1. Chapter 8
Raster Analysis

2. Outline Raster Data Raster analysis techniques
Map Algebra and Boolean overlay
Other functions

3. Georeferenced to earth’s surface
Raster Data
A data set composed of an array of numeric values, each of which represents a condition in a square element of ground.
X, Y location
X, Y location
Columns
Rows
Raster data file
N rows by M columns
Georeferenced to earth’s surface

4. Pixels or Cells Each pixel contains one numeric value
Dimension of a pixel varies (resolution)
Value represents some property of that pixel area, e.g. elevation or rainfall
Values may be integers or floating point numbers 3 1 4 6 2 5
30m
Unlike a polygon, each cell has only ONE attribute: its value.
Storing multiple values means storing multiple rasters.

5. Pixel depth The number of bytes used for each pixel Integer values
More bytes = larger numbers = more space
Integer values
8-bit pixel (one byte) stores
16-bit pixel (two bytes) stores ,565
24-bit pixel (three bytes) stores million
Floating point values
Required for decimal number storage
32-bit pixel (four bytes)

6. Types of raster data Discrete raster: land use Continuous raster: DEM
Discrete raster: roads
Continuous raster: image

7. Why use rasters? Better at storing certain kinds of data.
Better at analyzing certain kinds of data.
Often faster analysis than vectors.
Imagery desirable for certain maps.
BUT
Coordinate precision generally lower.
High precision has high storage costs.
Cannot store multiple attributes.

8. Raster resolution Measured by cell size dimensions
Storage space increases as the square of the resolution
50 m raster
16X bigger!
Vector format
200 m raster

9. Cell size units
Cell x-y resolution units are based on the raster’s coordinate system definition
Decimal degrees*
Meters
Feet
*Because distances and areas are fundamental to raster analysis, it is best to use projected coordinate systems for rasters.

10. Raster analysis techniques
Raster analysis uses cell-by-cell functions on one or more input grids.
Cells must be the same size and line up spatially.
Older software required the user to ensure that all input rasters had exactly the same size, shape, and aligned cell sizes.

11. Resampling
If input grids do not match, then one must be resampled to match the other. Resampling can degrade the accuracy of a raster even if the difference in cell size and location is small.
The new cell grid is determined, and the old cell values must be fit into the new structure somehow.
Several methods are used for resampling. Y2

12. Caution
ArcGIS automatically resamples rasters for analysis if needed, without warning the user.
Because resampling can degrade raster quality, users should be aware that this may be happening.
Best practices:
Strive to create and keep analysis data in aligned rasters with common extents and cell sizes.
Use environment settings to enforce common grids for newly created rasters
Use the available resampling functions to prepare data, so that YOU can control how resampling happens rather than having the software do it automatically.

13. Map algebra and Boolean overlay
Rasters are essentially arrays of numbers
Can be added, subtracted, etc
Line up matching cells vertically
Ingrid1 +
Ingrid2 =
Outgrid
Map algebra

14. Map Algebra: Conversions
Precip in cm
Precip in inches
[Precip_cm] / 2.54

15. Map Algebra: logical operators
Logical operators produce either TRUE (1) or FALSE (0) values in the output grid, based on whether a cell meets the condition.
[crowncov] < 70 AND [crowncov] > 40
[Elevation] > 1200
[Slope] < 10

16. Boolean rasters
Boolean rasters represent maps of True/False states for a particular condition
1 = True
0 = False
Slope < 10 degrees?

17. Boolean operators A AND B A XOR B A NOT B A OR B A B
Boolean rasters can be evaluated further using the Boolean operators.

18. Boolean Map Algebra
True (1)
False (0)
A AND B
A OR B
A NOT B
A XOR B
Inputs A B
Output
Compare values of corresponding cells and determine output value

19. Boolean overlay (AND) example1 1 1 =
AND
[Precip] AND [ElevGT1500]

20. AND is also multiplication1 1 1 = x
1 AND 1 = 1
1 AND 0 = 0
0 AND 1 = 0
0 AND 0 = 0
1 × 1 = 1
1 × 0 = 0
0 × 1 = 0
0 × 0 = 0
Boolean AND is equivalent to multiplication. So multiplying layers works just as well.

21. Additive Boolean overlay1 1 1 2 + =
Instead of multiplying, add the layers together to create a ranked result.

22. Other functions Other raster analysis techniques Reclassification
Surface functions
Distance functions
Density functions
Interpolation
Neighborhood functions
Zonal functions

23. Reclassify Slope High slope/low slope
Convert one set of grid values to another
Manual or classify

24. Surface analysis
DEM
Slope
Aspect
Hillshade
Contouring

25. Slope function
Calculates slope of the surface based on surrounding cells. Can be expressed in degrees or percent.

26. Aspect function
Calculates direction of steepest slope, e.g. which way the slope “faces”. Value represents direction from where 0/360 is North. Flat areas are assigned a -1 value.

27. Hillshade
Calculates the brightness or illumination of a surface from a specified light source.
Applications include terrain display and modeling satellite reflectance.
Azimuth is direction of illumination source (315 by default)
Altitude is the angle of the source above the horizon (45 deg)

28. Viewshed analysis
Calculate areas visible from a set of observation points

29. Hydrologic functions
Derive streams, watersheds, and other hydrologic features based on analysis of a DEM.

30. Euclidean distance
Starts from a set of features (points, lines, polygons).
Creates a grid where each cell represents distance to the closest of the features.
Distances are given in coordinate system map units
Distance to roads (meters)

31. Euclidean direction
Often created in association with a distance function
Indicates direction of travel to the closest road.
Direction to roads (degrees)

32. Buffers
Roaddist < 500
Logical expressions easily create Boolean rasters representing buffers from distance rasters.

33. What is interpolation?
Interpolation is the prediction of values in between measured points.
Sampling of points may be uniform, random, or based on a sampling scheme.
Numerous methods are used which have different mathematical models and make different assumptions about the data.
Best application of interpolation relies on substantial study of models and assumptions. If you use it a lot—learn more!

34. Interpolation is NOT truth!
Actual elevation
Interpolated from summits

35. Density functions
Appear similar to interpolation, but are calculated differently
Interpolation predicts values between points using a variety of mathematical methods
Density functions count occurrences within a given radius and divide by the area
Occurrences may be features or attributes of features (number of cities versus city population).

36. Neighborhood statistics
Calculates a statistic for the specified window. 3 1 4 6 2 5
Window

37. Zonal statistics Zones defined by the zone layer (watersheds)
Watersheds and slope
Zones defined by the zone layer (watersheds)
Generates statistics for each zone from the value grid (slope)
Output is either a raster, or a table
Average slope in watershed