear bright.

Natural and man-made vegetation: The vegetation appears to be of different tones which depend highly on the moisture content of the soil and the leaves of the trees. Moisture in the moist soil keeps them cooler than the drier soil and hence some darker patterns whereas the moisture in the leaves retains heat for longer duration and being warmer it has lighter tone at some places. One can see the variations in the shades in vegetation.

Sidewalks & Patio: They appear of lighter tone. The main reason could be the material of which they are made. The material seems to absorb heat and hence remain warmer and therefore appears of the lighter tone.

Storage Sheds in backyards: They appear darker in the aerial image since at night/dawn there is no heat source and hence cooler temperature. Also, the material of which they are made does not seems to retain heat and hence are cold. Therefore they look darker.

Automobiles: Most of the automobiles (cars) appear dark in the image since there is no heat source (sun) at night and at dawn, the temperature just starts to warm up because of the sunrise but not yet heated up. Hence the cars are cold and appear darker. Although in some cars you can see hot spots which could be due to the heated engine (car may be running and just parked) so the front engine is heated and appears brighter even at the dawn/night.

Bright spots on many of the roofs: There are bright spots of many roofs explaining that there is higher radiant temperature over there than the surroundings. There is probably a heat source, it could be a heating system’s vent, a chimney, which is keeping the temperature warmer out there and looks brighter in the aerial image.

SPOT Analysis

The resolution for the panchromatic image looks much higher. The picture looks much clear as compared to the Multi spectral image. But at the same time it is easier to distinguish between the vegetation (red), built up areas and water (blue) in MSS which gives different color to each. The pan image is black and white and hence difficult to identify.

Exercise II: What can you see?

What problems you infer or identify in using type of Picture (attachment 2)?
The vegetation is in shades of red which makes it difficult to identify specific details or type of vegetation. It is difficult to pin point which is forest cover, grass land etc. The blue shades mostly look like built up area but to identify types of buildings etc is not easy. The major intersections and roads can be seen easily but smaller lanes and details are not easily noticeable.

Contour Map showing Mean Annual Preciptation

I used manual interpolation method for contour map. The minimum value of 45" and maximum value of 70" is used. The isohyet interval is 5 inches. The width of contour lines is 2 points.

Wind Farm Location in Great Lakes

The potential off shore wind farm location is near "Water intake crib" which is around 3-4 miles off from the NW of the downtown Cleveland,OH.
Reasons for selecting this location:
1. There is estimated wind power of class 3 to class 5 in offshore areas of Lake Erie. Prevailing northerly and westerly winds have a long, smooth fetch across Lake Erie which results in strong winter and spring winds, especially along the coastal areas [1].
2. Already a pilot project is being done in the selected location.
3. BERR criterias: a) The average winds on the lake Erie are the highest recorded in the state. The average wind speed in the location is 16.4 mph which is excellent for power generation(utility scale wind farms require average of 15.7 mph). Prevailing winds are Southwest [2].
b) There could be impact on aquatic organisms, fishes due to placing the wind turbines , electrical cables. But this would not be a long term problem according to the study by DLZ Ohio Inc., a subcontractor to JW Great Lakes .
c) The nearest dwelling is around 12 miles from the location which satisfies the noise and shadow flicker problem criteria in BERR. According to the BERR criteria for noise, if the turbine to receptor is between 200 & 300 m, then noise power level is less than 50dBA. Also, source noise level of wind turbine is between 100-106dBA[3]. As regards to the nearest port "Cleveland" is nearly 16 miles from the location.
d) It being a off shore site, landscape visual impact would not be a reason for concern.
Sources:
[1] http://rredc.nrel.gov/wind/pubs/atlas/chp3.html#lake
[2](http://blog.cleveland.com/pdgraphics/2008/09/large_01FGWIND.jpg)
[3] http://www.berr.gov.uk/energy/sources/renewables/planning/onshore-wind/noise/page18728.html

FlowMap

I used maximum line width of 40 points. I did not put the heading for the legend because of space constraint.

CARTOGRAM

Contiguous Cartogram, Robinson Projection; 10 Iterations

Non-Contiguous, Feature shape Cartogram

Non Contiguous Circular Cartogram (Most appropriate)

I feel Non Contiguous Circular Cartogram is most appropriate for showing the World's GDP. One can easily figure out the countries GDP from the changing size of circles. I have added both Feature shape and circular types of Noncontiguous cartogram just for comparison.

Dot Distribution Map

Module 6 Lab: Proportionate Symbol Mapping

Choropleth Map (Diverging color scheme)

Projection Selection: I selected "Albers Equal Area Conic Projection" as I feel that this projection displays the areas accurately and is best for respresenting the east -west extent of US.

Color Scheme: It is a Choropleth map. Data provided is quantitative in nature. I have considered population change as a bipolar in nature since it has negative values indicating population losses in some areas and also positive values in other areas showing population gains. There are areas where there is very negligible or no population change considering it as a dividing point shown by lighter (neutral) color in the middle. So being bipolar in nature I felt that diverging color scheme would be appropriate for this map representation.

Choropleth Map (Gray scale)

For the Choropleth map shown in the left, Gray scale color scheme is used. Also, as mentioned in the lab directions, 5 equal interval classes are considered for this map, where class interval for each class is determined by dividing the range of the data by the number of classes (here it is 5) and upper limit is computed by adding class interval to the lowest value in the data. Class limits are then specified (shown in the legend).

Module 4: Map Composition & Organization

Module 4 : Typography Lab

Module 3: Data Classification

Best Classification

In my opinion, the Natural Break Classification best represents the data of percentage of black or African population in Escambia County, Florida. It presents the most realistic view of the data. On this map the extreme values, the highest and lowest are classified in 2 different classes by itself. And rest intermediate classes are divided into logical groups subjectively. The map presents as one expects it should look.

Map Critique ( Bad & Good Map)

The map in the left (Map1) is the example of a bad map. It is suppose to be a Florida location map but no one would ever reach to any destination following this map. There is no scale or any orientation indicated. Also, no legend explaining the symbols, colors and features used on the map. The map needs to be more clear and little more detailed to be understandable. I feel map is worthless if one is not able to interpret from it.

Map1: Example of a Bad Map

The Florida map on the right (Map2) is the example of a good map. The map is showing the populated areas in Florida. The map very clearly indicates the populated area with different sizes and types of symbols. Also, map shows interstate and all the major highways in the state with accuracy. A proper scale bar is indicated on the map. The legend in the map provides the great detail for anybody to understand the map easily.

Map 2: Example of a good map

Mental Mapping Exercise

I would prefer Florida as the 1st choice of my work place as I am already well settled here. Moreover, my area of interest is in "Water Resources" and FL would be an excellent choice from this perspective.

If I have to relocate from FL then I would prefer either east coast or IL because I have some of my family in these areas. The third preference would be California and Texas as they also offer many environmental related job opportunities.