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

Raster data-A Refresher lecture 9 Raster data-A Refresher Raster Elements Extent # rows # columns Coordinates Origin Orientation Resolution Grid cell

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

Map Query

lecture 9 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

lecture 9 Map Query

Map Query One can then convert this to a feature class or shapefile lecture 9 Map Query One can then convert this to a feature class or shapefile

Map Query: 2 Layer Example lecture 9 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

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

lecture 9

lecture 9 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

lecture 9 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

Map Calculation and Query lecture 9 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

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)

lecture 9 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).

lecture 9 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).

Produces a DBF table with the specified summary statistics lecture 9 Zonal Statistics Produces a DBF table with the specified summary statistics

lecture 9 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

Reclassifying Raster Data lecture 9 Reclassifying Raster Data

Reclassifying Raster Data lecture 9 Reclassifying Raster Data

Reclassifying Raster Data lecture 9 Reclassifying Raster Data

Neighborhood Statistics lecture 9 Neighborhood Statistics

Low Pass Filter Functionality: averaging filter lecture 9 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.

High Pass Filter Functionality: edge enhancement filter lecture 9 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

Neighborhood Statistics lecture 9 Neighborhood Statistics Min, max, mean, standard deviation, range, sum, variety Window size/shape

Low Pass Filter: Example lecture 9 Low Pass Filter: Example Filtering out anomalies in bathymetric data Bathymetry mass points: sunken structures Lecture Materials by Austin Troy except where noted © 2008

After turning into raster grid lecture 9 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

High pass filter Say we wanted to isolate where the wreck was lecture 9 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

Low pass filter for elevation lecture 9 Low pass filter for elevation

10 unit square neighborhood lecture 9 10 unit square neighborhood

20 unit square neighborhood lecture 9 20 unit square neighborhood

lecture 9 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

Can also do filters with remote sensing imagery. lecture 9 Can also do filters with remote sensing imagery.

lecture 9 Raster Surface Tools Arc GIS allows you to use a digital elevation model (DEM) to derive: Hillshade Slope Contours Aspect

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

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

lecture 9 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.

lecture 9 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

lecture 9 Viewshed Analysis Red represents areas that can be seen by 1 house, blue by 2 or more

lecture 9 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.

lecture 9 Viewshed Analysis In this case, red is for tower 1, blue for 2 and green for 3

lecture 9 Proximity Can use raster distance functions to create zones based on proximity to features; here, each zone is defined by the closest stream segment

lecture 9 Distance Measurement Can create distance grids from any feature theme (point, line, or polygon)

lecture 9 Distance Measurement Can also weight distance based on friction factors, like slope

Combining Distance and Zonal Stats lecture 9 Combining Distance and Zonal Stats Can also summarize distances by vector geography using zonal stats

Combining Distance and Zonal Stats lecture 9 Combining Distance and Zonal Stats Here we summarize by the mean

lecture 9 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

lecture 9 Density Functions

lecture 9 Density Functions