1. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Information Representation Imagery Terrain Thematic Maps

2. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Of the three types of maps discussed (image maps, physical models and line maps) image maps come closest to depicting the environment in the way we see it. Image maps are derived from systems that capture the a image of an entire region with all its "visible" details. The types of imaging systems are photographic systems, electronic sweep scanners and digital arrays.

3. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Photography is The process of collecting / recording images on a sensitized surface (e.g. film) by the action of light or other radiant energy. A Photograph is the resultant Negative or Positive

4. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Photography Example

5. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Imaging is The mapping of the intensity of radiation reaching a sensor as a function of position in a 1-D or 2-D field Either on film, in a computer, on a CRT screen, or other display device. An ‘image’ is the resultant map.

6. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imaging Example

7. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imaging Example

8. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Photo Interpretation The act of examining aerial photographs or images for the purpose of identifying objects & judging their significance Photogrammetry The science of obtaining reliable measurements by means of photography.

9. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Uses of Remotely Sensed Data Background Images for GIS Overlays Monitor Changes Extract Infrastructure Information Roads, Structures, etc Disaster Effects Extract Land Cover Information Vegetation Cover Water Sources

10. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Uses

11. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Uses

12. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Imagery Image vantage points are horizontal, oblique and vertical

13. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Horizontal This is the way we see the world. Capturing images from this vantage is problematic because of the limited area that can be seen and the variable scale associated with this vantage point.

14. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Oblique High oblique image are taken with the sensor inclined between 0 and 45 degrees to the horizon. The horizon is visible in a high oblique photograph. A low oblique photograph has a sensor incline between 45 and 90 degrees to the horizon. Scale for both of these images decreases from the foreground to the background.

15. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Vertical These are the most common type of images taken for inventory and mapping purposes. The sensor is perpendicular to the earth's surface. The greatest advantage of this type of image is that scale is relatively constant, and the block of features is minimized.

16. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Geometric Corrections Images have inherent distortions due to the imaging geometry of the sensor system. Photographic systems have distortions due to the perspective geometry of the photographic system and the camera orientation at the time the image is collected. These distortions have been mathematically modeled and can be corrected to produce orthophotography, a map-like image.

17. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Terrain Terrain is a critical aspect of our environment that limits the possible uses to which we can make of the land. Its portrayal on maps can take may forms.

18. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Data for Terrain Mapping and Analysis A Digital Elevation Model (DEM), consists of a sampled array of elevations for ground positions that are normally at regularly spaced intervals.

19. Introduction to Mapping Sciences: Lecture #2 (Information Representation) DEM Example

20. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Data for Terrain Mapping and Analysis Triangulated Irregular Network (TIN) Series of non-overlapping triangles Elevation values are stored at nodes Irregular distribution Sources: DEMs, surveyed elevation points, contour lines, and breaklines Breaklines are line features that represent changes of the land surface such as streams, shorelines, ridges, and roads

21. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Data for Terrain Mapping and Analysis Triangulated Irregular Network (TIN) Not every point in DEM is used Only points most important VIP (Very Important Points) algorithm Maximum z-tolerance algorithm Delaunay triangulation: all nodes are connected to their nearest neighbor to form triangles which are as equi-angular as possible. Borders are a problem Go beyond study area and clip to make best

22. Introduction to Mapping Sciences: Lecture #2 (Information Representation) TIN Example

23. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Terrain Mapping Contouring is most common method for terrain mapping Contour lines connect points of equal elevation (isolines) Contour intervals represent the vertical distance between contour lines. Arrangement of contour lines reflect topography

24. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Contour Example

25. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Terrain Mapping Vertical profile shows changes in elevation along a line, such as a hiking trail, road or stream.

26. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Terrain Mapping Hill shading is also known as a shaded relief or simply shading Attempts to simulate how the terrain looks with the interaction between sunlight and surface features. Helps viewers recognize the shape of land- form features on a map.

27. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Hillshading Example

28. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Hillshading Four factors control the visual effect of hill-shading Sun’s azimuth is direction of incoming light (0 to 360°) The sun’s altitude from horizon (0-90°) Surface slope (0-90°) Surface aspect (0 to 360°)

29. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Terrain Mapping Hypsometric tinting Applies different color symbols to represent elevation zones.

30. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Terrain Mapping Perspective View Perspectives are 3-D views of the terrain wherein the appearance is as viewed from an airplane. Viewing azimuth (0 to 360°) Viewing angle (0-90°) Viewing distance Z-scale is ratio between he vertical scale and the horizontal scale (exaggeration factor) 3-D draping of vector information

31. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Thematic Maps Planimetric maps Attribute maps focus on the locations of specific environmental features or attributes such as highways, towns, parks and rivers. Distribution/Statistical maps focus on the spatial variation of environmental features or themes.

32. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Attribute Maps Single-variable symbols: symbols that represent one kind of feature. Graphic elements used to communicate the qualitative aspects of the features are: shape, pattern arrangement, pattern orientation and color hue. (note: size, texture, color value or color intensity are reserved for quantitative representation.)

33. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Single Symbol Variables Point emphasis Identifies a feature at a specific location. Qualitative feature are usually represented by pictographic or geometric point symbols.

34. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Pictograph Example

35. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Geometric Shape Example

36. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Single Symbol Variables Line emphasis: symbols that have obvious length and width. Form is the usual graphic element used to distinguish line features.

37. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Single Symbol Variables Area emphasis: map makers use hue, arrangement and orientation to distinguish area qualities. The use of visual elements that indicate magnitude such as intensity, texture or size are confusing when applied to qualitative data and should not be used.

38. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Attribute Maps Multivariate symbols: usually map makers use separate symbols for each variable. At times however multiple variables are represented. By varying symbol shape, hue, texture and arrangement map makers could show four variables at once. However, no more than two variables are usually represented.

39. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Multiple Variables Point emphasis: 2 variables are often represented by combining shape and hue.

40. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Multiple Variables Line emphasis less useful because of the extent of the linear feature. Some examples are scenic routes and construction delays.

41. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Multiple Variables Area emphasis: multiple variables can be shown by overlapping regions or by combining pattern with color.

42. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Cartograms Area or distance are distorted to portray value

43. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Cartogram Example

44. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Statistical maps Statistical maps use quantitative symbols to depict magnitude information. The graphic elements for doing this are size, pattern texture, color value and color intensity.

45. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Statistical maps Single Variable: to show quantitative information symbols are varied to show changes in magnitude. These symbols can be either pictographic or geometric.

46. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Statistical maps Point emphasis Pictographic symbols usually vary only in size where as geometric symbols vary in texture, color value and color intensity as well as size. Creating apparent magnitude scaling of size and density compensation makes some accommodation of the limits of human perception.

47. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Statistical maps Line emphasis Cartographers use proportional line symbols to show changes in magnitude. One of the most common is line thickness.

48. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Statistical maps Area emphasis 2-D (Choropleth) These maps show both area and magnitude. 3-D maps Three types of maps are the region bounded map, the perspective profile and the continuous surface shading. While effective, the exact height at any given point is hard to determine.

49. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Choropleth Example

50. Introduction to Mapping Sciences: Lecture #2 (Information Representation) Statistical maps Multivariate symbols: showing several variables with the same symbol. Size is often used to represent multiple variables. Symbology can get complex and requires creativity. Pie Charts Bivariate Legends