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

Raster Layer-> Properties

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

• Toolbox → Spatial Analysis Tools → Map Algebra → Raster Calculator

3 4

2 3

0 1

2 9- =

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

Identify Slope < 20 degrees(Alternative method)

“Slope” < 20

“suitable aspect” * “suitable slope”

suitable_habitat

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

Land cover

Digital Elevation Model (DEM)

Raster Sources• Scanned

– Topos• Remotely Sensed

– Aerial Photos– Satellite Photos– Digital Elevation Models (DEM)

• Derived Rasters– Hill shade– Slope– Aspect– Statistical Spatial Analysis

Digital Raster Graphic

Digital Elevation Model (DEM)

Each pixel value is an elevation

Digital Orthophoto Quarter Quad (DOQQ)1 meter aerial photos

Flight Characteristics

LandSat• 7 Bands• 30m, 15m bw• Entire earth

• Twice a month• 26 years of

coverage • “Free”• EROS Data Center

National Land Cover Dataset (NLCD)Based on Landsat Imagery

21 Classes based on cover type

1992

2001

2006

Derived Rasters

• Land Cover from satellite and aerial• Topography: Slope, aspect, hillshade• Ecoregions• Suitable Habitat• Flood plains• Geological Regions

Shaded Relief (a.k.a Hillshade)

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.

Zonal Statistics

Esri ArcGIS 10 Help

Zone Layer

Value layer

Output layer (or table)

• Con(<condition>,<true>,<false>)• Given a raster “condition”:

• Puts the true value where true and false value where false

Conditional Operator

Con!

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

Elevations over 3000 meters

Reclassify

Mosaic Rasters• Data Management Tools -> Raster -> Raster Dataset -> Mosaic to new

Raster

Mosaic To Raster