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Remote sensing

             Glenmore reservoir (Vegetation, water quality).
             Define the problem: To examine the water level of reservoir and quality.
             Data Available: Landsat TM1 - TM4 50m x 50m georeferenced.
             Method: Looking at the red band only(calgary3), and knowing that water absorbs red wavelengths with depth, we took the information from a histogram of the red band. There is a peak in the histogram right around zero and then a valley until about 10. We decided that looking at a range between 0 and 10 would give the best visual representation of the water depth in the Glenmore Reservoir and the surrounding area. Areas of black are deep water, graduating through to lighter shades for shallow water. The dark green areas are areas where there is no water.
             Results: The incoming sun radiation and sky irradiance, on a body of water will either be reflected at the surface, transmitted through the surface and then reflected by the water body, or transmitted through the water body and reflected from the bottom. During the transmission the irradiance can be reflected by the water body. Thus when the photo is interpreted and shows little to no light for most of the water body, this implies that it is deep water. The more shallow regions to the east of the water body and in the perimeter of the water body show a higher level of reflectivity.
             Conclusion: Through the use of the histogram and incorporating the reflectivity properties of water and the red band, we were capable to determine the general depth of the water level, shallow to the west and deepest in the main areas.
             Street System (Filters/color composites).
             Define the problem: The street system is not that clear in the images. We"re going to use different spatial filters and color composites to determine the best tool to enhance the street system.
             Data Available: (Landsat) TM1 TM2 TM3 TM4 50m x 50m Georeferenced.
             Method: By using different color composites and spatial filters, we determined that the best methods for observing the street system were the spatial filters.

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