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.