1. Lecture 2: Review of Raster Operations
------Using GIS--
Lecture 2: Review of Raster Operations
By Austin Troy & Brian Voigt

2. Raster data-A Refresher
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Raster data-A Refresher
Raster Elements
Extent
# rows
# columns
Coordinates
Origin
Orientation
Resolution
Grid cell

3. Map Query
Single layer numeric example: lu_chit = 11 (residential)

4. Map Query

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Map Query
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
Vertical lines mean OR

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Map Query

7. Map Query One can then convert this to a feature class or shapefile
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Map Query
One can then convert this to a feature class or shapefile

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

9. Map Query: 2 Layer Example
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Map Query: 2 Layer Example
Multiple criteria, multiple layers
Land Cover = Coniferous Forest (42)
Elevation > 800
Slope > 20%

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Map Calculation
We 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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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

13. Map Calculation and Query
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Map Calculation and Query
We could then run a map query to find areas that have high erodibility factors and urban land use.
Lecture Materials by Austin Troy except where noted © 2008

14. Con function Uses Raster Calculator/map algebra interface
Con(<condition>, <true_expression>, <false_expression>)
Single output values, one condition
OutRas = Con(Elev > 5000, 10, 1)
Single output values, multiple conditions
OutRas = Con(Elev > 5000 & (LU==41,| LU==42), 10, 1)
Multiple output values, multiple false conditions
OutRas = Con(Elev > 5000 & (LU==41,| LU==42), 43, LUras)
Multiple output values, multiple false & true conditions
OutRas = Con(Elev > 5000 & (LU==41,| LU==42), HighElevForRas, LUras)

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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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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).

17. Produces a DBF table with the specified summary statistics
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Zonal Statistics
Produces a DBF table with the specified summary statistics

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Zonal Statistics
Now 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

19. Reclassifying Raster Data
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Reclassifying Raster Data

20. Reclassifying Raster Data
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Reclassifying Raster Data

21. Reclassifying Raster Data
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Reclassifying Raster Data

22. Neighborhood Statistics
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Neighborhood Statistics

23. Low Pass Filter Functionality: averaging filter
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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.

24. High Pass Filter Functionality: edge enhancement filter
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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

25. Neighborhood Statistics
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Neighborhood Statistics
Min, max, mean, standard deviation, range, sum, variety
Window size/shape

26. Low Pass Filter: Example
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Low Pass Filter: Example
Filtering out anomalies in bathymetric data
Bathymetry mass points: sunken structures
Lecture Materials by Austin Troy except where noted © 2008

27. After turning into raster grid
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After turning into raster grid
We see sudden anomaly in grid
Say we wanted to “average” that anomaly out
Lecture Materials by Austin Troy except where noted © 2008

28. High pass filter Say we wanted to isolate where the wreck was
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High pass filter
Say we wanted to isolate where the wreck was
All areas of sudden change, including our wrecks, have been isolated
Lecture Materials by Austin Troy except where noted © 2008

29. Low pass filter for elevation
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Low pass filter for elevation

30. 10 unit square neighborhood
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10 unit square neighborhood

31. 20 unit square neighborhood
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20 unit square neighborhood

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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

33. Can also do filters with remote sensing imagery.
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Can also do filters with remote sensing imagery.

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Raster Surface Tools
Arc GIS allows you to use a digital elevation model (DEM) to derive:
Hillshade
Slope
Contours
Aspect

35. lecture 9 DEM + Hillshade = Hillshaded DEM
Raster Surface Tools
DEM Hillshade = Hillshaded DEM + =

36. Raster Surface Tools Aspect Slope Contour lecture 9
Lecture Materials by Austin Troy except where noted © 2008

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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.

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Viewshed Analysis
Let’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

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

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Viewshed Analysis
In 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.

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

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

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Distance Measurement
Can also weight distance based on friction factors, like slope

45. Combining Distance and Zonal Stats
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Combining Distance and Zonal Stats
Can also summarize distances by vector geography using zonal stats

46. Combining Distance and Zonal Stats
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Combining Distance and Zonal Stats
Here we summarize by the mean

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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

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Density Functions

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Density Functions