Finally, if she hopes to locate urban, treeless areas, she will look for all cells containing a “1.” Unfortunately, the cell value “1” also is coded into each pixel for nonurban, tree cells. Similarly, if she hopes to find the location of all treeless (or “non-tree,” nonurban areas, she can examine the summed output raster for all “0” entries. If she hopes to find the location of trees in urban areas, a simple mathematical sum of these datasets will yield a “2” in all pixels containing a tree in an urban area. Assume that a natural resource manager has two input raster datasets she plans to overlay one showing the location of trees (“0” = no tree “1” = tree) and one showing the location of urban areas (“0” = not urban “1” = urban). The simplicity of this methodology, however, can also lead to easily overlooked errors in interpretation if the overlay is not designed properly. In both the Boolean and relational overlay methods, cells that meet the evaluation criteria are typically coded in the output raster layer with a 1, while those evaluated as false receive a value of 0. Similarly, the relational raster overlay method utilizes relational operators (, >, and =>) to evaluate conditions of the input raster datasets. As discussed in Chapter 6, the Boolean connectors AND, OR, and XOR can be employed to combine the information of two overlying input raster datasets into a single output raster. The Boolean raster overlay method represents a second powerful technique. Two input raster layers are overlain to produce an output raster with summed cell values. In particular, raster overlay is often used in risk assessment studies where various layers are combined to produce an outcome map showing areas of high risk/reward. As you can imagine, there are many uses for such functionality. Following the calculation, an output raster is produced that contains a new value for each cell (Figure 8.4). The numbers within the aligned cells of the input grids can undergo any user-specified mathematical transformation. The mathematical raster overlay is the most common overlay method. Exploring Geographic Information Systems. With this in mind, there are several different methodologies for performing a raster overlay (Chrisman 2002).Chrisman, N. If these assumptions are violated, the analysis will either fail or the resulting output layer will be flawed. Despite their simplicity, it is important to ensure that all overlain rasters are coregistered (i.e., spatially aligned), cover identical areas, and maintain equal resolution (i.e., cell size). Geographical Information Systems for Natural Resources Assessment. Raster overlays are relatively simple compared to their vector counterparts and require much less computational power (Burroughs 1983).Burroughs, P. Figure 8.3 Clipping a Raster to a Vector Polygon Layer The raster clip process results in a single raster that is identical to the input raster but shares the extent of the polygon clip layer. Here, the input raster is overlain by a vector polygon clip layer. Figure 8.2 Raster Buffer around a Target Cell(s)Ī raster dataset can also be clipped similar to a vector dataset (Figure 8.3). These cells could also be further classified to represent multiple ring buffers by including values of “3,” “4,” “5,” and so forth, to represent concentric distances around the target cell(s). Most geographic information system (GIS) programs calculate raster buffers by creating a grid of distance values from the center of the target cell(s) to the center of the neighboring cells and then reclassifying those distances such that a “1” represents those cells composing the original target, a “2” represents those cells within the user-defined buffer area, and a “0” represents those cells outside of the target and buffer areas. Figure 8.1 Raster ReclassificationĪs described in Figure 8.2). In addition, these reclassified layers are often used as inputs in secondary analyses, such as those discussed later in this section. This simplification allows for fewer unique values and cheaper storage requirements. These values could be simplified by aggregating each pixel value in a few discrete classes (i.e., 0–100 = “1,” 101–200 = “2,” 201–300 = “3,” etc.). For example, an elevation grid commonly contains a different value for nearly every cell within its extent. Reclassification is basically the single layer process of assigning a new class or range value to all pixels in the dataset based on their original values (Figure 8.1. Reclassifying, or recoding, a dataset is commonly one of the first steps undertaken during raster analysis.
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