# raster analysis - gis courses .raster analysis raster cells store data (nominal, ordinal,...

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• Raster Analysis

Raster cells store data (nominal, ordinal, interval/ratio)

•Complex constructs built from raster data

Connected cells can be formed in to networks

Related cells can be grouped into neighborhoods or regions

Examples:

Predict fate of pollutants in the atmosphere

Animal migrations

Crop yields

EPA - hazard analysis of urban superfund sites

Local to global scale forest growth analysis

• Raster

operations

require a

special set

of tools

• Raster Analysis

Map algebra Concept introduced and developed by by Dana Tomlin and

Joseph Berry (1970’s)

Cell by Cell combination of raster data layers

Each number represents a value at a raster cell

location

Simple operations can be applied to each number

Raster layers may be combined through operations

• Scope: Local operations

• Scope: Neighborhood operations

• Scope: Global operation

• Many Local

Functions

(page412 of book)

• Logical Operations

AND Non-zero values are “true”, zero values are “false”

N = null values

Pg 412 of book

• Logical Operations

OR Non-zero values are “true”, zero values are “false”

N = null values

Pg 412 of book

• Logical Operations

NOT

• More Local Functions – logical comparisons

(pg 414 of book)

• Conditional

Function

• Nested

Functions

no yes

Output= Con(ISNULL(LayerA), LayerC, LayerB)

• Neighborhood

Operations

Moving Windows (Windows can be any size;

often odd to provide a center)

• Neighborhood

Operations

• Neighborhood Operations: Mean Function

• Neighborhood Operations: Separate edge kernals can be used

• Example:Identifying

spatial differences in

a raster layer

• Raster Analysis

Moving windows and kernals can be used with a mean

kernal to reduce the difference between a cell and

surrounding cells. (done by average across a group of cells)

Raster data may also contain “noise”; values that are large

or small relative to their spatial context. (Noise often requiring correction or smooth(ing))

Know as “high-pass” filters

The identified spikes or pits can then be corrected or

removed by editing

• Raster Analysis

High pass filters

Return:

•Small values when smoothly changing values.

•Large positive values when centered on a spike

•Large negative values when centered on a pit

• 35.7

• Mean filter

applied

Note edge erosion

• Moving windows: Consider the overlap in cell calculations

Neighborhood operations often

Increase spatial covariance

• Overlay in Raster

Union and Clip

Cell by Cell Addition or Multiplication

Attribute combinations corresponding to

unique cell combinations

• Raster Clip or “Mask” (used in Lab 10)

What if you

only want

certain cells?

• Raster Clip or “Mask” (used in Lab 10)

Note: removed cell output values could be

0 or N depending of the GIS software

used.

• Raster zonal function

• A Problem with Raster

Analysis

• Too many cells

• Typically, one-to-one relationship between

spatial object and attribute table

• Rasters have multiple cells per feature

• Attribute tables grow to be unwieldy

• Vector Raster

• Output layer DOES NOT

have unique records Raster Overlay

• What to do? First multiply Layer A by 10

• Cost Surface

The minimum cost of reaching cells in a layer from

one or more sources cells

“travel costs” Time to school; hospital;

Chance of noxious foreign weed spreading out from an introduction point

•Units can be money, time, etc.

•Distance measure is combined with a fixed cost per unit

distance to calculate travel cost

•If multiple source cells, the lowest cost is typically placed in

the output cell

• Friction Surface (version of a Cost Surface)

The cell values of a friction surface represent the cost per unit

travel distance for crossing each cell – varies from cell to cell

Used to represent areas with variable travel cost.

Notes:

•Multiple paths are often not allowed

•Cost and Friction Surfaces are always related to a source

cell(s); “from something”

•The center of a cell is always used the distance calculations

• Digital Elevation Models &

Terrain Analysis

• Terrain determines or influences:

- natural availability and location of surface water,

and hence soil moisture and drainage.

- water quality through control of sediment

entrainment/transport, slope steepness.

- direction which defines flood zones, watershed

boundaries and hydrologic networks.

- location and nature of transportation networks or

• Digital Elevation Models

•Used for: hydrology, conservation, site planning, other

infrastructure development.

•Watershed boundaries, flowpaths and direction, erosion

modeling, and viewshed determination all use slope and/or

aspect data as input.

•Slope is defined as the change is elevation (a rise) with a

change in horizontal position (a run).

•Slope is often reported in degrees (0° is flat, 90° is vertical)

• Formats - Contour Elevation Data

• Source Independent

• USGS topo maps

• Contour shows a

line of constant

elevation

• Generally used

more as a

cartographic

representation

From Sean Vaughn, MNDNR

• DEM’s consist of an array representing

elevation values at regularly spaced intervals

commonly known as cells.

ELEVATION

VALUES (ft)

Formats - Digital Elevation Models

X

Y

Z

From Sean Vaughn, MNDNR

• DEM = Raster = Grid

Digital Elevation Models

Raster (Format)

DEM = Grid vs. Vector data format

From Sean Vaughn, MNDNR

• DEM Structure • Each cell usually

stores the average

elevation of grid cell.

• Typically they store

the value at the

center of the grid cell.

• Elevations are

presented graphically

67 56 49

53 44 37

58 55 22

D ig

it a l

G ra

p h ic

a l

Digital Elevation Models

From Sean Vaughn, MNDNR

• DEMs are a common way of representing elevation where every

grid cell is given an elevation value. This allows for very rapid

processing and supports a wide-array of analyses.

Digital Elevation Models

From Sean Vaughn, MNDNR

•  Resolution

 30 Meter

 USGS produced from Quad Hypsography.

 DNR published format in MN.

 Course resolution

 10 Meter

 Interpolated

 Resampled

52

Previously Published National DEMs

From Sean Vaughn, MNDNR

•  Resolution

 1 Meter

 3 Meter

 Most common published format

in MN.

 Storage requirements & faster

drawing speeds. 53

Previously Published National DEMs

• Lower resolution = Faster processing

• Higher resolution = Maintain small features

1-meter DEM claims 9-

times more process

resources and storage

than a 3-meter DEM

From Sean Vaughn, MNDNR

• Viewshed

The viewshed for a point is the collection

of areas visible from that point.

Views from any non-flat location are blocked by

terrain.

Elevations will hide a point if they are higher than the

viewing point, or higher than the line of site between

the viewing point and target point

• not

•  The azimuth is the

angular direction of the

sun.

 Measured from north in

clockwise degrees from 0

to 360.

 The altitude is the slope or

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