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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. Raster Analysis. Raster math Statistics: min, max, mean, std. dev. Local, Neighborhood, Zonal Distance (cost) Topography: Slope, aspect, contours - PowerPoint PPT Presentation
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GIS UPDATE?
Today:• Raster Analysis• Lab 10, Sea Level Rise Analysis
No Class on ThursdayMid Term Study Guide soonMid Term II 11/13/14
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
Raster Data Model
Uses grid cells of a given dimension to represent the value or attribute of a real world entity or phenomenon
The value may be a measurement or a code.
Cell values are numeric: can be either positive or negative, integer, or floating point.
Images:True Color Composite (multi-band raster data set; 3 raster layers; 1 each for RGB)
Spectral Reflectance
Name ArcGIS Attributes ArcGIS GRIDS* Geodatabase
Bit 1 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 Short Integer
Unsigned Integer Unsigned 32 bit
Signed Integer Signed 32bit Long integer
Long Long Integer
Float Float Floating-point 32 bit Single-precision floating point
Double Double Double-precision floating-point
String Text Text
Date Date Date
* ArcGIS documentation indicates the GRID values are always stored as 32-bit valuesSee: http://www.esri.com/news/arcuser/1002/files/table_2.pdf, http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=Bit_depth_capacity_for_raster_dataset_cells ,http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?topicname=Technical_specifications_for_raster_dataset_formats
ArcGIS Data Types
Name Number of Bits
Number of Bytes
Minimum Value Maximum Value Number of Values Sig. Digits
Bit 1 1/8 0 1 2 (21) <1
Chew 2 ¼ 0 3 4 (22) <1
Nibble 4 ½ 0 15 16 (24)
Unsigned Byte 8 1 0 255 256 (28) >2
Signed Byte (aka chars)
8 1 -128 127 256 (28) >2
Unsigned Short 16 2 -32768 32767 65536 (216 or 64k) >4
Signed Short 16 2 0 65535 65536 (216 or 64k) >4
Unsigned Integer (Int) 32 4 0 4,294,967,295 4,294,967,296(232 or 4 Gig)
>9
Signed Integer 32 4 -2,147,483,648 2,147,483,647 4,294,967,295 >9
Long(always signed)
64 8 A big negative number
A big positive number
264 >19
Float(always signed)
32 4 ~10-40 ~1040 232 ~7
Double(always signed)
64 8 ~10-300 ~10300 264 ~15
See: http://en.wikipedia.org/wiki/Integer_overflow, http://steve.hollasch.net/cgindex/coding/ieeefloat.html
Computer-Based Numeric Data Types
Dana Tomlin and Joseph Berry (1970’s)A method of treating individual raster layers
as members of algebraic expressions2 * LayerA
Arithmetic Operators (+, -, *, /)Mathematical Functions (Sqr, Sqrt, Log, Abs, exp, int, etc.)Comparison Operators (>, >=, =, <>, <, <=)Boolean Operators (AND, OR, NOT, XOR)
LayerA + LayerB
Local:
• Arithmetic: +,-,/, *, • 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)
• Abs (absolute): flips negatives to positive• Ceil (ceiling): float to integer next highest integer value
(i.e. 1.1 -> 2)• Floor: float to integer giving next lowest integer value
(i.e. 1.1 -> 1)• Int (integer): truncates float to integer
• <> (Not Equals)• == (Equals)• < (Less than)• <= (Less than or equal to)• > (Greater than)• >= (Greater than or equal to)
Raster Math: Boolean AND
0 0
1 1
0 1
0 1
0 0
0 1AND =
AND =0 1 0
“AND” works but the calculator will insert “&”
Raster Math: Boolean OR
0 0
0 1
1 1
0 1
1 1
0 1OR =
OR =0 1 1
“OR” works but the calculator will insert “!”
Probability of encountering the cascade treefrog (Litoria pearsoniana) within the forests of eastern Australia:
1 / (1 + exp(10.48 – 2.204 * log10(RAINFALL) – 2.037 * 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.
Map Algebra Examples:
[Fuel Density Hazard] + [Slope Hazard] + [Veg Type Hazard]Fire Hazard:
Green = suitable aspect (cell value = 1)Red = unsuitable aspect (cell value = 0)
Slope < 20 degrees South facing slopes aspect between 150 and 200 degrees.
Identifying suitable habitat
Green = suitable slope (cell value = 1)Red = unsuitable slope (cell value = 0)
Raster Analysis: reclass
Cell value = 1…Suitable slope & Suitable aspect
1 0 0 1
1 1 0 0
0 0 0 1
0 1 0 1
0 0 0 0
0 1 1 0
1 1 0 1
1 1 0 1
0 0 0 0
0 1 0 0
0 0 0 1
0 1 0 1
* =
“No-Data” or NULL Values• Rasters are always rectangular• No-Data values are “transparent” and are not
used for calculations
Raster Sources• Scanned
– Topos• Remotely Sensed
– Aerial Photos– Satellite Photos– Digital Elevation Models (DEM)
• Derived Rasters– Hill shade– Slope– Aspect– Statistical Spatial Analysis
LandSat• 7 Bands• 30m, 15m bw• Entire earth
• Twice a month• 26 years of
coverage • “Free”• EROS Data Center
Derived Rasters
• Land Cover from satellite and aerial• Topography: Slope, aspect, hillshade• Ecoregions• Suitable Habitat• Flood plains• Geological Regions
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…
Conversions• Raster to Point:
– Raster to Point• Raster to Polyline:
– Countour– Streams– Raster to Polyline
• Raster to Polygon:– Viewsheds– Watershed– Raster to Polygon
• Point to Raster– Interpolation– Density– Point to Raster
• Polyline to Raster– Polyline to Raster
• Polygon to Raster– Polygon to Raster
Raster Analysis• Raster math• Statistics: min, max, mean, std. dev.
– Local, Neighborhood, Zonal• Distance (cost)• Topography: Slope, aspect, contours• Reclassify• Raster / Vector Conversions
Raster Statistics
• Local– 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
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
Neighborhood (or Focal)• Result=Operation on pixels nearby
12 20 23 34 40
15 23 30 31 39
15 22 29 30 40
14 20 28 29 38
13 19 25 32 37
Columns
Rows
Focal Statistics• Computes stats on pixels around an existing pixel• Example for Sum:
Esri ArcGIS 10 Help
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.
• Con(<condition>,<true>,<false>)• Given a raster “condition”:
• Puts the true value where true and false value where false
Conditional Operator
Con!