Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208

1. Initial Display Alternatives and Scientific Visualization Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208 Jensen, 2003

2. Scientific Visualization Jensen, 2003

3. Input and Output Relationship Jensen, 2003

4. Digitized Panchromatic Aerial Photograph of an Area in Charleston, SC Jensen, 2003

5. Displaying Remotely Sensed Data Jensen, 2003

6. Various Class Intervals Used with Line Printer Brightness Maps Natural Breaks Equal Size Equal Area Jensen, 2003

7. Symbolization and Perceived Grayness of Line Printer Symbolization Based on Transmission Densitometer Measurements Jensen, 2003

8. Perception of the Range of Overprinted Symbols Produced Using a Line Printer Perceived Grayness Jensen, 2003

9. Crossed-line Shading Jensen, 2003

10. Crossed-line Shading Jensen, 2003

11. RGB Color Coordinate System Jensen, 2003

12. 8-bit Digital Image Processing System An 8-bit video image display system consists of several different components: The computer's central processing unit (CPU) accesses the remotely sensed data from a mass storage device such as a hard disk, CD, or DVD and transfers the bytes of information to the image processor display memory (i.e., a graphics card). The image processor display memory typically consists of > 64 megabytes of RAM. Each brightness value (BVi,j,k) at row (i) and column (j) of a single band (k) of imagery is stored in the display memory. Each line of data stored in the display memory is scanned every 1/30 second by a read mechanism. This is faster than the human eye can detect, therefore we do not perceive what is taking place. The brightness values encountered are passed through a color look-up table that is read by a digital-to-analog (DAC) converter. The red, green, and blue (RGB) analog output from the DAC is used to stimulate the RGB phospors at each pixel location on the video monitor. Jensen, 2003

13. 8-bit Digital Image Processing System R G B Jensen, 2003

14. Color Density Slice of the Thematic Mapper Band 4 Charleston, SC Image Jensen, 2003

15. 8-bit Digital Image Processing System Jensen, 2003

16. Class Intervals and Color Lookup Table Values for Color Density Slicing the Thematic Mapper Band 4 Charleston, SC Image Jensen, 2003

17. Color Density Slice of the Thematic Mapper Band 4 Charleston, SC Image Jensen, 2003

19. Color Density Slice of the Thermal Infrared Image of the Savannah River Jensen, 2003

20. Class Intervals and Color Lookup Table Values for Color Density Slicing the Pre-dawn Thermal Infrared Image of the Savannah River Jensen, 2003

21. Color Density Slice of the Thermal Infrared Image of the Savannah River Jensen, 2003

22. Jensen, 2003

23. 24-bit Digital Image Processing System Jensen, 2003

24. 24-bit Digital Image Processing System Jensen, 2003

25. Color Composite of Marco Island, Florida SPOT Imagery October 11, 1988 Created using the band substitution method: R = SPOT band 3 (NIR) 20 m G = SPOT band 4 (Pan) 10 m B = SPOT band 1 (Green) 20 m Jensen, 2003

26. Optimum Index Factor Ranks the 20 three-band combinations that can be made from six bands of Landsat TM data (not including the thermal-infrared band). Band combination: 1,2,3 1,2,4 1,2,5 1,2,6 2,3,4 2,3,5 2,3,6 3,4,5 3,4,6 etc. Where sk is the standard deviation for band k, and rjis the absolute value of the correlation coefficient between any two of the three bands being evaluated. The largest OIF will generally have the most information (as measured by variance) with the least amount of duplication (as measured by correlation) Jensen, 2003

27. Optimum Index Factor Ranks the 20 three-band combinations that can be made from six bands of Landsat TM data Band combination: 1,2,3 Band combination: 3,4,5 Jensen, 2003

28. Computing the Distance (Length) of a Linear Feature Distance is one of them most important geographic measurements extracted from remotely sensed imagery. Distance measurements are usually made using a rubber-band tool that lets the analyst identify beginning and ending vertices of the line and their X- and Y-coordinates. If the remotely sensed data has not been rectified to a standard map projection, then the X- and Y-coordinates will be in row and column space (i,j). If the imagery has been geometrically rectified to a standard map projection then the X- and Y-coordinates will be in longitude and latitude or some other coordinate system. One of the most commonly used map projections is the Universal Transverse Mercator (UTM) projection with X-coordinates in meters from a standard meridian and Y-coordinates in meters measured from the Equator. Jensen, 2003

29. Computing the Distance (Length) of a Linear Feature Once the coordinates of the beginning (X1, Y1) and ending vertices (X2, Y2) are identified, it is a simple task to use the Pythagorean theorum which states that the hypotenuse of a right triangle (c) can be computed if we know the length of the other two legs of a right triangle (a and b, respectively): c a b Jensen, 2003

30. Marco Island, Florida SPOT 10 x 10 m Panchromatic Data Jensen, 2003

31. Distance Measurement Jensen, 2003

32. Distance and Area Measurement Jensen, 2003

33. Computing the Distance (Length) of a Linear Feature This logic may be used to identify the length of the longest axis of the mangrove island, where: Jensen, 2003

34. Computing the Area of an Area of Interest (AOI) Polygon The area of a rectangle on a remotely sensed image is computed simply by multiplying the values of its length and width, i.e., A = l x w. Another elementary area computation is that of a circle which is A = pr2. Complications can arise, however, when the shape of the polygon varies from a rectangle or circle. In the remote sensing literature, polygons are also often referred to asareas of interest (AOIs). Jensen, 2003

35. Geographic Area of Interest (AOI) Jensen, 2003

36. Area Measurement Jensen, 2003

37. Distance and Area Measurement Jensen, 2003

38. Computing the Area of an Area of Interest (AOI) To calculate the area of a polygon (or AOI) in remotely sensed imagery, the analyst typically uses a rubber-band tool to identify nvertices at unique map (X, Y) or image (row and column) coordinates. The “contribution” of each point (vertex) in the polygon to the area is computed by evaluating the X-coordinate of a vertex prior to the vertex under examination (Xi-1) with the X-coordinate of the next vertex in the sequence (Xi+1) and multiplying the result by the Y-coordinate (Yi) of the vertex under examination according to the following formula: Jensen, 2003

39. Computation of the Area of a Mangrove Island Near Marco Island, FL using SPOT 10 x 10 m Panchromatic Data Jensen, 2003

40. Distance and Area Measurement Jensen, 2003

41. Merging Different Types of Remotely Sensed Data for Effective Visual Display • All data sets to be merged must be accurately registered to one another and resampled to the same pixel size. Several alternatives exist for merging the data sets, including: • Simple band substitution methods • Color space transformation and substitution methods using various color coordinate systems. • Substitution of the high spatial resolution data for principal component #1. Jensen, 2003

42. Color Composite of Marco Island, Florida SPOT Imagery October 11, 1988 Created using the band substitution method: R = SPOT band 3 (NIR) 20 m G = SPOT band 4 (Pan) 10 m B = SPOT band 1 (Green) 20 m Jensen, 2003

43. Intensity, Hue, Saturation (HIS) Color Coordinate System Jensen, 2003

44. Merging Different Types of Remotely Sensed Data for Effective Visual Display Intensity-Hue-Saturation (HIS) Substitution: The vertical axis represents intensity (I) which varies from black (0) to white (255) and is not associated with any color. The circumference of the sphere represents hue (H), which is the dominant wavelength of color. Hue values begin with 0 at the midpoint of red tones and increase counterclockwise around the circumference of the sphere to conclude with 255 adjacent to 0. Saturation (S) represents the purity of the color and ranges from 0 at the center of the color sphere to 255 at the circumference. A saturation of 0 represents a completely impure color in which all wavelengths are equally represented and which the eye will perceive as a shade of gray that ranges from white to black depending on intensity. Jensen, 2003

45. Merging Different Types of Remotely Sensed Data for Effective Visual Display Intensity-Hue-Saturation (IHS) Substitution: IHS values can be derived from the RGB values through the transformation equations: Substitute Intensity data from the IHS transformation for one of the bands, e.g., RGB = 4, I, 2 Jensen, 2003

46. Relationship Between RGB and IHS Color Systems Jensen, 2003