GEOG 487 Lesson 8 · Web viewSilty Clay Loam 3 Loam 5 Loamy Sand 6 Make sure you have the correct answer before moving on to the next step. The " S " attribute table should match

GEOG 487 Lesson 8: Step-by-Step Activity

Part I: Review the Relevant Data Layers and Organize the Map Document

In Part I, we will review the starting datasets and organize the map document for analysis.

1. Unzip the Data for Use in ArcMapa. Unzip the Lesson 8 data in your L8 folder. Since all of the starting raster format layers

are included in this zip file, you do not need to worry about how you unzip the data.b. Familiarize yourself with the contents of the data included with this zip file. Refer to the

Lesson Data section for additional information.

2. Organize the Map Document and Familiarize Yourself with the Study Area

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.

a. Start ArcMap and make sure the Spatial Analyst extension is on. Create a new map, and save it in your L8 folder.

b. Add the LakeRaystown, geology, soil, elev, and streams_buffer datasets from your L8Data folder. Select No if prompted, "Would you like to create pyramids?"

c. Examine the metadata and attribute tables of all of the starting datasets.d. Change the symbology of the "LakeRaystown" and "streams_buffer" layers to hollow

outlines, the geology layer to unique colors by "rock_type," and the soils layer to unique colors by "texture."

e. Zoom to the extent of the Lake Raystown Watershed and look for spatial patterns in the geology and soil datasets.

f. Add the Open Street Map basemap. Explore the study area.

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 of a buffer was used to create the streams data?

Where is the Lake Raystown Watershed located in relation to the state of Pennsylvania?

GEOG 487 Lesson 8: Step-by-Step Activity; Authors: Rachel Kornak, GISP, K. Corradini & J. Bishop. Updated 5/13/2023. Page 1 of 24© 1999-2016 The Pennsylvania State University.

3. Set the Spatial Analyst Option Settingsa. Go to Geoprocessing > Environments…

i. Set your workspace and scratch space to your Lesson 8 folder. ii. Set the output coordinates, mask and extent to “Same as LakeRaystown.”

iii. Set the cell size to 30 meters.iv. Choose to not build pyramids.

b. Save your map to lock in the options.

Part II: Customize the Data and Produce the DRASTIC Groundwater Vulnerability Layer

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.

1. Add DRASTIC ratings to the Soil Layer

a. Open the Soil attribute table and examine the data. Pay particular attention to the "TEXTURE" field. We will use this field to convert the vector file to a raster grid.

b. ArcToolbox > Conversion Tools > To Raster > Feature to Raster.


Make sure you have the correct answer before moving on to the next step.

The "soilgrid" 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 expanding the table columns to view all COUNT totals.)

c. Compare the "soilgrid" map and attribute table to that of the "soil" shapefile. d. Now that the soil data is in grid format, we can reclassify the grid to assign DRASTIC

Ratings to each soil type. e. Use the values in the table below to reclassify "soilgrid" based on the "TEXTURE" field.

Name the output file "S." (This is the letter used in the DRASTIC acronym to represent the Soil Media).

Table 1: DRASTIC Ratingsfor Soil Textures (S)

Texture DRASTIC Rating

Silty Clay Loam 3

Loam 5Loamy Sand 6



The "S" attribute table should match the example below. If your data does not match this, go back and redo the previous step.

2. Add DRASTIC ratings to the Geology Layer

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 Reclassify Tool again to assign DRASTIC ratings corresponding to these three factors for the appropriate surface geology units contained in the geology layer.

a. Open the Geology attribute table and examine the data. Pay particular attention to the "ROCK_TYPE" field. We will use this field to convert the vector file to a raster grid.

b. Convert the Geology shapefile to a grid. Use "ROCK_TYPE" as the "Field." Name the grid "geologygrid."


The "geologygrid" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.



c. Tables 2, 3 and 4 show the DRASTIC ratings for Aquifer Media, Vadose Zone, and Hydraulic Conductivity, respectively. Create three new grids from the "geology grid" raster using the reclassify tool and "ROCK_TYPE" field. Name the new grids "a," "i," and "c".

Table 2: DRASTIC Ratings for Aquifer Media (a)

Rock Type DRASTIC RatingInterbedded Sedimentary 6

Sandstone 6Shale 2

Carbonate 10

Table 3: DRASTIC Ratings for Impact of Vadose Zone (i)


Sandstone 6Shale 3

Carbonate 10

Table 4: DRASTIC Ratings for Hydraulic Conductivity (c)


Sandstone 1Shale 1

Carbonate 10



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.

3. Create a Slope Map from the Digital Elevation Model and add DRASTIC ratings

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.

a. Select ArcToolbox > Spatial Analyst Tools > Surface > Slope. b. Choose "elev" as the "Input raster", select "PERCENT RISE" for the "Output

measurement." Leave the "Z factor" at 1 and name the output raster "pctslope." The resulting layer depicts steep slopes with high values and gentle slopes with low values.

Degree vs. Percent Slope:

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


c. Examine the "pctslope" grid. Notice how the attribute table is greyed out. Remember from Lesson 5 that raster attribute tables are not created if the values contain decimals.

d. We want to reclassify the "pctslope" grid using the DRASTIC Ratings in Table 6.

Table 6: Ranges and Ratings for Topography

Topography Range DRASTIC Rating0 – 2 102 – 6 9

6 – 12 512 – 18 3

> 18 1

e. Open the Reclassify tool and select the "pctslope" grid. Notice the default number of classes and break values listed in the "Old values" column. These are not particularly useful to us, since we want to use 5 break values (2, 6, 12, 18, and the largest number in the dataset).

f. The quickest way to change these settings is to click on the "Classify…" button. Select "Natural Breaks (Jenks) as the Classification Method. Change the number of classes to "5." Manually type in the break values on the right hand side. Notice that once you alter the values, the classification method automatically changes to "Manual." Click "OK." 2


You should see the "Old values" update like the example below.


g. Modify the "New Values" in the reclassify window based on the values in Table 6. Name the resulting grid "t." Make sure you check the box "Change missing values to NoData."


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

Sometimes you will 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 "1" or "2," 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 "1," and the old value of "2.0001" to a new value "2" 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 Desktop Help for further information regarding reclassification.

4. Explore the DRASTIC Rating Output Grids

a. Add the "r" and "d" grids from your L8 folder and open the attribute tables. The numbers in the "VALUE" fields correspond with DRASTIC ratings based on each cell’s recharge rate and depth to groundwater, respectively. These are the final input datasets required to calculate DRASTIC groundwater vulnerability ratings for the watershed.

b. Change the symbology of the d, r, a, s, t, i, and c grids so that low vulnerability ratings (1-3) are green, medium ratings (4-7) are yellow, and high ratings (8-10) are red. You can group similar values together using "Unique Values" on the Properties > Symbology tab > Press the Ctrl key to select values to group > Right Click > Group Values.


c. Update the label column as shown below so the results are easier to interpret.

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?

d. Remove all of the datasets other than the drastic grids, the steam buffers and the watershed boundary so your map is easier to work with. Save your map.

5. Calculate the DRASTIC Groundwater Vulnerability IndexNow 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.

a. Open the Raster Calculator (ArcToolbox > Spatial Analyst Tools > Map Algebra > Raster Calculator) and enter the expression below and name the grid "drastic_index". Be careful when choosing your input files. Also, the syntax for the raster calculator must be absolutely correct, or you will get a "syntax error."

"d" * 5 + "r" * 4 + "a" * 3 + "s" * 2 + "t" * 1 + "i" * 5 + "c" * 3

b. Change the color ramp so that high values are shades of red and low values are shades of green like the example below.



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?


Part III: Identify the Potential Suitable Sites for Sludge Disposal

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.

Sludge Disposal Site Selection CriteriaFor the purposes of this lesson, we assume that state regulations require the following for a site to be considered for sludge disposal:1. Areas that are very vulnerable to groundwater contamination must

be avoided. Therefore we will only consider areas with DRASTIC Index values less than 150.

2. Sites must be at least 300 meters from surface water.3. Sites must have a contiguous area of at least 0.5 square km.


1. Explore the DRASTIC Rating Output Grids

a. In Part II Step 3, we talked about the potential pitfalls of using the reclassify tool when break values are important in your results. One way to avoid this issue is to use the Raster Calculator, which allows us to use mathematical sign of less than or greater than. Enter the expression shown below and name your grid "di150."

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).

b. Reclassify the "di150" grid using the settings below. Name the output grid "OK_DRASTIC."



The "OK_DRASTIC" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.


2. Create Grid of Suitable Surface Water

a. Convert the "steams_buffer" shapefile to a raster using the "Id" field. Name the output "streambuffgrd" and save it in your L8 folder. Note: This may take a while to run.

b. Reclassify "streambuffgrd" as shown below. Name the output "OK_Streams." Note: This may take a while to run.

Note: As we experienced in Lesson 7, if values “1” occur outside your study area “LakeRaystown” you will need to run an additional step with the Raster Calculator to turn these values to NoData. Name the output “OK_Streams2.”



The "OK_Streams2" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.

c. Compare the "OK_Streams2" grid to the "steambuffgrd." Notice how we have essentially flipped the areas of NoData. It is important that you choose appropriate mask and extent settings when using this technique.

3. Combine the Suitability Grids Using the Raster Calculator

a. Use the raster calculator to multiply the "OK_DRASTIC" and "OK_Streams2" rasters together. Cells that meet both of the criteria will be assigned a value of "1" in the output raster. Cells that do not meet either one or both of the criteria will be assigned a value of "NoData" in the output raster. Name the new grid "OK2criteria."



The "OK2criteria" attribute table should have all of the attributes shown below. If your data does not match this, go back and redo the previous step.

b. Examine the attribute table. Notice there is only 1 row. We need a way to lump together cells that make up contiguous units. To accomplish this, we will use the Region Group tool like we did in Lesson 7.

c. Go to ArcToolbox > Spatial Analyst Tools > Generalization > Region Group, select " OK2criteria " as the input raster, name the output raster " OK_Regions ", leave the number of neighbors to use as "FOUR", the zone grouping method as "WITHIN", uncheck the "Add link field to output", leave the excluded value setting , and click OK.


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.


4. Create Grid of Suitable Regions Greater than 0.5 sq km

a. 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.

b. Add a new float field to the "OK_Regions" attribute table named "AREA_SQM." Use the field calculator to populate the field.

Why did we use the number "30" to calculate the area?

c. We will use Extract by Attributes to perform a query on sites > 0.5 sq km. Extract by Attributes is similar to the Raster Calculator, except that it makes entering specific field expressions much easier.

d. Go to ArcToolbox > Spatial Analyst Tools > Extraction > ExtractByAttributes. Select "OK_Regions" as the input raster and name the output raster "OK_Area". To populate the "Where clause" with the expression given below, click the "Query Builder" button. The "Query Builder" dialog will appear. Click on "AREA_SQM", and then on ">=", and type 500000 at the end of the expression (0.5 sq km = 500,000 sq m). Click OK to automatically input the expression into the "Where clause."



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.

e. Reclassify "OK_Area" like the example below. Hint: Change the number of classes to "1," since all of the old values match all of the site selection criteria. Name the grid "FinalSites." These are your potential sludge disposal sites.


That is all for the required portion of the Lesson 8 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.

Try one or more of the optional activities listed below.

Redo Part III of the lesson using the value of "0" to denote unsuitable areas instead of "NoData." Compare your results with the "FinalSites" grid.

Redo Part III of the lesson, except add the suitable grids together instead of multiplying them. How do you need to alter the reclassification values to find suitable sites using this methodology?

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 Piazza in the “lessno8” folder with the tag “TryThis.”


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GEOG 487 Lesson 8 · Web viewSilty Clay Loam 3 Loam 5 Loamy Sand 6 Make sure you have the correct answer before moving on to the next step. The " S " attribute table should match