lecture by austin troy © 2005 lecture 13: introduction to raster spatial analysis ------using gis--...

Lecture by Austin Troy © 2005

Lecture 13:Introduction to Raster Spatial

Analysis

------Using GIS--Introduction to GIS

Lecture Notes by Austin Troy, University of Vermont


Raster data-A RefresherGrid Elements

Extent

# rows

# columns

Coordinates

Origin

Orientation

Resolution

Grid cell



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 involve the combining of two or more separate 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] AND [Slope>20]



Overlay Calculations

•Map algebra can be performed to identify relationships between layers, or to derive indices that describe phenomena

•Map calculations create a new layer

Calculation example:(Soil_depth_1990) – (Soil_depth_2000)=loss in soil between 1990 and 2000




Source: ESRI


Map Query ExamplesSingle layer numeric example: elevation > 2000 ft



Map Query ExamplesResults in a binary True/False layer


Map Query ExamplesMulti-criteria, single layer, categorical map query: looking for all developed land use types, using attribute codes (11, 12, 13) and OR


Vertical lines mean OR


Map Query ExamplesResults in a 1/0 binary layer, showing urbanized areas



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



Map Query: 2 layer ExamplesMulti-layer queries are 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 ExamplesFirst we would generate a slope map from out Digital Elevation Model by going to Surface>>Derive Slope



Map Query ExamplesLet’s say our criteria are elevation >800, slope >20% and land use category= coniferous forest (42)



Map Query ExamplesAgain we end up with a 1/0 binomial query layer



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



Map CalculationNow we simply type in the equation and a new grid is created that solves that equation



Map CalculationThe darker areas are those with both steep slope and erodable soils. This has the advantage over map query in that we now have a continuous index of values, rather than just a “true” “false” dichotomy



Map Calculation and QueryWe could then, for instance, run a map query to find areas that have high erodability factors and urban land use.



Zonal StatisticsNow, say we had a proposed subdivision map (this one is made up). We could overlay it on our new index layer and figure out which proposed subdivisions are problematic



Zonal StatisticsUsing Zonal Statistics we could summarize the mean, max or sum of the soil index for each of those units, even though one is grid and one is polygon. Here we summarize by mean the subdivision zones by the soil erodability calculation layer.



Summary by ZoneThis will create a DBF table that summarizes the pixel values by mean, median, max, min, etc., of all the pixels falling within a given polygon. Each row represent a polygon and each column represents a different summary statistic


Polygon layer with zones

Unique ID for polygons

This joins the DBF table to the polygon layer

Statistic by which your data will be charted


Summary by ZoneIt gives us a DBF table with values of mean, max, min, stdv, etc. in the table, plus a chart summarizing the means;



Summary by ZoneNow we can plot out the subdivision boundaries (zones) by a soil erosion statistic. In this case, clearly the most meaningful one is the mean of the soil erosion statistic. This represent the mean value, by polygon, of all the soil erosion pixels underlaying that polygon



Raster terrain functions in Arc GISArc GIS allows you to take a digital elevation model and derive:

•Hillshade

•Aspect

•Slope

•Contours



Raster terrain functions in AVDEM + Hillshade = Hillshaded DEM


+ =


Raster terrain functions in AVThis is done by making a hillshade using Spatial analyst, putting the hillshade “under” the DEM in the TOC and making the DEM transparent



Raster terrain functions in Arc GISSlope: Contours: Aspect:



Viewshed analysisThis is a multi-layer function that analyzes visibility based on

terrain.It requires a grid terrain layer and a point layer and produces a

visibility grid layer 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.

Introduction to GIS


Viewshed analysisLet’s say we’re local planners who are considering putting in a new

waste treatment facility in valley where the vacation homes of five rich and powerful Hollywood 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.

So we geocode the house locations, overlay them on a high-resolution digital elevation model and run a viewshed analysis

The lower the resolution, the more likely we’ll be wrong

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

Introduction to GIS


Viewshed analysisThis is done in ArcGIS 8, but can also be done in ArcView.

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

Introduction to 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.

Introduction to GIS


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

Introduction to GIS

lecture by austin troy © 2005 lecture 13: introduction to raster spatial analysis ------using gis--...

Documents