nr 422: raster analysis jim graham spring 2010. continuous vs. categorized continuous: –like...

NR 422: Raster Analysis

Jim Graham

Spring 2010

Continuous vs. Categorized

• Continuous:– Like photographs– Satellite and aerial photos– Best for analysis

• Categorized or discrete– Land Cover– Eco-regions– Limited analysis– Careful on precision and accuracy

Categorical vs. Continuous

“No-Data” or NULL Values

• Rasters are always rectangular

• No-Data values are “transparent” and are not used for calculations

Geo-Referenced Raster

• Known Projection and Datum

• Width and height of a pixel in map units

(X1,Y1)

Width in Pixels

Height in pixels

Geo-Referenced Raster

• Known Projection and Datum

(X1,Y1)

(X3,Y3)

Types of Rasters

• Digital Elevation Model (DEM)

• Digital Raster Graphic (Topos)

• Satellite and Aerial Photos

• Land Cover & other natural characteristics

• Cost Distance & other economic

• Population, taxes, etc.

• Your own!

Digital Raster Graphic

Digital Elevation Model (DEM)

Hill-shade

Contours

Contours

DEM Cross Section

2000m

1900m

2100m

2200m

Slope

Calculating SlopeDEM Cross Section

Aspect – Direction of the slope

Slope

Aspect (Direction)

Angle

Rise

Run

Slope = (Rise/Run) * 100%

Aspect

Hill-shade

Creating a Hillshade

• Obtain a DEM

• Crop to Desired Area

• Create Hillshade

• Apply color ? To DEM

• Add DEM over Hillshade with Transparency

• Screen shots:– Hillshade dialog– Colorizing dems– Transparency

Spatial Analyst Extension

• Make sure “Spatial Analyst” is Checked

Tool Bar

• Right-click in the menu area

• Select “Spatial Analyst”

Hill-shading

• Azimuth: “Direction” of the sun relative to the ground. 0 is north.

• Altitude: Angle from the horizon to the sun.

North

Azimuth

Altitude

Horizon

“Colorize” the DEM

Make the Hillshade Transparent

Continuous vs. Categorized

• Continuous:– Like photographs– Satellite and aerial photos– Best for analysis

• Categorized or discrete– Land Cover– Eco-regions– Limited analysis– Careful on precision and accuracy

“GeoReferenced” File Formats

• GRID: ESRI’s format

• GeoTIFF: Excellent support

• MrSID: LizardTech

• IMG: ERDAS

• ECW: ERMapper

• BIL, BIP, BSQ: See header (w/prj)

• “ASCII” or “GRID ASCII” (w/prj)

World Files

• Contains:– X-dimention Pixel size in map units– Y-axis rotation– X-axis rotation– Y- dimension Pixel size in map units

(negative)– X-coordinate of upper-left pixel– Y-coordinate of upper-left pixel

• Image file contains width and height

Not Geo-Referenced

• BMP

• PNG

• GIF

• JPEG

• Maybe with a world file and prj file?

JPEG

• Joint Photographic Experts Group

• Widest used photo format

• Not for use with vectors

• JPEG2000– Completely new format!– Can be georeferenced

Edge of Rocky Mountain National Park Boundary with high JPEG compression

Tagged Image File Format

• TIFF• Can be georeferenced (GeoTIFF)

– Can tell in ArcCatalog or ArcMap

• TIFF w/world file– Also need Projection and Datum (prj?)

• Can be compressed– Run-length – Categorical data– LZW – Categorical data– Huffman encoding – Categorical data– JPEG- Continuous data (don’t used on

Categorical data!)

GRIDS

• ESRI’s native raster format

• Pyramids

• Not an exchange format!

ASCII format• NCOLS 10• NROWS 9• XLLCORNER 1000• YLLCORNER 1000• CELLSIZE 1• NODATA_VALUE -9• -9 -9 1 1 0 1 0 1 -9 -9• -9 -9 1 1 2 2 2 1 1 -9• -9 1 1 1 2 2 2 2 3 3 • Etc.• See example

Types of Rasters

• Land Cover: forest, grass, water, roads, urban

• Digital Elevation Model: DEM

• Aerial Photos

• Satellite Photos

• Scanned: DRG, 24k Topos

• Derived rasters: lots!

Derived Rasters

• Land Cover from satellite and aerial

• Topography: Slope, aspect, hillshade

• Ecoregions• Suitable Habitat• Flood plains• Geological Regions

Raster To Vector

• Satellite & Aerial– Land Cover: roads, forests, etc.– Buildings

• DEMs– Contours– Peaks & Valleys– Stream Networks– Watersheds

Vector To Raster

• Drawing!

• Points of interest

• Roads

• Water bodies

• Contours

GIS Analysis

AnalysisResults

Raster toVector

Vector toRaster

Raster Analysis

• Topography: Slope, aspect, contours

• Raster Math

• Statistics: min, max, mean, std. dev.

• Distance

• Density

• Interpolation

• Classification

• Raster / Vector Conversions

Raster Math

• A matrix of pixels

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

Spatial Analyist

Analysis Environment

• Spatial Reference (Coordinate System)– Make them the same

• Extent– Area of interest– All rasters should overlap

• Cell Size– Largest of all rasters or larger

Spatial Analyst: General

Spatial Analyst: Extent

Spatial Analyst: Cell Size

Raster Calculator

Raster Math

1 2

2 3

12 9

13 10

13 11

15 13+ =

+ =1 12 13

Common Functions

• Local:– Arithmatic: +,-,/, *,

• 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)

Mathematical Functions

• 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

Exponents• Exp()• Exp10()• Ln()• Log10()• Max()• Min()• Pow()• SetNull()• Sqrt()• Sum()

Comparisons

• <> (Not Equals)

• == (Equals)

• < (Less than)

• <= (Less than or equal to)

• > (Greater than)

• >= (Greater than or equal to)

Raster Math: Comparisons

1 2

2 3

2 2

3 2

0 0

0 1> =

> =1 2 0

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 “!”

Conditional Operator

• Con(<condition>,<true>,<false>)

• Given a raster “condition”:– Puts the true value where true and false

value where false

• Example:– Find the elevations in Rocky over 3000

meters– HighElevations=con(RockyDEM>3000,1,0)

Elevations over 3000 meters

Elevations over 3000 meters

Viewshed

• Shows which “pixels” can be seen from pre-defined locations

View-shed

View from Estes Park

View from Ridge