The Step-by-Step Activity for Lesson 8 is divided into three parts. In Part I, we will review the relevant datasets and organize your Map. In Part II, we will create a DRASTIC Groundwater Vulnerability grid. In Part III, we will determine suitable land areas for sewage sludge application sites based on the DRASTIC ratings, distance from surface water, and size of each region.
Note: You should not complete this step until you have read through all of the pages under the Lesson 8 Module. See the Lesson 8 Checklist for further information.
In Part I, we will review the starting datasets and organize the map for analysis.
Since all of the datasets used in this lesson have the same projection, we do not need to be concerned with the order that we load the data.
Do the all of the provided raster grids have the same cell size?
Do all of the input datasets have the same extent?
What are the units of the "VALUE" attribute in the elevation grid?
How many different types of soil and rock types are in the study area?
How wide a buffer was used to create the streams data?
Where is the Lake Raystown Watershed located in relation to the state of Pennsylvania?
In Part II, we will create a series of grids representing the DRASTIC Ratings for each parameter (D -Depth to Water Table, R- Net Recharge, A - Aquifer Media, S - Soil Media, T - Topography, I - Impact of Vadose Zone, and C- Hydraulic Conductivity). The dataset we will use to create each grid is shown in the graphic below. In this section, we will introduce two new spatial analyst concepts: creating slope grids from elevation and reclassifying ranges of values as opposed to unique values.
Make sure you have the correct answer before moving on to the next step.
The "soilgrid.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step. Be sure to go to Feature to Raster tool > Environments and double-check and the output coordinates and processing extent to the same as "LakeRaystown.” Also, be sure to expand the table columns to view all COUNT totals.
Texture | DRASTIC Rating |
---|---|
Silty Clay Loam | 3 |
Loam | 5 |
Loamy Sand | 6 |
Make sure you have the correct answer before moving on to the next step.
The "s.tif" attribute table should match the example below. If your data does not match this, go back and redo the previous step.
Three of the seven DRASTIC factors (A - Aquifer media, I - Impact of the vadose zone, and C - Hydraulic Conductivity) can be defined on the basis of geology. We will use the Reclassify Tool again to assign DRASTIC ratings corresponding to these three factors for the appropriate surface geology units contained in the geology layer.
Make sure you have the correct answer before moving on to the next step.
The "geologygrid.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
OID | Value | Count | Rock_type |
---|---|---|---|
0 | 1 | 1480784 | Interbedded Sedimentary |
1 | 2 | 643096 | Sandstone |
2 | 3 | 388372 | Shale |
3 | 4 | 256791 | Carbonate |
Rock Type | DRASTIC Rating |
---|---|
Interbedded Sedimentary | 6 |
Sandstone | 6 |
Shale | 2 |
Carbonate | 10 |
Rock Type | DRASTIC Rating |
---|---|
Interbedded Sedimentary | 6 |
Sandstone | 6 |
Shale | 3 |
Carbonate | 10 |
Rock Type | DRASTIC Rating |
---|---|
Interbedded Sedimentary | 2 |
Sandstone | 1 |
Shale | 1 |
Carbonate | 10 |
Make sure you have the correct answer before moving on to the next step.
The "a," "i," and "c" attribute tables should have all of the attributes shown below. If your data does not match this, go back and redo the previous step. Again, be sure to expand the COUNT field to see all the complete values.
When you have data that represents elevation, you can create several different types of raster layers, one is a slope grid. Slope represents steepness, incline, or grade of a line or area. A higher slope value indicates a steeper incline. With Spatial Analyst, it is easy to create a slope layer from elevation data.
Degree vs. Percentage
Be careful when choosing the slope output measurement. There are two ways to express slope values, either as a percent or as a degree. "45 degrees" slope and "45 %" slope are NOT equivalent values.
Degree slope (θ): angle created by a right triangle with sides of length "rise" and "run"
Percent slope: length of "rise"/length of "run" * 100
Topography Range | DRASTIC Rating |
---|---|
0-2 | 10 |
2-6 | 9 |
6-12 | 5 |
12-18 | 3 |
>18 | 1 |
Make sure you have the correct answer before moving on to the next step.
The "t" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Reclassifying Ranges of Numbers vs. Unique Values
When you need to reclassify data based on ranges of values instead of unique values. For example, notice above that the old value of "2" is specified as the upper bound in the range "0-2" and the lower bound in the range "2-6." What new value, either "10" or "9," will be assigned to old values of "2" in the output grid?
In this case, ArcGIS will assign the old value "2" to a new value of "10," and the old value of "2.0001" to a new value "9" in the output grid. The general rule is that ArcGIS will include the break values themselves in the group that it forms the upper range boundary. Notice that you will encounter this same issue for all break values (e.g., "6", "12", and "18" in the example above).
This is particularly important when the break values themselves are meaningful in your analysis. The most common example of this situation is when you encounter specifications of "less than x" vs. "less than or equal to x" in your requirements. If you want to reclassify values "less than 5" to a new value, you would need to specify a break value of "4.99999999," so the value of "5" is not included in your new category. The particular number of decimals you need to specify will depend on the number of decimals in your input data. For example, if your data layer has five decimal places, then you would set the reclassification thresholds as follows: a.aaaaa - b.bbbbb, b.bbbbb - c.ccccc, and so forth.
See the ArcGIS Help for further information regarding reclassification by range [1].
Compare the "d" grid to the "streams_buffer" shapefile. Do areas near streams have high or low vulnerability?
Which input datasets (d, r, a, s, t, i, c) have the highest DRASTIC rating values?
Do you see any spatial patterns in the individual drastic grids?
Now that you have the required data layers, you can create a DRASTIC groundwater vulnerability grid based upon the DRASTIC index equation. This will involve use of the Raster Calculator to combine several grids in a weighted overlay. The graphic below shows an example of how cell values are updated during the calculation.
Combining raster layers is a simple, yet very important process with Spatial Analyst. You will often find that it is necessary to create a single layer that is comprised of several data sets. The idea is similar to that of performing an overlay with vector layers, in that you are making one out of many, with the major exception that the cell values change based on the expression used.
The addition (+) and multiplication (*) signs are the most common arithmetic operators used to combine raster layers. The plus (+) sign performs an addition with each cell, so the value in a given cell of one grid will be added to the value of the same cell in the next grid, and so on. The multiplication (*) sign, as expected, performs a multiplication based on the values in each cell.
Either of these can be used when the purpose is to simply combine grids, although you should use the same operator for all grids. However, when forming an expression that includes additional operations on individual grids, as in the case above, it is important to understand the precedence that the operators will be performed. In mathematical order of operation rules, multiplication always takes precedence over addition. Hence, in the expression above, the values in the "D" grid will be multiplied by 5 before they are added to the values in the "R" grid. If an expression should occur that is out of precedence, enclose that expression with parentheses, as you would when using a calculator.
Make sure you have the correct answer before moving on to the next step.
The" drastic_index" grid should have the following information. The statistics from the "COUNT" field are also provided. If your data does not match this, go back and redo the previous step.
What do the numbers in the "VALUE" field of the "drastic_index" mean in the real world? For example, do high values represent areas with high or low vulnerability to groundwater pollution?
Which parts of the watershed are most vulnerable to groundwater pollution?
Do any of the parameters have a greater influence on the final results?
Now that the groundwater vulnerability layer has been produced, we can use this data to help find the areas in the watershed most suitable for sludge disposal. Along with this dataset, we also need to incorporate the stream buffer dataset. Remember from previous lessons that it is possible to reclassify grid cells to values of "NoData" to exclude them from your analysis. We will use this technique to remove portions of each dataset that do not meet the relevant criteria. For example, we will reclassify suitable areas within each dataset as "1" and unsuitable areas as "NoData."
You can also do the opposite of this by assigning existing values of "NoData" to more meaningful values. We will use this technique to create a grid of areas that are outside of steam buffers. Then, we will use the Raster Calculator to combine the individual suitability results into one grid. We will then use the "RegionGroup” command to create regions from adjacent cells with the same results. This process is illustrated in the graphic below.
For the purposes of this lesson, we assume that state regulations require the following for a site to be considered for sludge disposal:
The calculation performed in the previous step combines the results of two Boolean operations that are either evaluated as:
TRUE (indicated by a value of 1) OR FALSE (indicated by a value of 0)
We are only interested in cells that meet the criteria (values of 1).
Make sure you have the correct answer before moving on to the next step.
The "OK_DRASTIC.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Make sure you have the correct answer before moving on to the next step.
The "OK_Streams" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Make sure you have the correct answer before moving on to the next step.
The "OK2criteria.tif" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.
Make sure you have the correct answer before moving on to the next step.
The "OK_Regions" statistics for the "COUNT" field should match the example below. If your data does not match this, go back and redo the previous step.
The last criteria we need to incorporate is - Area (sites greater than 0.5 sq km). We learned in Lesson 5 that you can calculate the area of a raster by multiplying the number of cells by the area of each cell. To calculate the area of regions within a raster, we can use this same method.
Why did we use the number "30" to calculate the area?
Make sure you have the correct answer before moving on to the next step.
The "OK_Area" statistics for the "COUNT" field should match the example below. If your data does not match this, go back and redo the previous step.
Try one or more of the optional activities listed below.
Note: Try This! Activities are voluntary and are not graded, though I encourage you to complete the activity and share comments about your experience on the lesson discussion board.