Introduction to Geographic Information Systems in Forest Resources
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Introduction to Geographic Information Systems in Forest Resources |
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Discussion:
There are many different types of raster analysis available in ArcView. Here are just a few of the common analytical functions. More analytical functions on raster surfaces models will be dealt with in 3-D and Surface Modeling.
Many data sets are available on the World Wide Web. Most of the raster data sets are in a generic format. Those formats that can be imported into ArcView are
The most common format you are likely to see is the USGS DEM. There is a page of Washington 30m DEMs for USGS 7.5' quad sheet boundaries on a server in Geological Sciences. The UW Map Library also has a server with many 10m DEMs for the state.
Using the File menu, choose Import Data Source. The Spatial Analyst Extension must be activated in order to import raster data. Imported raster data will be converted to the ArcInfo grid data format.
I have previously downloaded and unzipped the Blackhorse Canyon DEM from the site in Geological Sciences. Importing the grid is fairly straightforward using ArcView's GUI.
The input DEM format file is located.
An output grid name and location is specified.
The grid is then displayed in a 30 equal-interval class green-yellow-red custom color ramp.
The same process is used to import from the other raster interchange file formats.
Merging or mosaicking adjacent grids is used when your study area falls across several grids, and you wish to treat those grids as a single grid. This is commonly used when the data source is the USGS series of DEMs. Because DEMs are created and distributed as tiles, if your study area falls across several tiles, it is often necessary to merge these tiles together.
In this example, I have downloaded and imported the Stensgar Mountain DEM in addition to the Blackhorse Canyon DEM. The following image shows the grid created from mosaicking the two inputs.
Distance surfaces are grids whose output value is the distance to the closest feature in the input theme. The input theme can be a selected set of any type of feature (point, line, polygon, or cell). Distance surfaces are calculated by using the Analysis > Find Distance menu choice.
Distance surfaces are similar to buffers in the vector world. The difference between vector buffering and creating distance grids is that the distance surface represents a continuous change in distance as you move across the landscape, whereas the buffer analysis moves in user-defined quantized steps.
Here is a selected set of cells (Value > 1175) from the Blackhorse DEM. Distance will be calculated only from the selected cells.
A distance grid is calculated. Every cell in the output data set is assigned cell value equal to the straight-line distance to the closest selected cell from the Blackhorse grid theme. Those cells closest to the selected set in the previous image are light in color, and those farthest away are red.
The distance surface in the image below was generated from the Pack Forest streams theme. Calculating distance surfaces can be performed on vector or raster inputs.
Determining proximity is similar to calculating a distance surface, but rather than creating a continuous surface whose value is the distance to a feature, the proximity grid contains values in the cells for a corresponding value in the input feature theme table. Each cell is coded with the closest feature's value from the input theme, rather than for the distance to features.
Proximity analysis uses as input the selected set of the active theme, and is available from the menu at Analysis > Assign Proximity.
Here, proximity is calculated for Public Land Survey System (PLSS) section corners. In the output, the value for any given cell in the output Proximity grid theme is the section corner number (Plss_point) for the closest corner.
This technique is also known as Thiessen or Voronoi analysis.
Creating surfaces from point samples
Frequently point samples are taken to because it is too costly (either in terms of time or money) to sample an entire population. It is possible to generate interpolated surfaces based on point samples. The cells between the sampling points are given a value that represents a smooth transition of value between the sampling points. If you need an estimate of a value somewhere that you do not have a sampling point, you can get a grid value at that spot. Be careful here, because the assumption that values change smoothly across the landscape is not necessarily true! This type of analysis is well-suited to data that definitely do change gradually over a large area, such as precipitation. In any case, if your sampling points are spread too far apart, you may create an interpolated grid that does not capture local variations.
Here is a surface generated from the Pack Forest CFI plot centers. The darker the shade, the higher the amount of conifer volume in 1994. Plot centers are also displayed here for illustration.
There are a number of different options for creating surfaces from point samples. If you need to perform surface interpolation from points, you should read the help documents thoroughly.
Mapping contours
If you have data representing a continuous surface, it is possible to create single contour lines for a given grid cell value, or to create a whole group of contour lines at a regular interval. This can be of value if you wish to create a contour map of any continuously changing surface. Although digital vector elevation contours are available for some USGS quad sheets, many areas of the state have not been digitized yet. However, we do have complete statewide coverage for DEMs. These DEMs can be used to create contour lines that can be added to maps.
Using the Create Contours button 
, you can click on a
cell and have a contour isoline created for that value. Single contour lines
are created as simple graphics. As simple graphics, they do not contain any
attributes, and are not stored anywhere on disk. These graphics can be converted
later to shapefiles, but they will not have elevation values.
Here is a single contour line created interactively for the Blackhorse DEM.
To create contours that are shapefiles with attributes, another method is used. Here, contours are created for the entire surface at an interval of of 100 units.
Zones in one grid theme can be defined by either polygons or zones of integer grids. For areas within different polygons, or for zones within an integer grid, the input grid values are summarized. The output is a table in which a single record exists for the unique values in the chosen field in the zone-defining theme. Each record in the output table contains the fields Area, Min, Max, Range, Mean, Std, and Sum. If there are less than 25 records in the output table, a chart will be automatically created and added to the project.
In this example, the zone-defining theme is Stands. The individual zones are polygons containing the same value for the Species field. This means that for every unique occurrence of a species in the Stands theme a new grid zone will be created (even if the stands are not contiguous). The theme to be summarized is Dem. The statistic to be charted is Mean, the mean elevation within each zone. In this case, this signifies the mean elevation for all cells within stands of a common species.
Based on the chart, the stands with the highest mean elevation are dominated by Western hemlock, and the lowest-elevation stands are dominated by Western white pine.
Cross-tabulation allows you to compare the area of one specific value in one polygon or integer grid theme against one specific value in an another polygon or integer grid theme. The input themes and fields are chosen by selecting Analysis > Tabulate Areas from the View GUI menu.
In this example, I am comparing the Species field in the Stands theme against the Soil.name field in the Soils theme.
The output table contains a unique value for each Species record, and fields representing unique values from the Soils theme.
The values in the fields are the area (in map units) for the spatial overlap between the classes in the input themes. For example, in Red Alder stands, there are 210,857 ft^2 of Barneston soils, and 6,569,848 ft^2 of Scamman soils.
If you have two themes representing the same data for a study area at different times, you can use cross-tabulation for change analysis. Tabulation can be used any combination of (integer) grid and/or polygon themes.
Cross-tabulating areas is a raster analysis technique. When tabulating areas for polygon themes, ArcView creates temporary grids, and tabulates the temporary grids against each other. In this case, the user must supply analysis extents and cell sizes. If you use grids as the inputs, the cell size and analysis extent will be set by the Analysis properties.
This is a very powerful technique for change analysis. If you have data sets representing two different time slices, you can compare the area of such attributes as land cover or zoning designations.
While the normal feature theme table query allows a query only on a single theme, the Map Query allows you to make a complex query based on multiple themes. These types of queries are simple to perform as long as the grid themes representing the properties in question are contained in a single View document. To do the same query in the vector world requires polygon themes representing the themes (which is in itself a problem, since vector themes are not good at representing continuous phenomena), and the performance of multiple topological overlay operations.
In this example, I am interested in finding cells closer than 500 ft from a stream, with an elevation > 1500, with greater than 6,000 bd-ft timber volume.
Those cells displayed in green meet the criteria.
How would you go about getting the answer to the same query if you only had access to vector data and vector processing?
Neighborhood statistics are the focal functions referred to in Raster Analysis I. The neighborhood is defined as the group of cells for which statistics will be calculated. The neighborhood (a.k.a. kernel or focus) can be shaped as a circle, rectangle, ring, or wedge. Statistics available are
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The processor looks in the neighborhood, identifies cells or point features within that neighborhood, and calculates a single statistic for that neighborhood. That single value is then placed in the output grid in the cell located at the center of the neighborhood. The process is performed for every input cell location in the analysis window.
It is possible to perform neighborhood statistics on point themes. If a point theme has a numeric field, the process is performed for the entire area within the analysis window, and the statistic is generated for the points located within the kernel at each output cell location.
A typical use of neighborhood statistics is known as "filtering." A "low pass" filter is nothing more than a 3 by 3 cell focal mean performed for an entire grid. Low pass filters smooth out anomalies and peaks in surfaces.
A "high pass" filter is also a 3 by 3 focal function, but rather than taking the mean of the 9-cell window, it performs a focal sum of the kernel cells, but first multiplies the cells by these coefficients:
There are several different coefficients that can be used in a high-pass filter, but they all have the objective of sharpening edges. ArcView's default high-pass filter uses these particular coefficients.
In this example, an input grid represents several different vegetation zones.
The high pass filter makes the zone interiors the same value (0), while the edges get either a high or low value. The edges are most pronounced where the contrast is greatest.
This analysis is performed using a Map Calculation:
With this grid as the result:
Running a map query on the result of the high pass filter (selecting out cells that <> 0) gives the result below, a grid composed of two zones, one representing interiors, and the other representing edges.
Edges can be used to define places where animal movement may be hindered.
The key to the analysis above is in the use of Avenue syntax for the Filter request in the Map Calculation expression:
Most of the analyses that are available in the Spatial Analyst need to be coded in this way; there are simply too many analyses for there to exist a button for each. You will appreciate this if you read through the help file for the Grid object class.
In this next example, which is quite a bit more unconventional, the neighborhood statistic (mean) is calculated on the CFI plot centers, with the value field representing hardwood volume. One crucial difference between this analysis and the previous is that the input data set is a point theme rather than a grid. In this case, both a focal function as well as a surface interpolation are at work.
As the kernel passes along (shown here as a circle with a radius of 15 cells), it calculates the mean value for points that fall within the circle. The hardwood board-foot volume is displayed for the CFI plot center.
That value is then placed at the location of the central cell for the kernel in the output grid.
The kernel keeps moving on and performing the same operation until it has passed over the entire analysis window. The darker cells are those which have a high mean hardwood volume in their 15-cell-radius neighborhood.
This pattern should look familiar: hardwood volume is greatest near streams. Note that not all CFI plot centers have been sampled, which is why many of the areas that are close to streams appear to have low hardwood volume. Here, only plot centers with measured hardwood volume are displayed as "X" markers.
Reclassifying cell values is useful when you need to rescale the values in a grid, or if you wish to decrease or increase the number of classes in a grid. Reclassification always occurs with a loss of original information content, even if the number of classes is increased.
Reclassify in the Analysis menu changes the values in a grid theme from one value to another through one of two techniques, reclass (for categorical data) or slice (for continuous data).
Reclassification can change classification types from one to another, e.g., from categorical to ordinal. For example, you may have a grid representing soil types, including a value for soil name. You may wish to reclassify this nominal classification to an ordinal class, where the output class represents the suitability for building roads. You would set up your classification in the output grid theme, so that the output value represents suitability.
Here is a simple reclassification of soils by soil name. The output grid theme has the same number of classes as the input theme. If the Classification Field had been a numeric field, it would have been possible to lump input classes together.
Following is an equal interval slice based on the site index field:
Conditional processing
Conditional processing is a method of creating new grids based on an "if-then" condition. For example, we may be interested in reclassifying cells that have a certain value, but leaving other cells with their original value, this is possible with a reclassification. However, reclassification can be tedious (setting up the output classes), whereas conditional processing can create the new grid based on specific rules rather than simple numerical transformations. The conditions can also include several grids, rather than reclassifying based only on the values within a single grid.
Going back to the Blackhorse-Stensgar grids, all cells with a value less than 900 are coded with a value of -9999, those above 1000 with a value of 9999, and the cells between that range with their original values.
The conditional statement is a little complicated, but can be decoded:
([Map Calculation 1] < 900.AsGrid).Con (-9999.AsGrid, ([Map Calculation 1] > 1000.AsGrid).Con (9999.AsGrid, [Map Calculation 1]))
This translates to:
If Map Calculation 1 is less than 900,
then make the new grid value -9999,
but if Map Calculation is greater than 1000, make the new grid value 9999,
or else make the new grid's value the same as the original Map Calculation 1 value.
Here is the resultant grid:
Conditional processing is very useful when you need to select out or analyze a specific group of cells in one way, and another group of cells in another way.
It is possible to go back and forth between raster and vector formats. This always is at the expense of the loss, or generalization, of shapes. Any feature theme can be converted to a grid theme.
Vector to Raster:
Points are converted to single cells. Lines are converted to groups of cells oriented in a linear arrangement. Polygons are converted to zones. In all cases, only selected features are converted, or all features if no selection is active.
Raster to Vector:
Grid themes can only be converted directly to polygon vector themes. Be careful, because a new polygon will be created based on the field that is used for the conversion. If you have an elevation grid theme and you convert this to a polygon feature theme based on the Value field, you will get a very large number of very small polygons, and this will take a long time. It is more customary to first reclassify grids to create zones, and then convert these zones to polygon features.
ArcView 3.1 cannot convert grid themes to line or point features, but this is possible with ArcInfo.
Here, the Blackhorse DEM has been reclassified into 9 elevation bands and then saved as a shape file. The value for the new polygon attribute Gridcode matches the original Value field from the input grid data source.
The new polygon theme is displayed in a graduated color classification based on the Gridcode field.
Here, the Soils polygon feature theme has been converted to a grid theme, based on the unique polygon identifier field (soils#).
The new soils grid is displayed by using the Soil.name field, which was joined back onto the grid value attribute table.
Once a raster data set has been converted to vector format, all of the vector analysis and overlay tools can be used. Likewise, when a vector data sets is converted to a grid, it can be used in raster analytical techniques.
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The University of Washington Spatial Technology, GIS, and Remote Sensing Page is provided by the College of Forest Resources and the College of Ocean and Fisheries Sciences through Unit-Specific UIF. Site administrator: Phil Hurvitz. |
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