1. GIS UPDATE?
Today:
Raster Analysis
Lab 10, Sea Level Rise Analysis
No Class on Thursday
Mid Term Study Guide soon
Mid Term II 11/13/14

2. Raster Analysis Raster math Statistics: min, max, mean, std. dev.
Local, Neighborhood, Zonal
Distance (cost)
Topography: Slope, aspect, contours
Reclassify
Raster / Vector Conversions
Originally Developed by James Graham, modified by J. R. Patton

3. Raster Data Model
Uses grid cells of a given dimension to represent the value or attribute of a real world entity or phenomenon

4. The value may be a measurement or a code.
Cell values are numeric: can be either positive or negative, integer, or floating point.

5. Images:
True Color Composite (multi-band raster data set; 3 raster layers; 1 each for RGB)
Spectral Reflectance

6. Raster Layer-> Properties

7. ArcGIS Data Types Name ArcGIS Attributes ArcGIS GRIDS* Geodatabase Bit
Chew
2 bit
Nibble
4 bit
Unsigned byte
Unsigned 8 bit
Signed byte
Signed 8 bit
Unsigned short
Unsigned 16 bit
Signed short
Short Integer
Signed 16 bit
Unsigned Integer
Unsigned 32 bit
Signed Integer
Signed 32bit
Long integer
Long
Long Integer
Float
Floating-point 32 bit
Single-precision floating point
Double
Double-precision floating-point
String
Text
Date
* ArcGIS documentation indicates the GRID values are always stored as 32-bit values
See:

8. Computer-Based Numeric Data Types
Name
Number of Bits
Number of Bytes
Minimum Value
Maximum Value
Number of Values
Sig. Digits
Bit 1
1/8
2 (21)
<1
Chew 2 3
4 (22)
Nibble 4 15
16 (24)
Unsigned Byte 8
255
256 (28)
>2
Signed Byte
(aka chars)
-128
127
Unsigned Short 16
-32768
32767
65536 (216 or 64k)
>4
Signed Short
65535
Unsigned Integer (Int) 32
4,294,967,295
4,294,967,296
(232 or 4 Gig)
>9
Signed Integer
-2,147,483,648
2,147,483,647
Long
(always signed) 64
A big negative number
A big positive number
264
>19
Float
~10-40
~1040
232 ~7
Double
~10-300
~10300
~15
See:

9. Dana Tomlin and Joseph Berry (1970’s)
A method of treating individual raster layers as members of algebraic expressions
2 * LayerA

10. Arithmetic Operators (+, -, *, /)
LayerA + LayerB
Arithmetic Operators (+, -, *, /)
Mathematical Functions (Sqr, Sqrt, Log, Abs, exp, int, etc.)
Comparison Operators (>, >=, =, <>, <, <=)
Boolean Operators (AND, OR, NOT, XOR)

11. Toolbox → Spatial Analysis Tools → Map Algebra → Raster Calculator

12. 3 4 2 1 2 9 = -

13. Local: Arithmetic: +,-,/, *, Boolean: CON (Conditional)
MOD (Modulo): returns the remainder
Boolean:
OR: If either input is true, output is true
AND: If both inputs are true, output is true
CON (Conditional)

14. Abs (absolute): flips negatives to positive
Ceil (ceiling): float to integer next highest integer value (i.e > 2)
Floor: float to integer giving next lowest integer value (i.e > 1)
Int (integer): truncates float to integer

15. <> (Not Equals)
== (Equals)
< (Less than)
<= (Less than or equal to)
> (Greater than)
>= (Greater than or equal to)

16. Raster Math: Boolean AND
AND 1 = 1 1 1 =
AND
“AND” works but the calculator will insert “&”

17. Raster Math: Boolean OROR 1 = 1 1 1 1 = OR
“OR” works but the calculator will insert “!”

18. Map Algebra Examples:
Fire Hazard:
[Fuel Density Hazard] + [Slope Hazard] + [Veg Type Hazard]
Probability of encountering the cascade treefrog (Litoria pearsoniana) within the forests of eastern Australia:
1 / (1 + exp( – * log10(RAINFALL) – * PALMS))
RAINFALL= the annual volume of rain falling in the watershed above the stream
PALMS = 1 if palms are present at the site and 0 otherwise.

19. aspect between 150 and 200 degrees.
Raster Analysis: reclass
Identifying suitable habitat
South facing slopes
aspect between 150 and 200 degrees.
Slope < 20 degrees
Green = suitable slope (cell value = 1)
Red = unsuitable slope (cell value = 0)
Green = suitable aspect (cell value = 1)
Red = unsuitable aspect (cell value = 0)

20. Identify Slope < 20 degrees
(Alternative method)
“Slope” < 20

22. “suitable aspect” * “suitable slope”
suitable_habitat

23. Suitable slope & Suitable aspect
Cell value = 1…
Suitable slope & Suitable aspect 1 1 1 = *

24. “No-Data” or NULL Values
Rasters are always rectangular
No-Data values are “transparent” and are not used for calculations

25. Land cover
Digital Elevation
Model (DEM)

26. Raster Sources Scanned Remotely Sensed Derived Rasters Topos
Aerial Photos
Satellite Photos
Digital Elevation Models (DEM)
Derived Rasters
Hill shade
Slope
Aspect
Statistical Spatial Analysis

27. Digital Raster Graphic

28. Digital Elevation Model (DEM)
Each pixel value is an elevation

29. Digital Orthophoto Quarter Quad (DOQQ)
1 meter aerial photos

30. Flight Characteristics
The flight characteristics also apply to satellites and cause the images to be rather strange shapes within a rectangular raster grid
The good news is that preprocessed, orthorectified images have all the distortion removed

31. LandSat 7 Bands 30m, 15m bw Entire earth Twice a month
26 years of coverage
“Free”
EROS Data Center

32. National Land Cover Dataset (NLCD)
Based on Landsat Imagery
21 Classes based on cover type

33. 1992
2006
2001

34. Derived Rasters Land Cover from satellite and aerial
Topography: Slope, aspect, hillshade
Ecoregions
Suitable Habitat
Flood plains
Geological Regions

35. Shaded Relief (a.k.a Hillshade)

36. GeoReferenced File Formats
GRID: ESRI’s format
GeoTIFF: Excellent support
MrSID: LizardTech
IMG: ERDAS
ECW: ERMapper
BIL, BIP, BSQ: See header
“ASCII” or “GRID ASCII” (asc)
Lots of others…

37. Conversions Raster to Point: Raster to Polyline: Raster to Polygon:
Countour
Streams
Raster to Polyline
Raster to Polygon:
Viewsheds
Watershed
Raster to Polygon
Point to Raster
Interpolation
Density
Polyline to Raster
Polygon to Raster

38. Raster Analysis Raster math Statistics: min, max, mean, std. dev.
Local, Neighborhood, Zonal
Distance (cost)
Topography: Slope, aspect, contours
Reclassify
Raster / Vector Conversions

39. Raster Statistics Local Neighborhood (or Focal) Zonal Global
Operate on one pixel in each raster
Neighborhood (or Focal)
Operate on a few pixels around each pixel
Zonal
Collection of regions
Region is a contiguous area of the same pixel values
Global
Operate on the “whole” raster

40. Local Stats
“Cell Statistics” computes stats on all the values for a pixel in a multi-band raster.
Example for “Sum”:
Esri ArcGIS 10 Help

41. Neighborhood (or Focal)
Result=Operation on pixels nearby
Columns 12 20 23 34 40 15 30 31 39 22 29 14 28 38 13 19 25 32 37
- Min,Max,Mean of various areas. Shape can be square or circular
Rows

42. Focal Statistics Computes stats on pixels around an existing pixel
Example for Sum:
Esri ArcGIS 10 Help

43. Zonal Statistics Computes statistics for defined regions (features)
Tools:
Zonal Statistics:
Outputs a raster (not sure of the value)
Zonal Statistics as Table:
Outputs a table
Used to compute all kinds of valuable things:
Percent land cover
Percent impervious cover
Percent water below a certain depth
Etc.

44. Zonal Statistics Zone Layer Value layer Output layer (or table)
Esri ArcGIS 10 Help

45. Conditional Operator Con!
Con(<condition>,<true>,<false>)
Given a raster “condition”:
Puts the true value where true and false value where false
Con!

46. Elevations over 3000 meters
Con("W100N40.DEM“>3000,0,1)

47. Elevations over 3000 meters
Mount Hood

48. Reclassify

49. Mosaic Rasters
Data Management Tools -> Raster -> Raster Dataset -> Mosaic to new Raster

50. Mosaic To Raster