fundamentals of gis lecture materials by austin troy, brian voigt and weiqi zhou except where noted...

Fundamentals of GIS

Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

Lecture 5:Introduction to Raster Analysis

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By Austin Troy, Brian Voigt and Weiqi Zhou

University of Vermont

Fundamentals of GIS

Raster data-A RefresherRaster Elements–Extent –# rows –# columns –Coordinates –Origin –Orientation–Resolution –Grid cell


Fundamentals of GIS

Raster Data Structure• Methods for storing raster data in a more

computationally and memory efficient way.• Where a raster layer is random noise, this does not

work.• Requires repetitive patterns or areas of homogeneity.• The fewer z values, the easier to compress.• Simplest method is cell-by-cell encoding where cell

values are stored by row and column number; This is essentially uncompressed.

• DEM’s and satellite images generally use this structure because there is typically so much variation.


Fundamentals of GIS

Raster Data Structure• Run-length encoding (RLE):

– Compression method that records cell values in groups called “runs.”

– It records the starting and ending pixel for a “run” with the same value for a given row, so hundreds of pixels could be recorded with only two values, if they all have the same value and are adjacent.

– However, because it measures runs along rows, it is not efficient for two dimensional areas of homogeneity.

– RLE can reduce file size by 10:1, depending on data.


Fundamentals of GIS

Raster Data Structure

• Runs:– Row 2: 3,4– Row 3: 2, 8– Row 4: 4,7– Row 5: 5,7– Row 6: 2,6


Fundamentals of GIS

Raster Data Structure• Chain code:

– This is a more efficient method for dealing with two-dimensional compression

– This defines a homogeneous two-dimensional area using cardinal directions and units movements to define bounding perimeter in relative terms from a known point

– For instance, go 2 N, 1 W, 1N, 3 W, 1S….etc.


Fundamentals of GIS

Raster Data Structure• Here, starting from the

lower left, the computer would define that coordinate then code 1N, 3E, 1N, 1W, 1N, 2W, 1N, 1E, 1N, 2E etc…..

• This would define the perimeter of a homogeneous area.

• All must have exactly the same value


Fundamentals of GIS

Raster Data Structure• Block code:

– A method that uses square blocks to represent areas of homogeneous values

– Each block is encoded only with location of one corner cell and the dimensions; since they are square, only one dimension needs to be given

– Uses medial axis transformation technique


Fundamentals of GIS

Raster Data Structure• Quad tree:

– Divides a grid into hierarchy of quadrants– Starts with four quadrants; any quadrant that has totally

homogeneous cells will not be subdivided further, but is stored as a “lead node” which is coded only with that value and the id of the quadrant.

– Any quadrants with more than one value are subdivided again into four more quadrants and again the computer checks for homogeneity.

– It keeps on doing this until it has generated all its leaf node or until it gets down to the pixel level

– This is known as recursive decomposition– This is good where one part of a grid is very uniform and the

rest is heterogeneous.


Fundamentals of GIS


Homogeneous

(all one value)

Not homogeneous: more than one value within quadrant


Fundamentals of GIS

Raster Data Structure• Quad tree: now we break down those quadrants

with non-homogeneous values into four sub quadrants



Fundamentals of GIS

Raster Data Structure• Quad tree: and we keep doing this until we’ve come

down to the point where there are only homogeneous quadrants, even if those are one cell in dimension



Fundamentals of GIS


One value (leaf node)Mixed values (non-leaf)


Fundamentals of GIS

Converting Features to Rasters

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Fundamentals of GIS

Converting Vector to Raster

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Fundamentals of GIS

Raster Overlay Queries•The raster data model performs overlay operations more efficiently than the vector model•Raster cells have a one-to-one relationship between layers •Raster overlay queries: combining two or more thematic layers to identify relationships between them such as:

–Areas that are common to all layers–Areas that meet criteria from each layer

Query example:

(“elevation” > 2500) & (“Slope” > 20)

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Fundamentals of GIS

Raster Overlay Calculations

•Map algebra can be performed to identify relationships between layers, or to derive indices that describe phenomena•Map algebra calculations create a new output raster•Example:

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(“Soil_depth_1990”) – (“Soil_depth_2000”) = Soil loss 1990 - 2000


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Source: ESRI


Fundamentals of GIS

Map Query: Example 1

• Single layer numeric example: lu_chit = 11 (residential)

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Fundamentals of GIS


Lecture Materials by Austin Troy except where noted © 2008

Fundamentals of GIS

Map Query: Example 2• Single layer numeric example: lu_chit = 11

(residential)


Fundamentals of GIS



Fundamentals of GIS

Map Query: Example 3• Con Function• Results in a binary True/False layer


Fundamentals of GIS


Fundamentals of GIS

Map Query Examples• Multi-criteria, single layer, categorical map query: looking for all

developed land use types, using attribute codes (11, 12, 13) and the OR logical operator

• Results in a 1/0 binary layer, showing urbanized areas

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Vertical lines mean OR


Fundamentals of GIS

Map Query Examples

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Fundamentals of GIS

Map Query ExamplesOne can then convert this to a feature class or shapefile

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Fundamentals of GIS

Raster Query: 2 Layer ExampleMulti-layer queries use criteria across two or more layers; in this case we’ll query land use (categorical), elevation (number) and slope (number)

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Let’s say we want to find identify potential habitat for a rare plant that grows at higher elevation, on steeper slopes and in coniferous forest


Fundamentals of GIS

Raster Query: Slope

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Fundamentals of GIS

Raster Query: Multiple Criteria• Multiple criteria, multiple layers

• Land Cover = Coniferous Forest (42)• Elevation > 800• Slope > 20%

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Fundamentals of GIS


Fundamentals of GIS

Map CalculationWe can also make calculations between layers (or between a layer and a constant): here we’ll multiply the k factor (soil erodibility factor) by slope; let’s just imagine this will yield a more accurate and spatially explicit index of erodibility that factors in slope at each pixel

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Fundamentals of GIS


Map Calculation• Darker areas feature both steep slopes and erodible soils. • Advantage over map query approach: result is a continuous

index of values, rather than just a “true” / “false” dichotomy

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Fundamentals of GIS

Map Calculation and Query• We could then run a map query to find areas that have high

erodibility factors and urban land use.


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Fundamentals of GIS

Zonal Statistics• Suppose we had a proposed subdivision map (this one is

made up). We could overlay it on our new index to determine which proposed subdivisions are problematic (due to soil erodibility).

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Fundamentals of GIS

Zonal Statistics• Summarize the mean,

max or sum for some value within each of the bounding units

• Polygon and Raster• Raster and Raster• Here we summarize by

subdivision zones the mean soil erodibility value (from our calculation).

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Fundamentals of GIS

Zonal Statistics

Produces a DBF table with the specified summary statistics


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Fundamentals of GIS

Zonal StatisticsNow we can plot out the subdivision boundaries (zones) by a soil erosion statistic. In this case we plot subdivision boundaries shaded by the mean of the soil erosion statistic. This represent the mean value of all the soil erosion pixels underlying a polygon


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Fundamentals of GIS

Reclassifying Raster Data

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Fundamentals of GIS

Neighborhood Statistics


Fundamentals of GIS

Low Pass Filter• Functionality: averaging filter

– Emphasize overall, general trends at the expense of local variability and detail.

– Smooth the data and remove statistical “noise” or extreme values.

• Summarizing a neighborhood by mean or median– The larger the neighborhood, the more you smooth, but the

more processing power it requires.– A circular neighborhood: rounding the edges of features.– Resolution of cells stays the same.


Fundamentals of GIS

High Pass Filter• Functionality: edge enhancement filter

– Emphasize and highlight areas of tonal roughness, or locations where values change abruptly from cell to cell

– Emphasize local detail at the expense of regional, generalized trends

• Perform a high pass filter– Subtracting a low pass filtered layer from the original– Summarizing a neighborhood by standard deviation– Using weighted kernel neighborhood


Fundamentals of GIS

Neighborhood Statistics

• Min, max, mean, standard deviation, range, sum, variety

• Window size/shape


Fundamentals of GIS


• Filtering out anomalies in bathymetric data

Bathymetry mass points: sunken structures

Low Pass Filter: Example

Fundamentals of GIS


• After turning into raster grid

We see sudden anomaly in grid

Say we wanted to “average” that anomaly out

Fundamentals of GIS


• Try a low-pass filter of 5 cells

We can still see those anomalies but they look more “natural” now

Fundamentals of GIS


• Try a low-pass filter of 25 cells

The anomalies have been “smoothed out” but at a cost

Fundamentals of GIS


• We can also do a local filter in that one area

Fundamentals of GIS


What about high pass filters?• Say we wanted to isolate where the wreck was

All areas of sudden change, including our wrecks, have been isolated

Fundamentals of GIS

Low pass filter for elevation


Fundamentals of GIS

A low pass filter of the DEM done by taking the mean values for a 3x3 cell neighborhood: notice it’s hardly different

DEM Low pass


Fundamentals of GIS

10 unit square neighborhood


Fundamentals of GIS

20 unit square neighborhood


Fundamentals of GIS

In this high-pass filter the mean is subtracted from the original

It represents all the local variance that is left over after taking the means for a 3 meter square neighborhood


Fundamentals of GIS

We do this using Spatial Analyst Tools >>> Math >>> Minus


Fundamentals of GIS

If we do a high-pass filter by subtracting from the original the means of a 20x 20 cell neighborhood, it looks different because more local variance was “thrown away” when taking a mean with a larger neighborhood

Dark areas represent things like cliffs and steep canyons


Fundamentals of GIS

Using standard deviation is a form of high-pass filter because it is looking at local variation, rather than regional trends. Here we use 3x3 square neighborhood


Fundamentals of GIS

• Note how similar it looks to a slope map because it is showing standard deviation, or normalized variance, in spot heights, which is similar to a rate of change.

• Hence it is emphasizing local variability over regional trends.• The resolution of the slope is quite high because it is sampling

only every nine cells.• When we go to a larger neighborhood, by definition, the

resulting map is much less detailed because the standard deviation of a large neighborhood changes little from cell to cell, since so many of the same cells are shared in the neighborhood of cell x,y and cell x,y+1.


Fundamentals of GIS

Here is the same function with an 8x8 cell neighborhood.

The coarser resolution (due to the larger neighborhood) makes it so that slope rates seem to vary more gradually over space


Fundamentals of GIS

Later on we’ll look at filters and remote sensing imagery, but here is a brief example of a low-pass filter on an image that has been converted to a grid. This can help in classifying land use types


Fundamentals of GIS

Raster Surface ToolsArc GIS allows you to use a digital elevation model (DEM) to derive:

•Hillshade•Slope•Contours•Aspect


Fundamentals of GIS

Raster Surface ToolsDEM + Hillshade = Hillshaded DEM

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+ =


Fundamentals of GIS


Display Options1. Place the hillshade “under” the DEM in the TOC

2. Make the DEM partially transparent

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Fundamentals of GIS

Raster Surface Tools


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Slope Contour Aspect

Fundamentals of GIS

Viewshed Analysis• This is a multi-layer function that analyzes visibility based on terrain.• It requires a raster terrain layer and a point layer and produces a

visibility layer (raster) that tells you where the feature can be seen from, or alternately, what areas someone standing at that feature could see (remember, line of sight is two way).

• If there are more than one point feature, then each grid cell tells you how many of the point features can be seen from a given point.

• However in that case, you lose information about the other direction; You don’t know which features can see a particular grid cell.

• Viewshed analysis can use “offsets” to define the height of the viewer or of the object being viewed; designated using a new field in the input layer’s attribute table.


Fundamentals of GIS

Viewshed AnalysisLet’s say we’re local planners who are considering sites

for a new waste treatment facility in a valley where the vacation homes of five rich and powerful executives are located.

We want it in a place that won’t ruin anyone’s views, since they comprise 95% of the local tax base.

This generates a grid with three values, representing how many houses can see a given pixel


Fundamentals of GIS

Viewshed AnalysisRed represents areas that can be seen by 1 house, blue by 2 or more


Fundamentals of GIS

Viewshed AnalysisIn order to compare the viewability of several facilities, separate

viewshed analyses need to be done for each feature.

In the next example we will look at three candidate sites for a communications tower.

Each will produce a viewability grid.

This grid can then be superimposed on a layer showing residential areas.

Since each grid will belong to a different tower, we can tell which tower will be most viewable from the residential areas through simple overlay analysis.


Fundamentals of GIS

Viewshed AnalysisIn this case, red is for tower 1, blue for 2 and green for 3


Fundamentals of GIS

Proximity• Can use raster distance functions to create zones based on

proximity to features; here, each zone is defined by the closest stream segment

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Fundamentals of GIS

Distance Measurement

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• Can create distance grids from any feature theme (point, line, or polygon)


Fundamentals of GIS

Distance Measurement• Can also weight

distance based on friction factors, like slope

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Fundamentals of GIS

Combining Distance and Zonal Stats

• Can also summarize distances by vector geography using zonal stats

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Fundamentals of GIS

Combining Distance and Zonal Stats

• Here we summarize by the mean

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Fundamentals of GIS

Density Functions• We can also use sample points to map out density raster surfaces. This need to

require a z value in each, it can simply be based on the abundance and distribution of points.

• Pixel value gives the number of points within the designated neighborhood of each output raster cell, divided by the area of the neighborhood


Fundamentals of GIS

Density Functions


fundamentals of gis lecture materials by austin troy, brian voigt and weiqi zhou except where noted...

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