Download the Lesson 7 data (Lesson7ArcGIS_Pro.zip [2]) and unzip them in your course folder.

Note:

Do not place spaces in directory or file names. This can result in fatal program errors, especially when working with raster data. (e.g., c:\483\lesson 7 should be changed to c:\483\lesson_7).

1. Open ArcGIS Pro and start a new blank project called GEOG483_Lesson7 in your Lesson7 folder. Uncheck Create a new folder for this project.
2. Click Add Data.
3. Browse to your Lesson7 folder. Double-click the viticulture geodatabase and add the climate, elevation, floodplain, hydro, landuse, and soils layers.Metadata for data that you will be using in this lesson:
   • Projection: Universal Transverse Mercator-1927 projection.
   • Zone: 10
   • Spheroid: Clarke 1866
   • Longitude of Central Meridian: -123
   • Latitude of Projection Origin: 0
   • False Easting: 500,000
   • False Northing: 0
4. Make sure the Map Units are set to Meters. Set the Display Units to US Feet.
5. Modify the symbology and drawing order of layers in the Table of Contents, as necessary.

You will set the extent and cell size, which will be applied to layers that are created.

1. Select the Analysis tab and click Environments to open the Environments panel.
2. Expand the Workspace category, change the Current Workspace to your Lesson 7 geodatabase viticulture.gdb. Also, change the Scratch Workspace to your Lesson 7 geodatabase viticulture.gdb.
3. Expand the Processing Extent category and choose Same as layer > elevation from the pull-down menu. Keep the other defaults.
4. Expand the Raster Analysis category and type 10 in the text box next to Cell Size. If you look at the properties of the elevation layer, you will see that the cell size is 10. Alternatively, we could choose Same as layer elevation since the elevation layer has the cell size we want to use.
5. Click OK.

All layers that are now created will be saved in your Lesson7 viticulture geodatabase. They will have the extent of the elevation layer and they will have a cell size of 10. Zoom in on the elevation layer. With the Map tab selected, use the Measure tool  to measure the size of each cell.

Note:

You should take screen captures as you go through the lesson; the project deliverables require some images of the process you followed in the analysis.

You will do a new kind of spatial operation in this step – a dissolve. It is not entirely necessary in this situation, but you might want to use it in the upcoming final project for this class.

1. Click the List By Drawing Order  button at the top of the Table of Contents.
2. The landuse raster layer should be above the elevation and floodplain layers in the Table of Contents, so you can see it in the data frame.
3. Open the attribute table and notice that there are 80 records, which means there are also 80 features. Scroll down through the Landuse Type field (Lu_Type) and notice that there are many features that have the same value. Suppose you want to be able to click in an area of agricultural land in the data frame and have all of the agricultural land area selected. As the layer is now, you would have to highlight 39 different polygons or do an attribute query. Sometimes it is desirable to have one feature for every unique value in a field. A dissolve does this. Another situation where a dissolve would be useful is if you have a streets layer that you want to label. There may be many segments that make up one particular street. You may not want a text label placed next to every segment because the map would be too cluttered. You could dissolve the layer on the street name field so that you would have only one feature for every street name.
4. With the Analysis tab selected, click Tools.
5. Click Data Management Tools > Generalization > Dissolve.
6. Select landuse from the drop-down list next to the Input Features text box.
7. Save the shapefile as landuse_dslv in the Output Feature Class text box.
8. Select LU_TYPE as the Dissolve Field. There is no need to specify any Statistics Fields, but feel free to add one – maybe the sum of the perimeter field. Click Run. [See Figure 7.1]

   

   Figure 7.1 Dissolve a layer.
9. Open the attribute table of the new layer and notice that there are only four records now, one for each landuse type.

In the last part of this lesson, you will combine all of the different layers to find the suitable vineyard sites. In order to do that, all the layers must be in the same format. You will now convert the vector data to raster data.

1. In Tools, double-click Conversion Tools > To Raster > Polygon to Raster.
2. Choose landuse_dslv for Input Features.
3. Choose LU_TYPE for Value field. The attributes in the Lu_type field of the landuse layer will be used for the cell values. Make sure the Cellsize is set to 10. (We specified that earlier).
4. Save the layer as lu_raster in the Output Raster Dataset text box. Click Run. The new layer will not be added to the top of the Table of Contents, so you may have to turn off a few layers or drag it to the top in order to see it in the map display.

   Note:
   
   If one of your raster conversions fails during creation or if you make a mistake while creating a new raster layer and want to redo a step, give the new raster layer a slightly different name. (e.g., lu_raster2) Remember this throughout the lesson.

5. Remove the landuse_dslv layer from the Table of Contents. Remember, removing that layer will NOT delete the layer; it only removes the reference to the data so that it is not visible in the map.
6. In Tools, double-click Conversion Tools > To Raster > Polyline to Raster.
7. Select hydro for Input Features.
8. Choose HYDRO_ID for Value field.
9. Save the layer as hydro_raster and click Run.
10. Repeat steps 1 through 4 above to convert the floodplain coverage to a raster named flood_raster. Choose TYPE for Value field.

You have just completed Part I of this lesson, which involved organizing the data and converting data from vector to raster format. In Part II, you will continue processing the new and existing raster data by performing several types of surface operations.

Spatial Analyst can compute the hillshade for a surface. The resulting layer represents the hypothetical illumination of the surface. When used as a background, hillshading gives layers a more 3-dimensional appearance that aids in visualization of the actual terrain.

ArcGIS Pro Help:

Find out more about Hillshading, in the ArcGIS Pro Help.

Click the question mark (?) button in the upper right corner of the window to open the Help. Type Hillshade and choose How Hillshade Works to understand more about how a hillshade is created. Click Hillshade (Spatial Analyst) to read more about the specifics of the Spatial Analyst tools used in this lesson.

1. Open your project if it isn’t already open.
2. In Tools, click Spatial Analyst Tools > Surface > Hillshade.
3. Select the elevation layer from the Input raster pull-down menu, save the Output raster as hillshade, accept all other defaults listed on the Hillshade pane and click Run.
4. The hillshade layer will appear in the Table of Contents. Examine the result and drag it to the bottom of the Table of Contents so that it is below the elevation layer. You can compare this raster layer to the World Hillshade basemap that was automatically added to your Project at the beginning. You might also want to look at the Layer Properties for that layer to see how it was created.
5. Click the elevation layer in the Table of Contents. Click the Raster Layer option that appears to the right of the Share ribbon.
6. Set the Transparency to 50%. Turn off any layers obscuring the elevation and hillshade layers so that you can see it in the map. Toggle the elevation layer on and off to see how the transparency affects the display of the layers.
   The aspect of a location refers to the orientation of its slope. When deriving aspect in ArcGIS, the steepest down-slope direction from each cell to its neighbors is found. The values of the output raster layer represent the compass direction of the aspect; 0 degrees is north, 90 degrees is east, 180 degrees is south, 270 degrees is west, and flat areas receive a value of -1.
7. In Tools, click Spatial Analyst Tools > Surface > Aspect and create an Aspect layer, using an approach similar to the one you used to create the hillshade. Use the elevation layer as input and call the resulting raster layer aspect.
8. Examine the result.

ArcGIS Pro Help:

Find out more about Inverse Distance Weighted (IDW) interpolation in the ArcGIS Pro Help. Open the help, type IDW, and click How IDW Works from the choices listed to read about inverse distance weighted (IDW) interpolation. Then click IDW (Spatial Analyst) to read more about the specifics of the tools used in this lesson. Additional reading about this topic and other interpolation methods such as kriging and splining can be found by searching on Comparing Interpolation Methods.

The Soils layer is a set of sample points with depth and drainage attributes. By using a surface interpolation, a continuous raster layer can be created.

1. In Tools, click Spatial Analyst Tools > Interpolation > IDW.
2. In the IDW (Inverse DistanceWeighted) dialog box, select soils for Input point features and select SOILDEPTH from the Z value field (elevation units) drop-down box.
3. For Output raster save the layer as soildepth.
4. Type the numeral 4 into the Power text box, keep all other defaults, and click Run. [See Figures 7.2 & 7.3]. *(You will use this power for the rest of the raster layers you create in this part.)*

   Note:
   
   The power controls how the sample points around an output cell contribute to that cell's value. The higher the power, the less influence far away points have. The lower the power, the greater influence far away points have. A lot depends on the phenomenon you're trying to interpolate. Some surfaces are smooth (like temperature) and some have greater variation across distance (like precipitation). In other words, you'd want to use a higher power for precipitation than for temperature. As you saw in the help, the optimal power value can be attained by using the Geostatistical Analyst, which we do not cover in this class (but which is used in Geog 586: Geographic Information Analysis). If you do not use that, it is another case of knowing the data and maybe having an expert who knows what to be looking for in the interpolation to determine what power is best in certain situations.

   

   Figure 7.2 Interpolating soil depth

   

   Figure 7.3 Soildepth layer results.
5. Examine the resulting layer. If the extent of the soildepth layer is not the same as the elevation layer, go back to Environments and make sure the Processing Extent is Same as layer elevation. Uncheck the layer in the Table of Contents so it is not displayed in the data view.
6. Repeat the steps above to create a surface representing soil drainage. Use the DRAINCODE attribute for the Z value field. Call the layer soildrainage.
7. Remove the soils layer from the map document.

We already used interpolation to create soil depth and drainage layers. Now, we’ll use the climate layer to generate minimum temperature and maximum wind speed layers.

1. Using the climate layer, repeat the procedure described earlier to produce an average minimum temperature layer (mintemp) and an average maximum wind speed layer (maxwind). Select MIN_TEMP as the Z value field for the temperature layer and MAX_WIND for the wind speed layer. Remember to change the power.
2. Remove the climate layer from the map document when you are finished adding the two new layers.

You have just completed Part II of this lesson, which involved creating several types of raster surfaces (hillshade, soils, and climate) from existing point and raster data. In Part III, you will continue processing the raster data by performing a number of reclassifications to update existing cell values.

In raster analyses, buffering operations can be performed by “growing” features out a certain distance, or a specific number of cells. ArcGIS Pro calculates the distance from raster features, displaying them as a continuous set of incremental bands. Any of these ranges can then be reclassified to define a buffer or to find features within a specific distance.

Concept Gallery:

Learn more about Distance Calculations in the Concept Gallery in Lesson 7.

1. Open your Lesson7 Project if it isn’t already open.
2. In Tools, click Spatial Analyst Tools > Distance > Distance Accumulation. Select the hydro_raster layer as the Input raster or feature source data and specify the Output distance raster as dist_hydro. Keep all other defaults and click Run.
3. Turn off all of the layers except dist_hydro and hydro. Make sure hydro is above dist_hydro in the Table of Contents. Click some different points in the map. Each value is the distance from the stream in the hydro layer.

Most spatial analyses performed with raster software involve a combination of operations to derive a final map. Complicated analyses weight data, multiplying the cell values in a specific raster layer by a certain factor, to reflect their relative importance with respect to other layers. This gives each layer more or less impact on the result. Although weighting is a useful operation, it is not always appropriate. For the purposes of this exercise, we take a different approach. First, we reclassify the data to isolate certain criteria as necessary conditions for good grape growing. This exercise is simplified by limiting the classification to two categories: desirable (given a value of 1) and undesirable (given a value of 0). We then multiply the reclassified layers together using the Raster Calculator, which allows us to determine the optimal sites for vineyard development.

Concept Gallery:

Learn more about Combining Operations for Derivative Mapping in the Concept Gallery in Lesson 7.

1. In Tools, click Spatial Analyst Tools > Reclass > Reclassify. Select dist_hydro from the Input raster pull-down menu.
2. Click the Classify… button.
   Remember that one of our criteria is land outside the floodplain and more than 100 meters from a stream.
3. Change the Classification Method: to Equal Interval, set the number of Classes: to 2. You will see two values appear in the Classes text box. Change the first value to 100, hit Enter on your keyboard. [See Figure 7.4] You will notice that Equal Interval gets changed to Manual Interval when you are done with this tool. That is fine.

   

   Figure 7.4 The classification dialog box.
4. Click OK. The Start value in the first row should be 0 and the End value in the firts row should be 100. Change the value in the first row of the New column to 0.
5. In the second row, the Start column should be 100. The End column value will be the maximum value of the data (e.g., 100 – your max. value may include decimals.) The value in the New column should be changed to 1. Hit Enter on your keyboard to make sure the value sticks. When you have two classes that include the same value, the value will go in the first of the two. So in this case, if there are any cells with a value of 100, they will get a new value of 0. Save the raster as hydro_buffer (Output raster). [See Figure 7.5]

   

   Figure 7.5 Assign new values to the classes.
6. Click Run to complete the reclassification
7. To avoid confusion, drag the original dist_hydro layer to the bottom of the Table of Contents or remove it from the data frame.

   Note:
   
   If you want, you can make only the desirable cells visible by making the undesirable cells transparent. Open the Symbology pane and click the colored box next to the 0 value of your newly reclassified raster layer. The color selector will pop up. Click No color.
   
   An alternative way to display the raster layers is to choose "No color" for the desirable cells and choose a color for the undesirable cells. With all of the criteria layers turned on you'll be able to get a preview of which cells meet all of the criteria (they will be the cells with no color).

8. In Tools, click Spatial Analyst Tools > Reclass > Reclassify. Select the lu_raster layer from the Input raster pull-down menu.
9. Select LU_TYPE from the Reclass field drop-down menu. Agricultural and undeveloped land are desirable, so we will give them a New value of 1. Forest and urban land are undesirable, so those cells will get a New value of 0.
10. In the first row, make sure it has the Value as ag and change the New value to 1.
11. In the second row, make sure it has the Value as forest and change the New value to 0.
12. In the third row, make sure it has the Value as undeveloped and change the New value to 1.
13. In the fourth row, make sure it has the Value as urban and change the New value to 0.
14. Save the raster layer as rc_lu_raster and click Run.

In the previous step and the next two steps, you use the Reclassify tool to update cell values in each of the raster layers. You create a binary classification by calculating cells equal to “1” only where existing values meet the layer-specific criteria specified for this analysis. All other cells will be given a value of “0.” You can also think of this as a “True” or “False” type of classification, where cells that meet a particular set of criteria are assigned to “True” and all others are “False.” For example, by default, the Aspect layer created in Part II, Step B uses a total of 9 classes to represent aspect. For this analysis, however, we’re only interested in flat land (values of -1) and land that is oriented to the SE, S, SW, W or NW (112 – 337 degrees). You will assign a “1” to everything that falls within this range and a “0” to everything outside of the range. In this step we are grouping the ranges into broader categories and then assigning binary values of either 1 or 0 to isolate desirable and undesirable areas respectively.

1. In Tools, click Spatial Analyst Tools > Reclass > Reclassify. Select the aspect layer from the Input raster pull-down menu.
2. Click the Classify… button and set the Method: to Equal Interval and number of Classes: to 4.
3. In the Classes text box, change the four values to -.000001, 112, 337, and 360, and click OK.
4. As you can see above, we are only interested in the old values in rows 1 and 3 (values of -1- -.000001 and 112-337), so those are the rows that will get a New value of 1. In the first row, you’ll see that the old values are -1 to -1E-06. Make sure there is a 1 in the New column.
5. In the second row (-1E-06 to 112), type 0 in the New column.
6. In the third row (112 to 337), type 1 in the New column.
7. In the fourth row (337 to 360), type 0 in the New column.
8. Save the layer as rc_aspect and click Run.

1. Use the information contained in Table 7.1 to reclassify data values associated with the MaxWind, MinTemp, SoilDepth, and SoilDrainage layers. Please note that the desirable values are listed first for each raster layer.
   Use a process similar to the one described in the previous step to complete each reclassification.

   Note:
   
   It is a good idea to change the name of each reclassified layer so that you can easily differentiate between the different layers. We suggest that each name be preceded with an "rc" (for reclassified).

2. Modify the symbology, visibility and drawing order of layers in the Table of Contents as necessary.
   Again, you might want to make the undesirable areas transparent so that only the desirable areas are visible (or vice versa).

You have just completed Part III of this lesson, which involved reclassification for the purpose of synthesizing the data. In Part IV, you will use Spatial Analyst’s Raster Calculator to combine data contained in several raster layers into a single output layer. This overlay operation will then enable you to identify areas that meet all of the required criteria for land that is suitable for vineyard development.

In the following steps, you will use Spatial Analyst’s Raster Calculator to combine multiple raster layers into a single layer.

This is a little reminder that a value of 0 indicates that the location is NOT optimal for the cultivation of a new vineyard, and a value of 1 delineates prime areas for vineyard development. Corresponding cell values from the input raster layers will be multiplied together to enumerate the output raster. Cells in the output layer will contain values equal to 0 or 1 and will represent the product of cell values in the input layers. For example, in this step you will combine the floodplain data with the buffered streams to produce a single composite raster layer. All cells in the output layer will have values equal to 1, only if corresponding cells in both of the input layers are equal to 1 (e.g., 1 x 1 = 1). All other cells will have a value of 0. In this scenario, zero values are produced by the following cell value combinations: 0 x 0 = 0, 1 x 0 = 0 or 0 x 1 = 0.

1. In Tools, click Spatial Analyst Tools > Map Algebra > Raster Calculator to begin building an expression that will enable you to multiply two raster layers together.
2. Double-click the hydro_buffer layer located in the Map Algebra expression list box. Double-click the multiplication operator.
3. Finish the expression by double-clicking the flood_raster layer from the list box.
4. Rename the Output raster to flood_hydro.
5. Click Run to perform the calculation.

Examine the results of this operation. You should notice that cell values in the output layer represent the product of corresponding cell values contained in the two input layers. All of the cells that represent floodplain, stream buffers, or both will have a value of 0. All other cells will have a value of 1. For the purposes of this analysis, we are interested in all of the non-zero cells, as these are not located within a floodplain or within 100 meters of a stream or river.

1. Open the Raster Calculator and build an expression to multiply all of the layers listed in Table 2 together. Your layer names may be slightly different.
2. Rename the Output raster as suitablesites.
3. Click Run to execute the calculation.

In this step, you will manipulate elements in the data frame to facilitate better visualization of the optimal vineyard sites.

1. Drag the suitablesites layer to the top of the Table of Contents and place the hydro vector layer, flood_hydro raster layer, and hillshade raster layer in that order just below.
   Remember that a hillshade operation was performed on the elevation layer. Overlaying the suitablesites layer on the hillshade layer allows for the visualization of the optimal sites within the context of the actual terrain.
2. Modify the symbology used to display the suitablesites layer. Make all cells with a value of 0 transparent – No Color.
3. Choose a prominent and appropriate color to symbolize the suitable vineyard sites (those with a value of 1).
4. Select suitable symbolization for the hydro and flood_hydro layers.
   When symbolizing the floodplain, you do not want the non-floodplain area (VALUE field = 1) to obscure the other features in the data frame, so you can choose No Color. You could display the area within the floodplain as a partially transparent color.

Now that you’ve completed the analysis, you might be curious about how much land was suitable for grape cultivation.

1. Open the attribute table of the suitablesites layer and look at the number of cells that have a value of 1 (suitable).
2. To calculate the area of the suitable land in square meters, you first need to remember what cell size was used. How do you calculate the area (square meters in this case) of each cell using the cell size?
3. Multiply the number of square meters by 10.7639 to get the area in square feet.
4. Divide the number of square feet by 43,560 to get the area in acres.

That’s it for Part IV…and Lesson 7!