raster analysis and terrain analysis chapter 10 & 11 raster analysis

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• Raster Analysis and Terrain AnalysisChapter 10 & 11Raster Analysis

• Raster Data Model

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

Excellent for terrain and hydrological modelingComplex constructs built from raster data-Connected cells can be formed in to networks -Related cells can be grouped into neighborhoods or regions

• Examples of Raster Analysis

Predict fate of pollutants in the atmosphereThe spread of diseaseAnimal migrationsCrop yieldsEPA - hazard analysis of urban superfund sitesMarket analysisWatershed analysisTerrain analysis

• Map algebraConcept introduced and developed by Dana Tomlin and Joseph Berry (1970s)

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 such as addition, subtraction and multiplication

• Scope: Local operations

• Many Local Functions(page 412 of text)

• Logical OperationsANDNon-zero values are true, zero values are falseN = null values

• Logical OperationsORNon-zero values are true, zero values are falseN = null values

• Logical OperationsNOT

• More Local Functions logical comparisons

• An Example of a Logical Operation

• Reclassification

• ConditionalFunction

• Nested Functions

• Overlay

• Raster Clip Operation

• Be Careful of Ambiguity

• Be Careful of Ambiguity

21315123741322113

• Raster Overlay in Idrisi

• First * Second0*0=00*1=01*1=1

101101101

111001001

101001001

• First + Second0+0=00+1=11+1=2

101101101

111001001

212102102

• Scope: Neighborhood operations

• Neighborhood Operations

Moving Windows(Windows can be any size; often odd to provide a center)

• Kernels vs. Moving Window

• Neighborhood Operations: Separate edge kernals can be used

• Neighborhood Operations

• 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

• Zonal Functions

• Scope: Global operation

• ArcMaps Raster CalculatorFrom ArcGIS Help Files

Raster Calculator tool dialog box example

• Cost Surface

The minimum cost of reaching cells in a layer from one or more sources cells

travel costsTime 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 cellUsed to represent areas with variable travel cost.

Notes:Barriers can be added. Multiple paths are often not allowedCost and Friction Surfaces are always related to a source cell(s); from somethingThe center of a cell is always used the distance calculations

• Friction Surface

• Terrain Analysis

• Digital Elevation Models (DEM)/Terrain Analysis

Terrain determines the natural availability and location of surface water, and hence soil moisture and drainage.

Water quality through control of sediment entrainment and transport, slope steepness and direction defines flood zones, watershed boundaries and hydrologic networks.

Terrain also strongly influences location and nature of transportation networks or the cost and methods of house and road construction.

• Digital Elevation ModelsTerrain AnalysisSlope and Aspect

Used for: hydrology, conservation, siteplanning, other infrastructure development.

Watershed boundaries, flowpaths and direction, erosion modeling, and viewsheddetermination 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)

• Slope (continued)

• Slope (continued)Measured in the steepest direction of elevation change

Often does not fall parallel to the raster rows or columns

Which cells to use?

Several different methods:

Four nearest cells3rd Order Finite Difference

• Slope (continued)

Elevation is ZUsing a 3 by 3 (or 5 by 5) moving windowEach cell is assigned a subscript and the elevation value at that location is referred to by a subscripted Z value

The most common formula:

• Slope (continued)for ZoZ/x = (49 40)/20 = 0.45

Z/y = (45 48)/20 = -0.15

• Slope (continued)Slope calculation base on cells adjacent to the center cellThe distance is from cell center to cell centerfor ZoZ/x = (49 40)/20 = 0.45

Z/y = (45 48)/20 = -0.15Generalized formula forZ/x and Z/y

Z/x = (Z5 Z4)2*Z/y = (Z2 Z7)2*

Using the four nearest cells

* = times cell width

• Slope (continued)Z/x = (49 40)/20 = 0.45Z/y = (45 48)/20 = -0.15Kernal for Z/xKernal for Z/yMultiply (kernal, cell by cell)Add (results)Divide by #cells x cell widthUse slope formula

• Multiply (kernal, cell by cell)Add (results)Divide by #cells x cell widthUse slope formula

• Slope in ArcGIS 10From ArcGIS 10 Help

• Aspect

• Aspect

The orientation (in compass angles) of a slope

Calculation:

Aspect = tan-1[ -(Z/y)/(Z/x)]

As with slope, estimated aspect varies with the methods used to determine Z/x and Z/y

Aspect calculations also use the four nearest cell or the 3rd-order finite difference methods

• Aspect in ArcGIS 10From ArcGIS 10 Help

• Curvature

• ViewshedThe viewshed for a point is the collectionof areas visible from that point.

Views from any non-flat location are blocked byterrain.

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

• From ArcGIS 10 Desktop HelpFlow Direction

• Raster Analysis

High pass filters

Return: Small values when smoothly changing values.Large positive values when centered on a spikeLarge negative values when centered on a pit

• High Pass FilterRaster data may also contain noise; values that are large or small relative to their spatial context.A mean kernal is used to reduce the difference between a cell and surrounding cells. (done by average across a group of cells)The identified spikes or pits can then be corrected or removed by editing

• 35.7

• Watershed

An area that contributes flow to a point on the landscapeWater falling anywhere in the upstream area of a watershed will pass through that point.

Many be small or large

Identified from a flow direction surface

Drainage network

A set of cells through which surface water flows

Based on the flow direction surface

• Routing and AllocationRoutingFinding the shortest path between any nodes in a networkOptimal pathEach link in the net can also be assigned an impedance valueUsing an accumulated distance and the impedance factorMost efficient route can be found, rather than just the shortestNodes can also be coded with stops and barriersPreventing movement and forcing traffic along another pathAlthough routing can be done in raster, it is much easier if performed in a vector system

• Routing and AllocationAllocation Process used to define the areal extent of services areasService areas are defined around a siteRegion is formed that includes a defined areaLocation/allocation model (optimizes network efficiency)Technique for the evaluation of multiple facility locations Determining the configuration of facilities (location)Assigning demand for the facilities (allocation)

****Incompatible cell sized and boundaries can cause problems with multi-layer raster operations. What cell value would you choose from layer 2 to combine with layer 1n and a portion of cell B is undefined.

Ambiguities are best resolved by resampling prior to layer combination*As with vector analysis, the scope of raster analysis can be classified as local , neighborhood or global

*****Raster reclassification assigns output values that depend on a specific st of input values.**Many differences between vector and raster overlay due to differences in the data models. Raster overlay is often restricted to nominal data. Overlay with continuous data creates to many unique cell combinations to be of much use,The output layer includes only source values indicated by the output layer.*Vector neighborhoods are not well-defined and depends upon the size and shape of the feature and neighboring feature, Raster neighborhood is better defined.

****This is an edge detection operation The first kernel amplifies differences in the x direction th