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Chapter 8 Raster Analysis.

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Presentation on theme: "Chapter 8 Raster Analysis."— Presentation transcript:

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) example
1 1 1 = AND [Precip] AND [ElevGT1500]

20 AND is also multiplication
1 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 overlay
1 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

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