1. Fundamentals of GIS Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011 Lecture 5: Introduction to Raster Analysis ------Using GIS-- By Austin Troy, Brian Voigt and Weiqi Zhou University of Vermont

2. Fundamentals of GIS Raster data-A Refresher Raster Elements –Extent –# rows –# columns –Coordinates –Origin –Orientation –Resolution –Grid cell Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

3. Fundamentals of GIS Raster Data Structure Methods for storing raster data in a more computationally and memory efficient way. Where a raster layer is random noise, this does not work. Requires repetitive patterns or areas of homogeneity. The fewer z values, the easier to compress. Simplest method is cell-by-cell encoding where cell values are stored by row and column number; This is essentially uncompressed. DEM’s and satellite images generally use this structure because there is typically so much variation. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

4. Fundamentals of GIS Raster Data Structure Run-length encoding (RLE): –Compression method that records cell values in groups called “runs.” –It records the starting and ending pixel for a “run” with the same value for a given row, so hundreds of pixels could be recorded with only two values, if they all have the same value and are adjacent. –However, because it measures runs along rows, it is not efficient for two dimensional areas of homogeneity. –RLE can reduce file size by 10:1, depending on data. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

5. Fundamentals of GIS Raster Data Structure Runs: –Row 2: 3,4 –Row 3: 2, 8 –Row 4: 4,7 –Row 5: 5,7 –Row 6: 2,6 Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

6. Fundamentals of GIS Raster Data Structure Chain code: –This is a more efficient method for dealing with two-dimensional compression –This defines a homogeneous two-dimensional area using cardinal directions and units movements to define bounding perimeter in relative terms from a known point –For instance, go 2 N, 1 W, 1N, 3 W, 1S….etc. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

7. Fundamentals of GIS Raster Data Structure Here, starting from the lower left, the computer would define that coordinate then code 1N, 3E, 1N, 1W, 1N, 2W, 1N, 1E, 1N, 2E etc….. This would define the perimeter of a homogeneous area. All must have exactly the same value Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

8. Fundamentals of GIS Raster Data Structure Block code: –A method that uses square blocks to represent areas of homogeneous values –Each block is encoded only with location of one corner cell and the dimensions; since they are square, only one dimension needs to be given –Uses medial axis transformation technique Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

9. Fundamentals of GIS Raster Data Structure Quad tree: –Divides a grid into hierarchy of quadrants –Starts with four quadrants; any quadrant that has totally homogeneous cells will not be subdivided further, but is stored as a “lead node” which is coded only with that value and the id of the quadrant. –Any quadrants with more than one value are subdivided again into four more quadrants and again the computer checks for homogeneity. –It keeps on doing this until it has generated all its leaf node or until it gets down to the pixel level –This is known as recursive decomposition –This is good where one part of a grid is very uniform and the rest is heterogeneous. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

10. Fundamentals of GIS Raster Data Structure Quad tree: Homogeneous (all one value) Not homogeneous: more than one value within quadrant Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

11. Fundamentals of GIS Raster Data Structure Quad tree: now we break down those quadrants with non-homogeneous values into four sub quadrants Not homogeneous: more than one value within quadrant Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

12. Fundamentals of GIS Raster Data Structure Quad tree: and we keep doing this until we’ve come down to the point where there are only homogeneous quadrants, even if those are one cell in dimension Not homogeneous: more than one value within quadrant Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

13. Fundamentals of GIS Raster Data Structure Quad tree: One value (leaf node) Mixed values (non-leaf) Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

14. Fundamentals of GIS Converting Features to Rasters ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

15. Fundamentals of GIS Converting Vector to Raster ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

16. Fundamentals of GIS Raster Overlay Queries The raster data model performs overlay operations more efficiently than the vector model Raster cells have a one-to-one relationship between layers Raster overlay queries: combining two or more thematic layers to identify relationships between them such as: –Areas that are common to all layers –Areas that meet criteria from each layer Query example: (“elevation” > 2500) & (“Slope” > 20) ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

17. Fundamentals of GIS Raster Overlay Calculations Map algebra can be performed to identify relationships between layers, or to derive indices that describe phenomena Map algebra calculations create a new output raster Example: ------Using GIS-- (“Soil_depth_1990”) – (“Soil_depth_2000”) = Soil loss 1990 - 2000 Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

18. Fundamentals of GIS ------Using GIS-- Source: ESRI Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

19. Fundamentals of GIS Map Query: Example 1 Single layer numeric example: lu_chit = 11 (residential) ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

20. Fundamentals of GIS Map Query: Example 1 Lecture Materials by Austin Troy except where noted © 2008

21. Fundamentals of GIS Map Query: Example 2 Single layer numeric example: lu_chit = 11 (residential) Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

22. Fundamentals of GIS Map Query: Example 2 Lecture Materials by Austin Troy except where noted © 2008

23. Fundamentals of GIS Map Query: Example 3 Con Function Results in a binary True/False layer Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

24. Fundamentals of GIS Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

25. Fundamentals of GIS Map Query Examples Multi-criteria, single layer, categorical map query: looking for all developed land use types, using attribute codes (11, 12, 13) and the OR logical operator Results in a 1/0 binary layer, showing urbanized areas ------Using GIS-- Vertical lines mean OR Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

26. Fundamentals of GIS Map Query Examples ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

27. Fundamentals of GIS Map Query Examples One can then convert this to a feature class or shapefile ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

28. Fundamentals of GIS Raster Query: 2 Layer Example Multi-layer queries use criteria across two or more layers; in this case we’ll query land use (categorical), elevation (number) and slope (number) ------Using GIS-- Let’s say we want to find identify potential habitat for a rare plant that grows at higher elevation, on steeper slopes and in coniferous forest Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

29. Fundamentals of GIS Raster Query: Slope ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

30. Fundamentals of GIS Raster Query: Multiple Criteria Multiple criteria, multiple layers Land Cover = Coniferous Forest (42) Elevation > 800 Slope > 20% ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

31. Fundamentals of GIS Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

32. Fundamentals of GIS Map Calculation We can also make calculations between layers (or between a layer and a constant): here we’ll multiply the k factor (soil erodibility factor) by slope; let’s just imagine this will yield a more accurate and spatially explicit index of erodibility that factors in slope at each pixel ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

33. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Map Calculation Darker areas feature both steep slopes and erodible soils. Advantage over map query approach: result is a continuous index of values, rather than just a “true” / “false” dichotomy ------Using GIS--

34. Fundamentals of GIS Map Calculation and Query We could then run a map query to find areas that have high erodibility factors and urban land use. Lecture Materials by Austin Troy except where noted © 2008 ------Using GIS--

35. Fundamentals of GIS Zonal Statistics Suppose we had a proposed subdivision map (this one is made up). We could overlay it on our new index to determine which proposed subdivisions are problematic (due to soil erodibility). ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

36. Fundamentals of GIS Zonal Statistics Summarize the mean, max or sum for some value within each of the bounding units Polygon and Raster Raster and Raster Here we summarize by subdivision zones the mean soil erodibility value (from our calculation). ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

37. Fundamentals of GIS Zonal Statistics Produces a DBF table with the specified summary statistics Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011 ------Using GIS--

38. Fundamentals of GIS Zonal Statistics Now we can plot out the subdivision boundaries (zones) by a soil erosion statistic. In this case we plot subdivision boundaries shaded by the mean of the soil erosion statistic. This represent the mean value of all the soil erosion pixels underlying a polygon Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011 ------Using GIS--

39. Fundamentals of GIS Reclassifying Raster Data ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

40. Fundamentals of GIS Reclassifying Raster Data ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

41. Fundamentals of GIS Reclassifying Raster Data ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

42. Fundamentals of GIS Neighborhood Statistics Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

43. Fundamentals of GIS Low Pass Filter Functionality: averaging filter –Emphasize overall, general trends at the expense of local variability and detail. –Smooth the data and remove statistical “noise” or extreme values. Summarizing a neighborhood by mean or median –The larger the neighborhood, the more you smooth, but the more processing power it requires. –A circular neighborhood: rounding the edges of features. –Resolution of cells stays the same. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

44. Fundamentals of GIS High Pass Filter Functionality: edge enhancement filter –Emphasize and highlight areas of tonal roughness, or locations where values change abruptly from cell to cell –Emphasize local detail at the expense of regional, generalized trends Perform a high pass filter –Subtracting a low pass filtered layer from the original –Summarizing a neighborhood by standard deviation –Using weighted kernel neighborhood Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

45. Fundamentals of GIS Neighborhood Statistics Min, max, mean, standard deviation, range, sum, variety Window size/shape Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

46. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Filtering out anomalies in bathymetric data Bathymetry mass points: sunken structures Low Pass Filter: Example

47. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 After turning into raster grid We see sudden anomaly in grid Say we wanted to “average” that anomaly out

48. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Try a low-pass filter of 5 cells We can still see those anomalies but they look more “natural” now

49. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Try a low-pass filter of 25 cells The anomalies have been “smoothed out” but at a cost

50. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 We can also do a local filter in that one area

51. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 What about high pass filters? Say we wanted to isolate where the wreck was All areas of sudden change, including our wrecks, have been isolated

52. Fundamentals of GIS Low pass filter for elevation Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

53. Fundamentals of GIS A low pass filter of the DEM done by taking the mean values for a 3x3 cell neighborhood: notice it’s hardly different DEM Low pass Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

54. Fundamentals of GIS 10 unit square neighborhood Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

55. Fundamentals of GIS 20 unit square neighborhood Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

56. Fundamentals of GIS In this high-pass filter the mean is subtracted from the original It represents all the local variance that is left over after taking the means for a 3 meter square neighborhood Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

57. Fundamentals of GIS We do this using Spatial Analyst Tools >>> Math >>> Minus Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

58. Fundamentals of GIS If we do a high-pass filter by subtracting from the original the means of a 20x 20 cell neighborhood, it looks different because more local variance was “thrown away” when taking a mean with a larger neighborhood Dark areas represent things like cliffs and steep canyons Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

59. Fundamentals of GIS Using standard deviation is a form of high-pass filter because it is looking at local variation, rather than regional trends. Here we use 3x3 square neighborhood Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

60. Fundamentals of GIS Note how similar it looks to a slope map because it is showing standard deviation, or normalized variance, in spot heights, which is similar to a rate of change. Hence it is emphasizing local variability over regional trends. The resolution of the slope is quite high because it is sampling only every nine cells. When we go to a larger neighborhood, by definition, the resulting map is much less detailed because the standard deviation of a large neighborhood changes little from cell to cell, since so many of the same cells are shared in the neighborhood of cell x,y and cell x,y+1. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

61. Fundamentals of GIS Here is the same function with an 8x8 cell neighborhood. The coarser resolution (due to the larger neighborhood) makes it so that slope rates seem to vary more gradually over space Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

62. Fundamentals of GIS Later on we’ll look at filters and remote sensing imagery, but here is a brief example of a low-pass filter on an image that has been converted to a grid. This can help in classifying land use types Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

63. Fundamentals of GIS Raster Surface Tools Arc GIS allows you to use a digital elevation model (DEM) to derive: Hillshade Slope Contours Aspect Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

64. Fundamentals of GIS Raster Surface Tools DEM + Hillshade = Hillshaded DEM ------Using GIS-- += Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

65. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Display Options 1.Place the hillshade “under” the DEM in the TOC 2.Make the DEM partially transparent ------Using GIS--

66. Fundamentals of GIS Raster Surface Tools Lecture Materials by Austin Troy except where noted © 2008 ------Using GIS-- SlopeContourAspect

67. Fundamentals of GIS Viewshed Analysis This is a multi-layer function that analyzes visibility based on terrain. It requires a raster terrain layer and a point layer and produces a visibility layer (raster) that tells you where the feature can be seen from, or alternately, what areas someone standing at that feature could see (remember, line of sight is two way). If there are more than one point feature, then each grid cell tells you how many of the point features can be seen from a given point. However in that case, you lose information about the other direction; You don’t know which features can see a particular grid cell. Viewshed analysis can use “offsets” to define the height of the viewer or of the object being viewed; designated using a new field in the input layer’s attribute table. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

68. Fundamentals of GIS Viewshed Analysis Let’s say we’re local planners who are considering sites for a new waste treatment facility in a valley where the vacation homes of five rich and powerful executives are located. We want it in a place that won’t ruin anyone’s views, since they comprise 95% of the local tax base. This generates a grid with three values, representing how many houses can see a given pixel Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

69. Fundamentals of GIS Viewshed Analysis Red represents areas that can be seen by 1 house, blue by 2 or more Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

70. Fundamentals of GIS Viewshed Analysis In order to compare the viewability of several facilities, separate viewshed analyses need to be done for each feature. In the next example we will look at three candidate sites for a communications tower. Each will produce a viewability grid. This grid can then be superimposed on a layer showing residential areas. Since each grid will belong to a different tower, we can tell which tower will be most viewable from the residential areas through simple overlay analysis. Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

71. Fundamentals of GIS Viewshed Analysis In this case, red is for tower 1, blue for 2 and green for 3 Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

72. Fundamentals of GIS Proximity Can use raster distance functions to create zones based on proximity to features; here, each zone is defined by the closest stream segment ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

73. Fundamentals of GIS Distance Measurement ------Using GIS-- Can create distance grids from any feature theme (point, line, or polygon) Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

74. Fundamentals of GIS Distance Measurement Can also weight distance based on friction factors, like slope ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

75. Fundamentals of GIS Combining Distance and Zonal Stats Can also summarize distances by vector geography using zonal stats ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

76. Fundamentals of GIS Combining Distance and Zonal Stats Here we summarize by the mean ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

77. Fundamentals of GIS Density Functions We can also use sample points to map out density raster surfaces. This need to require a z value in each, it can simply be based on the abundance and distribution of points. Pixel value gives the number of points within the designated neighborhood of each output raster cell, divided by the area of the neighborhood Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

78. Fundamentals of GIS Density Functions Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

79. Fundamentals of GIS Density Functions Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011