Step-by-Step Activity: Overview

In the Step-by-Step section for Lesson 2, we will explore a variety of environmental geospatial datasets available online, review metadata, create maps using ArcGIS.com, and share the maps as an interactive web application.

Introduction

In Lesson 2, we are going to create a website that contains interactive maps with datasets related to our project scenario described in the Introduction. We will create maps and web applications using ArcGIS Online. We will save our maps in the cloud using Penn State’s ArcGIS Online for Organizations account. Our final product will look like the example below.

Lesson 2 will cover the major steps listed below

Part I

• Explore Metadata
• Configure ArcGIS Online Account

Part II

• Explore Imagery Data & Land Cover Data
• Create Imagery & Land Cover Map 

Part III

• Configure Swipe Web Application to Display Map

Part I: Metadata

1. Explore Metadata

   It is important to understand the opportunities and limitations of your input datasets before you begin working with them. We will do this by exploring the available metadata. As a geospatial professional, you will often be the only person reviewing this level of detail about the datasets. It will be your job to communicate what you find with the rest of your team.

   1. Use the links to metadata provided in the Lesson Data section to fill in the table below. Note: It is OK if you can’t find the answers to all of the questions for both data sets. This exercise is meant to demonstrate that finding the metadata you need is not always easy.

      Metadata

      Metadata	Imagery	Land cover
      Timeframe (Year or Range)
      Scale (1:x or cell size)
      Organization(s) that Created Data
      Organization Hosting Web Data
      Citation Information Requested by Data Provider
      Description of Coded Attribute Values Included? Y/N

      

      Were there particular pieces of information that were harder to find than others? Did you notice any differences in the quality and ease of use of different data provider’s websites?

      Note: Critical Thinking Questions are not graded. They are provided to help you think about the lesson concepts. I encourage you to share your thoughts on the lesson discussion forum.

2. Confirm your account on ArcGIS Online for Organizations

   Before you can access ArcGIS Online, you need to confirm your account. We will only have to do this once to have access for the rest of the semester. ArcGIS Online has a feature that helps us to manage a group such as this course. In order to take advantage of the Group features, I will need you to "Sign In" to the Penn State ArcGIS Online organization using your Penn State Username and Password. Follow the steps below to Sign In and confirm your account for the first time. There are directions at arcgis.com to create a Personal Account that you can use to complete this course. Note: If you are not enrolled in the class, you will not have access to Penn State’s ArcGIS Online for Organizations account.

   1. Go to ArcGIS Online and login using your university single sign-on account username and password:
      1. Go to ArcGIS Online and login using your Penn State Username and Password. URL: https://pennstate.maps.arcgis.com
         
          
         
      2. This should log you onto Penn State’s ArcGIS Online for Organizations account titled “ArcGIS Online at Penn State”
      3. Click on your name > My Profile (upper right) > My Settings > Licenses and confirm your “Role” as a “Publisher".
      4. Notify the instructor if you are not able to successfully complete this step.
   2. Begin Step #3 once you are able to sign in to the Penn State AGO Organization.
      

      Please do not log in using an account that you created outside of the program. According to Esri’s website, “you will transfer ownership of your items to Penn State's Online for Organizations” for any content that is saved in the account you log in with. This means that any instructors or students using the Penn State account will be able to have administrative rights to your preexisting content.

3. Confirm that you are also listed within our Geog487 Group.

   1. Click the “Groups” tab at the top of the Pennsylvania State University AGO Organization page. This will return a list of all of your Groups, or, you can type in GEOG487 into the search box and select the option that says “Search for Groups” and look for the current semester (e.g., GEOG487 - Summer 2024).

      
      Note: There is a group for each semester. 

   2. Click on the group name to access our Group’s space.
   3. As the course progresses, we will be posting several assignments here for review and discussion.

Part II: Imagery and Land Cover Data

1. Explore Imagery & Create Map 

   1. The first dataset we will explore is offered as a basemap in ArcGIS Online. Log in at Penn State AGO Organization using the Sign In link at the top (if you are not already logged in).
   2. Click on the Content tab
      . 
   3. Select the +New item button.
      
   4. Add the Lesson 2 Study Boundary.
      1. Download the L2Data.zip file from the Lesson Data tab on the main course website if you have not already done so. Be sure to keep the data zipped and follow the folder naming directions specified on the page. You will likely experience trouble later in the semester if you choose to use a different pathname.
      2. Click Your device and then browse to the L2Data zip file. The item type is a Shapefile. Select the "Add the L2Data.zip and create hosted feature layer" radio button. Click Next.  
         
         
          
         
      3. Add a unique Title (e.g., Lesson2Data_YourInitials), Tags, and Summary description of the Lesson 2 study boundary file. Click Save.
      4. You will be redirected to an overview of the Lesson2Data shapefile. Verify and add relevant information.
   5. Click on the Open in Map Viewer button.
      *Alternatively, you can select the Map tab to create an Untitled map in Map Viewer. Select Add a layer, navigate to My Content, and select "Lesson2Data" to add the Feature Layer.
       
   6. An "Untitled map" in a Map Viewer will open with the "Lesson2Data" added as a Feature Layer.
   7.  Click on the "Lesson2Data" layer name to open the layer Properties along the right side. Click on Edit layer style under Symbology.
      
   8. Under #2 "Pick a style" select Style options.
      
   9. A dialog box will open that allows you to change the symbol style of the features in the layer. Click on the pencil to change the style of the "Symbol" of the Lesson 2 study boundary to a hollow red outline. Select the "X" to close the window when your symbol style selections have been made, and click Done two times to exit the Styles options.
      
   10. Click on the Options icon "…" after the file name in the Table of Contents, then "Zoom to" to center the Map on the study area.
   11. Save your Map by clicking on Save  > Save As. Note: It's a good idea to save often, just in case you lose your Internet connection or other unforeseen mishaps occur. Change the title to "Imagery and Land Cover Map," add tags for the class and your name, and write a summary of what the Map shows. The folder's name will default to match your login name (so it won't match the graphic below).
       
   12. Change the name of the Lesson2Data layer to "Study boundary" in the Table of Contents. To do this, click on the "…" after the layer name, then "Rename." Save your Map again to lock in your changes.
       

       Note: Data layers often have default names that will be meaningless to most people who read your Map (like your boss and clients). Make sure you review layer names and change them, so your maps make sense to people other than yourself.

   13. Explore the surrounding area using the zoom and pan functions. To zoom, use the mouse wheel. To pan, left-click your mouse, hold it down, and then drag your mouse in the direction you want to view on the Map.
   14. Change the Basemap to "Imagery Hybrid."
       
        
   15. Explore the surrounding area again using the zoom and pan tools. Save your Map.
       

       Which National Park is the Study Site near?

       Which state(s) is the Study Area located in?

       How much detail can you see in the imagery if you zoom in close?

2. Explore Land Cover Data & Add to Map

   The second dataset we will explore is part of the United States Geological Survey (USGS) National Map. The land cover data service is hosted on their website and server. We can access the data in ArcGIS Online and by using their website (there are other options too).

   
   1. Let's start by exploring their website.
      1. Go to The National Map. Wait a few seconds, and the Map will automatically appear.
         
      2. Notice there are many datasets available in addition to Land Cover. Spend 10-15 minutes exploring the tools and datasets, as you will likely need to use these in many of your own environmental projects.
      3. Try to identify which layer or layers match the data sets we will add in this lesson. See the "Lesson Data" page for specific file names.
      4. Practice customizing the Map by turning layers on and off. You may need to click a button for the Map to redraw.
      5. Experiment with identifying the attributes of a vector data layer. Activate the vector feature layer by placing a check in the Layers List. For example, place a check beside the "Watershed Boundary Dataset" and "WBDLine" in the Layer List and then click on the Map to activate a pop-up containing attribute information.
      6. Look for tools or links to download datasets.
   2. Instead of downloading the data through the website, let's add the NLCD to our Map via ArcGIS Online. We can do this by performing a search. Search ArcGIS Online for the land cover dataset and add it to your Map.
      1. Select Add layer and change the drop to ArcGIS Online. Enter "USA NLCD Land Cover" in the search, and you should see a service published by ESRI. Add the NCLD Land Cover time series layer to your Map. 
         
         
   3. Navigate back to the Map. Change the order of the layers if necessary, and click on the legend icon  to show the Legend. Review the Legend so you can understand what the colors represent.
      
   4. Change the transparency to ~50% so you can see the imagery beneath it. You must click on the "Properties" top right to do this. You can also rename the layer if you would like. Do you remember how we did that earlier in the lesson? 
   5. Explore the land cover for the study area using the zoom and pan tools. Click on the "X" to close the Legend.
   6. Save your Map.
      

      After exploring the same dataset using ArcGIS Online and the online viewer provided by the data creation agency, can you think of any scenarios where one method would be preferable over the other?

Part III: Building a Web App on the Penn State AGO  Website

Now that we have created individual maps for each dataset, we can combine them into one using a template web application provided by Esri. You can use these templates to share your data in an easy-to-use format. Note: You need to be a member of the GEOG487 group to complete this section.

1. Share the Display Map

   1. Click on Home > Content to view the list of maps you created. The map list is found under the My Content tab.
      
      
   2. Share your maps with the class by using the visual guides below.
      
   3. Select “Geography 487 - Semester” 
       
      
       
   4. If necessary, click Update to synchronize sharing of the dependent items you have permission to update (e.g., Lesson2Data).
   5. You should see the Group listed as the Shared Status as shown below.
      
      
      

      We will be sharing our work with the class throughout this course in ArcGIS Online. Please follow Penn State’s Academic Integrity guidelines covered in the Syllabus. (As a group administrator, I will be able to see if you created your own maps or made a copy of another student’s work).I encourage you to view other students' work to learn and be inspired. If you incorporate any of their ideas in your own work, please list their name and map URL in your sources.

   6. Edit the descriptions for a map by clicking on the title. Your text should be short and to the point. What do map viewers need to know about this dataset to use it quickly?
   7. Include the citation information requested by the data provider (see metadata) in the credits section.
      
      

      When designing maps and applications, it is best to assume that your end users don’t know where the study area is and are not familiar with the data – where it came from, what it is supposed to be used for, etc.

      It is your job to point them in the right direction by crafting descriptive titles, useful captions, and helpful legends. Be nice to your audience! A good rule of thumb is to show your map to a non-geospatial friend. If they look confused, you need to revise it.

      Good captions describe what a map shows AND why the reader should care.

      Bad Caption – “This map shows the study area in red.”

      Good Caption – “The study area (shown in red) is located near Yellowstone National Park along the border of Montana and Wyoming. The terrain consists of steep mountains and valleys, making transportation by car difficult.

2. Create Web Application

   1. Click on the Content tab to once again view a list of the maps you have created. 
   2. Click on the + Create app menu. Select Web AppBuilder.
      
   3. Specify the Title: and Tags: for your web app. Make the title meaningful but succinct. The Summary: is optional and folder location will default to your My Content folder.
      
   4. The Web AppBuilder for ArcGIS will open. Click on the Map tab and select Choose web map.
       
   5. Under Choose web map, select the "Imagery and Land Cover Map" 
      
   6. Click on the Widget tab, and select click on a widget placeholder. 
      
   7. The Choose Widget window will open. Select the Swipe widget.
      
   8. Configure the Swipe widget based on the following options. The type of swipe tool will be a Vertical bar, the swipe mode is Single layer, the swipeable layers are "Study Area Boundary" and "USA NLCD Land Cover," and the default swipe layer is "USA NLCD Land Cover."
      
   9. To open the Swipe widget automatically when the app starts, hover over the bottom left of the Swipe icon under the Widget tab and click the circle.
       
      
   10. Select the Map tab. You should see the Swipe widget icon on the upper left corner of the map. Click on this widget icon to turn the widget on and off. Take some time to explore the functionality of the widget. Click on the Vertical bar to move it right or left. Be sure to Save your work.
       
   11. Navigate back to the Content tab within the Penn State AGO Organization and notice that the new App has been added to your My Content list.
   12. Be sure that your App is shared with the GEOG 487 Group.
        
       
   13. Click on the Title of your App to open it. Review your final web application to make sure the titles, descriptions, and other information are correct. You may need to edit the individual maps for the information to update in the web application (legend titles, colors, extent, etc.). Double-check that you listed your map sources.

That’s it for the required portion of the Lesson 2 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.

Step-by-Step Activity: Overview

The Step-by-Step Activity for Lesson 3 is divided into two parts. In Part I, we will look at publicly available datasets from Esri, the Fish & Wildlife Service National Wetlands Inventory, the Great Lakes Coastal Wetlands Inventory, and aerial photos from the USDA Farm Service Agency. In Part II, we will explore site-level data from three time periods that was digitized from high-resolution aerial photos. We will use the various datasets listed above to practice many of the data customization tasks covered in the lesson text.

Part I: Explore Publicly Available Wetlands Data & Historical Imagery

Part I, we will explore our study area and the time-series aerial photos used to digitize the vegetation data we will use in Part II. We will also look at two publicly available datasets specifically related to wetlands: the National Wetlands Inventory from the U.S. Fish & Wildlife Service and a more detailed wetlands inventory from a regional public agency called the Great Lakes Commission. In the process, we will explore several different data delivery options and sources in ArcGIS: Esri Map Packages, Esri Basemaps, ArcGIS Online Datasets, Web Map Services (WMS) from GIS Servers, and raw GIS files.

1. Familiarize Yourself with the Study Area and Set Up Your Map

   1. Open the L3_Data folder you downloaded in the Lesson Data section. Double-click on the “Lesson3.mpkx” map package file. Designate your L3_Data folder as the unpacking location. This will open a map inside ArcGIS Pro with the study area boundary and historical imagery already loaded.
      

      You can share your own data and maps by creating a map package in ArcGIS Pro. Go to the Share tab, Package group, and select Map Package . The file can either be uploaded to ArcGIS Online or saved locally.

   2. Set your Current Workspace and Scratch Workspace to your L3 folder by navigating to the Analysis tab, Geoprocessing group, Environments . Make sure to read the associated help topics about “Current Workspace” and “Scratch Workspace.”
      
      

      Setting your Current Workspace allows you to customize the location of where output files created during geoprocessing steps are saved. By default, files will be saved at …My Documents\ArcGIS.

      To easily access information related to geoprocessing parameters and environments in ArcGIS: Click on the information icon which will open a dialog window with information about usage and options. It is also a good idea to read the help information related to tools you are not familiar with (click  or go to the Project tab > Help).

   3. Add the “Open Street Map” ArcGIS Online Service (go to the Map tab, Layer group, click on Basemap  > select OpenStreetMap) so you can tell where the study area is located in relation to the other places. You may need to refresh your map for the Basemap to load. (You need to be connected to the Internet and Signed In to the PSU AGO Organization using your WebAccess account information).
        
       
   4. Use the Explore tool  found on the Map tab, in the Navigate group to take a look at the study site and the surrounding area. What is the nearest major city? How far away is the site from the state border with Michigan?
   5. Turn off the Basemap, as this can slow down the drawing speed of your map in late steps. Save your project.

2. Explore Historical and Recent Aerial Photos

   1. Zoom to the study area boundary by right-clicking on it in the Contents pane > Zoom to Layer.
   2. Turn on the “2005” layer in the Contents pane. This is a Color Infrared (CIR) image. Notice the information on the edge of the scanned film showing the date, location, and scale of the original image.
   3. Turn on the “1973” and “1962” images. These images are black and white, a common format before 2000.
   4. Compare the three images by turning them on and off and viewing them at a number of different scales: 1:15,000, 1:10,000, and 1:5,000. Try navigating to different locations within the study area. What differences do you notice between the three images?
      

      Take a minute to look at the swipe and flicker tools available on the Raster Layer, Appearance tab, Compare group (be sure an image is highlighted in the Contents pane) These tools can be useful for temporal change detection (especially of satellite images or air photographs that were taken at different times of the same location), data quality comparison, and other scenarios where you want to visually compare the differences between two layers in your map. Swipe allows you to interactively reveal what is underneath a particular layer; Flicker flashes layers on and off at the rate you specify. You can read more about these tools in Esri help. 

   5. The photos that were used to create the detailed vegetation data we will use in Part II all show the study area in the past. Let’s take a look at more current imagery and see if we notice any changes in the vegetation. We’ll use an image from 2018 to 2020 from the National Agricultural Imagery Program (NAIP).
   6. We can add ArcGIS Online data to the Map by going to the Map tab, Layer group, Add Data > Data. Under Portal, click ArcGIS Online.
   7. Search for USA NAIP Imagery. Click OK.
      
   8. The USA NAIP Imagery layer will be added to the Contents pane.
   9. Compare the NAIP Imagery with the historical aerials. What types of changes do you see? Notice that the NAIP Imagery is more natural in color, which is a bit easier to interpret (as compared to color infrared and black-and-white formats).
   10. Turn off the imagery layers in the Contents pane and save your project.

3. Add the boundaries of the Fish & Wildlife Service Wildlife Refuges to your map

   1. Again go to the Map tab, Layer group, Add Data > Data. Under Portal, click ArcGIS Online.
   2. In the ArcGIS Online search window, type “national wildlife refuges” Review the datasets that match the search. Locate the "National Wildlife Refuges” dataset in the results. 
       
   3. Click on the "Details" button to review the metadata. Notice that this data is a Feature Layer and was last modified on 7/25/2022. Click OK to add the layer to the Map.
   4. Drag the study site boundary to the top of the Contents pane so you can see the site boundary on top of the feature service.
   5. Zoom out to 1:250,000 so that you can see the various federal lands in the vicinity. The refuge is shown as a pinkish-filled polygon, which means it is a Great Lakes National Wildlife Refuge. Use the explore tool  to click on the feature and identify the refuge's name.
   6. Notice how the lesson study area only covers a portion of the whole refuge. The study site boundary represents the wetland areas within Ottawa National Wildlife Refuge that are hydraulically connected to Lake Erie. Some of the wetlands in the refuge are excluded from this area because they are hydraulically separated from Lake Erie by dikes and are therefore not susceptible to fluctuations in Lake Erie water levels. Other areas of the refuge are not wetlands.
   7. Right-click on the “FWSBoundaries” layer name in the Contents pane > select Attribute table. Explore the attribute table. Are there any abbreviations you don’t understand?
   8. Again, right-click on the “FWSBoundaries” layer > Properties > Source > Spatial Reference. Look at the spatial reference information. Is it the same as the study site? Does it match the data frame? (Right-click on StudyArea in the Contents pane > Properties > Coordinate Systems). Note: You may experience errors editing a data layer if it does not match that of the data frame.

4. Create a new shapefile of the Ottawa National Wildlife Refuge Boundary

   1. Right now, the refuge boundaries on our map are part of a feature layer. We want to create a new shapefile from just a portion of the records so we can customize it for our study site.
   2. Open the FWSBoundaries attribute table. Click on the “Select by Attributes” icon .
   3. Create a new selection expression to select all of the polygons within the Ottawa National Wildlife Refuge Where "Organization Name" is equal to "Ottawa National Wildlife Refuge". Apply the expression. You should have 1 of 574 records selected. Close the attribute table. Notice that the polygon is a multipart feature, or it has more than one part but is defined as one feature because it references one set of attributes. Confirm this by viewing the attribute table.
   4. Right-click on the layer in the Contents pane and click Data > Export Features. Export the selected features to a new shapefile called “OttawaNWR.shp” to your L3 folder. Make sure to go to the Environments tab in the Export Features dialog to establish the Output Coordinates as the same coordinate system as the Current Map [Study Area], or you may not be able to edit this data later on in the lesson.
   5. The “OttawaNWR” shapefile will be added to your map. Verify the Coordinate System (Properties > Source > Spatial Reference) is the same as the StudyArea Map. Remove the “FWSBoudaries” dataset from your map and save the project.
   6. Save your project.

5. Download and Explore the National Wetlands Inventory (NWI) Data

   1. Connect to the National Wetlands Inventory data service. Go to the Insert tab, Project group, and click on Connections > Server > New WMS Server > Copy and paste the URL: https://fwspublicservices.wim.usgs.gov/wetlandsmapservice/services/Wetlands/MapServer/WMSServer?request=GetCapabilities&service=WMS and click OK.
   2. Go to the Map tab, Layer group, Add Data > Data.  (Note: If you cannot view the layer using Add Data, try dragging the wetland layer from the Catalog pane > Project > Servers to the Map).
   3. Under the Project folder, click on Servers. Double-click on WMS (https://www.fws.gov/program/national-wetlands-inventory/web-mapping-serv...) > WMS > Layers. Highlight the Wetlands layer and click OK to add the data set to your map. The layer will be added to the Contents pane and will be named WMS > Wetlands.
   4. Drag the WMS Wetlands layer toward the top of the Contents pane but under the Study_Site layer. Zoom to the study area layer and explore the data.
   5. Use the Explore tool from the Map tab to see what information is included with the layer. Notice that you can’t view an attribute table like you could with the feature layers.
   6. According to the metadata, “the data are intended for use with base maps and digital aerial photography at a scale of 1:12,000 or smaller. Due to the scale, the primary intended use is for regional and watershed data display and analysis, rather than specific project data analysis.”
   7. Unfortunately, the data does not contain enough detail to help us analyze vegetation changes in a specific wetland. It also does not allow us to study changes over time.
   8. Turn off the “WMS” layer in the Contents pane and save your project.

6. Explore the Great Lakes Coastal Wetland Inventory Data

   1. Read a quick description of the data at Great Lakes Commission (scroll down to the "Links to Inventory and Metadata" heading). Notice the link to the product metadata provided on the site. We are going to use one file from this site called, “the complete polygon coverage in shapefile format.”
   2. Download the data zip file from the site or from the Lesson Data page (glcwc_cwi_polygons.zip - 12.46 MB), save it in your L3 folder, and unzip the file.
   3. Add the “glcwc_cwi_polygon” shapefile to your map.
   4. Open the attribute table and explore the available information. Notice how there is significantly more information than the previous wetland datasets we reviewed.
   5. The “HGM_CLS1” and “HGM_CLS2” attributes show the wetland classification codes. You can read detailed descriptions of these codes in the “Great Lakes Coastal Wetlands" (enhanced metadata) document. 
   6. Right-click on the “glcwc_cwi_polygon” shapefile in the Contents pane > Zoom to Layer. It is difficult to see any detail.
   7. Zoom back to the Study_Site layer. Do you see any wetlands inside the Ottawa National Wildlife Refuge that are not inside our study site boundary? You may need to move the Study_Site boundary layer and OttawaNWR layers to the top of your Contents pane.
   8. Turn the layer off in your Contents pane and save your project.
   9. The Great Lakes Coastal Wetland Inventory provides much more detailed attribute information than the National Wetlands Inventory. However, it still doesn’t provide the time-series information we need to answer our research questions. For time-series data, we need to look at yet another data set.

Part II: Explore & Customize Site Level, Time Series Wetlands Data 

The publicly available datasets we just explored are helpful for familiarizing yourself with your study area or for regional or other large-scale analyses. However, they do not contain the level of detail for the site level analysis we want to conduct. The highest resolution data you can usually find is 1:24,000 scale for vector data and 30 m cell size for raster data. Publicly available datasets also typically do not have time-series information available. In Part II, we are going to explore a high resolution, time-series dataset that was digitized from the aerial photos we reviewed in Part I. Oftentimes, you will need to digitize information in this manner if you have a small study site or if you want to do an in-depth, time series analysis. The work required to create the data is significant. However, you can do a lot more with your data.

We want to explore how vegetation has changed over time in our study area. To answer our research questions, we need the following datasets: 1) polygons of vegetation species over time 2) polygons of vegetation groups over time, and 3) polygons of invasive species over time. All of the files need to show just the region within our study site. We will create these custom datasets for three time periods using the Join, Union, Clip, and Dissolve tools. The workflow we will follow is illustrated in the diagram below using the data for the seventies time period. You may wish to consult this diagram after completing each step.

Flow Diagram of Needed Data Sets

Click here for a text description of the image above

1. Seventies + VEGCODE + join = 70s_Join
2. 70s_Join + Study Site + union = 70s_union
3. 70s_union + Study Site + clip = 70s_species
4. 70s_species + dissolve = 70s_VegGrp + 70s_Invasive

Credit: R. Kornak © Penn State University is licensed under CC BY-NC-SA 4.0

1. Explore the Site Level Vegetation Data

   1. Open the map from Part I. 
   2. Add the “sixties,” “seventies,” and “twothousands” vegetation shapefiles from the L3 folder to your map.
   3. The default symbology should show the vegetation polygons filled in. Do you see any gaps in coverage between the vegetation data and site boundary? Hint: turn some of the layers on and off and use the zoom tools. Look along the coastline along the northeast boundary.
   4. Compare the extents of the vegetation data and study site. Do you see any differences?
   5. Open the attribute tables. Do you notice any differences in the number of records in each dataset? Do you see any coded or missing values? Missing data may sometimes be coded as values of “0.”
   6. Notice how the vegetation files contain a lot more spatial detail than the publicly available datasets we looked at earlier. At this point, we do not know what the values in “VEG_ID” mean, though we can assume they correspond to different types of vegetation. Even without knowing what the “VEG_ID’s” mean, we can still tell that the "twothousands" data has a lot more polygons than the other time periods. What do you think the VEG_ID code “11” means?

2. Understand Coded Values

   1. Now that we have a general sense of what our starting data looks like, we can work on customizing it for our purposes. Let’s start by figuring out what the coded values in the “VEG_ID” fields mean.
   2. Add the “VEGCODE.dbf” table from your L3 folder to your map. Open the table. 
   3. The VEGCODE table is a master lookup table that tells us what the coded values (VEG_IDs) mean. The VEG_IDs correspond with detailed vegetation types (Veg_Type).
   4. I have reclassified this information for you into 2 simpler categories: Veg_Group and Invasive. The numbers at the beginning allow us to sort the values based on the depth of water they prefer (e.g., most water (open water) to least water (upland vegetation) instead of alphabetically by name).

      Note: You may notice that some of the Veg_IDs are listed as “May Be Invasive” in the “Invasive” field. Two of the most common invasive species in the wetland (narrow-leaved and hybrid narrow/broad-leaved cattails) look very similar to native species (broad-leaved cattails,) which makes them difficult to distinguish in aerial photos.

   5. Join the VEGCODE table to the sixties vegetation shapefile Right-click on the “sixties” shapefile > Joins and Relates > Add Join. Base the join on the Veg_IDs. Keep All Target Features. Validate the join and then click OK.

      Caution - watch out for similar attribute names like OID. This is not the same as Veg_ID.
       

      

      Add Join
   6. Open the attribute table of the sixties shapefile to make sure the join worked properly.
   7. To make the join permanent, export features to a new shapefile in your L3 folder called “60s_Join.” Be sure the new shapefile is added to your map.
   8. Repeat steps e - g for the remaining two vegetation data sets. Name them “70s_Join” and “00s_Join.” Remove the “sixties,” “seventies,” “twothousands,” and VEGCODE table from your map and save.
   

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

   The 60s_Join, 70s_Join, and 00s_Join shapefiles should have the number of records and all of the attributes shown below. If your data does not match this, go back and redo the previous step.
    

   

   60s_Join - 352, 70s_Join – 280 records and 00s_Join – 449 records.

3. Modify Extents - Fill in Gaps

   1. We want all of our input data sets to have the same total area so we can compare changes in the area of different vegetation types over time. This means we need to remove pieces from some study years and fill in gaps for other years so they all cover the same extent.
   2. First, we will fill in gaps. A quick way to fill in gaps is to use the union tool to create new polygons in areas that overlap within the extent of two datasets. We will union the vegetation shapefiles with the study boundary since this file does not have any gaps and it covers the area we are interested in. Follow the steps below to union the two files:
   3. Go to the Analysis tab, Tools group > Union

      

      Union tool.
   4. Click on the “Show Help”  button for more information about what the tool does and what the different input criterion means.
   5. Input Features: Study Site; 60s_Join (make sure the study site is listed first).
   6. Output Feature Class: 60s_Union.shp (save it in your L3 folder not in the default.gdb).
   7. Keep the defaults for Join Attributes (ALL). 
   8. Make sure the “Gaps Allowed” checkbox is NOT checked. Read the help topic about this, so you understand why.
   9. Keep the defaults for the Environments.
   10. Click Run.
   11. Compare the output file to the original shapefile from the same time period to make sure the tool worked as expected. Notice the records that have an FID_Study_ value of -1. What do these mean? Hint: See step vi.
4. Repeat union for the remaining two vegetation shapefiles “70s_Join” and “00s_Join.” Name them “70s_Union” and “00s_Union.”

Geodatabases may have naming restrictions for table and field names. For instance, a table in a file geodatabase cannot start with a number or a special character such as an asterisk (*) or percent sign (%). Shapefiles do not have such restrictions and allow us to use names such as 60s_Join.

If you receive an Error 000361: The name starts with an invalid character during geoprocessing, check to make sure you are saving your output as a shapefile.

It’s very easy to make mistakes when using geoprocessing tools. For example, you can select the wrong input files by mistake. Another common error is running tools while unknowingly having records selected. Any output from geoprocessing tools will only contain the selected records. Comparing your results with your input datasets after using automated tools is a good habitat to get into.

If you want to double-check the input files you used previously, the parameter settings, environment settings, etc., you can view them under Geoprocessing > History.

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

• 60s_Union should have 367 records
• 70s_Union should have 289 records
• 00s_Union should have 458 records

If your data does not match this, go back and redo the previous step.

• Modify Extents - Remove Excess Area

  1. Notice that some of the study years still cover a larger area than others. For example, compare the 60s_Union shapefile with the 70s_Union shapefile. Let’s change the extent of all data sources to match the study area boundary by using the clip tool. Note: The Clip tool is only for vector datasets. In later lessons, we will look at tools to clip raster datasets.
     1. Go to the Analysis tab, Tools group > Clip 
     2. Click on the “Show Help”  button for more information about what the tool does and what the different input criteria mean.
     3. Input Features: 60s_Union.
     4. Clip Features: Study_Site.
     5. Output Feature Class: 60s_Species.shp (save it in your L3 folder).
     6. Keep the defaults for Environments.
     7. Click Run.
  2. Compare the output file to the joined shapefile and unioned shapefile from the same time period. What differences do you notice?
  3. Clip 70s_Union and 00s_Union using the directions above. Name them “70s_Species” and “00s_Species.”
     

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

     • 60s_Species should have 240 records
     • 70s_Species should have 206 records
     • 00s_Species should have 325 records

     If your data does not match this, go back and redo the previous step.

  4. Remove the original vegetation, unioned, and joined shapefiles from your map and save the project.
  5. All of our study years should now have the same extent. Let’s confirm this by calculating the area of each study year. Add a new double field  to each year called “sqm” with the default scale and precision values. I find it helpful to name fields by their units so I remember what they mean later on. Remember to  your changes.
     

     Specifying a specific precision and scale when adding a field to a shapefile gives you the option to limit the number of digits (precision) and decimal places (scale) of values within number fields. There are many situations where you would want to do this. However, there are also occasions where it is best to keep all of your options open. Accepting the default value of 0 for both properties gives you the most versatility. It may seem counterintuitive, but the value of 0 acts somewhat similar to the value of infinity in this case. Setting custom precision and scale values is only relevant to data stored in an enterprise geodatabase. Default values are always enforced when editing data in a shapefile or file geodatabase. Refer to the ArcGIS field data types for more information.

     I recommend using values of 0 when you are in the preliminary stages of data exploration. That way you won’t unknowingly exclude values in your results. For example, if you are calculating area values for the first time, you probably won’t know how many digits you will need to store your calculated values (precision) until after you’ve made the calculation. If you estimate a number to use for precision that ends up being too low, you will not be able to store the full range of values. For example, a precision of 2 would limit your values to two digits, whereas a precision of 4 would limit your values to four digits.

  6. In the open attribute table, right-click on the “sqm” field and click Calculate Geometry. Choose area and units of square meters. Use the coordinate system of the data source. Repeat this step for the remaining two shapefiles.
     

     Calculate Geometry
  7. Use the statistics tool to find the total area for each year by right-clicking on the “sqm” field and choosing “Statistics”. All of the study years should have the same “SUM” value. You may notice very small differences between the layers. This is due to tiny topology errors such as overlapping sliver polygons. We could have corrected these with the Environments > XY Tolerance settings during our union and clip operations if we needed this level of precision for our analysis. In this case, we don’t, but I wanted to point out this issue in case you come across it in other projects. For more information about XY Tolerance, see the Esri Help.

• Explore Attributes & Missing Data

  1. Now that we’ve fixed the geometry of our input data, we can start to work with the attributes. Before inputting data into an analysis, you should have a good understanding of the distribution of your values. You should also be aware of any missing values or outliers that can skew your results, so you can exclude or recode these if necessary.
  2. One way to quickly get a sense of the distribution of data values and missing data is to change the symbology to be categorical based on each attribute.
  3. Right-click on the 60s_Species shapefile in the Contents pane > Symbology. The Symbology pane will open to the right. Under Primary symbology > Unique values > Field 1 Veg_Type.
     

     Symbology pane
  4. Expand the More dropdown list and click on Show Count. How many polygons have missing data (blank entry) in the “Value” column? Do you see any values with typos?
  5. Repeat this process for the “Veg_Group” and “Invasive” variables.
  6. Repeat steps c and d for the remaining two time periods (“70s_Species” and “00s_Species”).

• Generalize Data

  1. For our analysis, we are particularly interested in two attributes, “Veg_Group” and “Invasive.” Right now, each polygon represents vegetation clusters of the same species, which is more detailed than we need. We want to create two new data sets in which the polygons represent clusters of vegetation groups and clusters of invasive types over time. We will use these customized data sets in Lesson 4, where we will discuss how to interpret and present results from several datasets.
  2. First, let’s create the time series shapefiles of vegetation groups.
     1. Go to the Analysis tab, Geoprocessing group > Tools .
     2. In the Geoprocessing pane, search for "Dissolve".
     3. Click on the “Show Help” button  for more information about what the tool does and what the different input criteria mean.
     4. Input Features: 60s_Species.
     5. Output Feature Class: 60s_VegGrp.shp (save it in your L3 folder, not the default.gdb).
     6. Dissolve Field: Veg_Group.
     7. Statistics Field: Select “sqm” as the field and “SUM” as the statistics type.
     8. “Create multipart features” should be checked.
     9. “Unsplit lines” should NOT be checked.
  3. Compare the output file to the input file from the same time period.
  4. Repeat the dissolve for the remaining two time periods. Name them “70s_VegGrp” and “00s_VegGrp.”
     

     Summary Statistics tool (go to the Analysis tab, Geoprocessing group >Tools  > search "Summary_Statistics") is another option you can use to calculate statistics for your data. This tool is similar to the “Summarize” option available by right clicking on a field in an attribute table. The advantage of the Summary Statistics tool is that it allows you to create statistics based on multiple fields. For example, you could use it to find the total area for every unique combination of vegetation type and invasive classification. You could interpret the results to find out which plant type makes up the majority of invasive species for each time period.

     Multipart polygons are features that have more than one polygon for each row in the attribute table. If you want to explode these into individual records at a later time, there is a tool available on the Edit tab, in the Tools group.

  5. Now, let’s create the time series shapefiles by invasive type.
     1. Go to the Analysis tab, Geoprocessing group > Tools.
     2. In the Geoprocessing pane, search for "Dissolve".
     3. Input Features: 60s_Species.
     4. Output Feature Class: 60s_Invasive.shp (save it in your L3 folder).
     5. Dissolve Field: Invasive.
     6. Statistics Field: Select “sqm” as the field and “SUM” as the Statistics Type.
     7. “Create multipart features” should be checked.
     8. “Unsplit lines” should NOT be checked.
  6. Compare the output file to the input file from the same time period.
  7. Repeat step c for the remaining two time periods. Name the output files “70s_Invasive” and “00s_Invasive.”
  8. Remove the “60s_Species,” “70s_Species,” and “00s_Species” from your map and save.

That’s it for the required portion of the Lesson 3 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.

After experimenting with online data services in Lesson 2 and raw data in Lesson 3, which do you think is easier to work with? What are the pros and cons of each one? Can you think of any scenarios in which one is preferable over the over?

Do you have a good understanding of why we completed each step in Part II? If not, compare the starting vegetation files and final outputs (XX_Species, XX_VegGrp, XX_Invasive) in terms of extent, area, gaps, spatial detail, and attributes.

Step-by-Step Activity: Overview

In Part I, we will explore tools to visually explore and compare multiple datasets, such as animations and layouts with multiple map frames. In Part II, we will explore tools to statistically compare multiple datasets, including calculating percent area and creating graphs. We will use both techniques to interpret our data and explore how vegetation within our study area changes over time as it responds to changes in water levels.

Part I: Visually Explore Trends

Part I, we will explore several tools and technique to make it easier to visually interpret patterns in your data using ArcGIS. These can be especially helpful when you have multiple datasets to compare.

1. Organize Your Map and Data

   1. Open a new blank Map and save the project in your L4 folder (uncheck "Create a new folder for this project").
   2. Set your Current Workspace and Scratch Workspace to your L4 folder by navigating to the Analysis tab, Geoprocessing group, Environments . 
   3. Add the study area boundary (Study_Site), Ottawa National Wildlife Refuge boundary (OttawaNWR), polygons of vegetation groups (60s_VegGrp, 70s_VegGrp, 00s_VegGrp), and polygons of invasive species classes (60s_Invasive, 70s_Invasive, and 00s_Invasive) from your L4 folder.
   4. Change the study site and refuge boundaries symbology to hollow outlines. You don’t need to alter the symbology of the remaining layers since we will be doing this in Step 3.
   5. Check the projection of the current Map by right-clicking on “Map” in the Contents pane > Properties > Coordinate System. It should say “NAD_1983_UTM_Zone_17N.”
   6. Add the Open Street Map layer as a Basemap.
   7. Save your project. 

2. Review Contextual Information

   1. One of our main research questions is how vegetation within our study area changes over time in response to fluctuating water levels. We need to know what the water elevation was for our study area for each of our study years. Since the wetlands in our study area are hydraulically connected to Lake Erie, we know they will both have the same water elevation.
   2. The water levels in the table below are from the Lake Erie Hydrograph (graph of water level over time). Compare the water levels for each year and rank them as high, medium, or low.

      Year	water Level (m)	High, Med, Low
      1962	173.9
      1973	174.9
      2005	174.2

      

      Based on the Lake Erie Hydrograph, how do the water levels for 1962, 1973, and 2005 compare to the long term averages for Lake Erie? Which years had the highest and lowest water levels between 1920 and the present?

3. Set Layer Symbology

   1. One of the first steps to understand trends in your data is to simply look at the spatial patterns. To compare time-series datasets, you want to make sure all of your layers have the same symbology settings. If not, it won’t make sense to visually compare them because changes in patterns may be related to different symbology settings instead of changes in your actual data.
   2. Layer files (.lyr) are a way to save symbology settings in ArcGIS. I’ve already created layer files for you for two purposes, first to save you time and second to demonstrate some of the tools available in ArcGIS to make your life easier.
   3. Highlight the 00s_VegGrp in the Contents pane. Under Feature Layer, on the Appearance tab, in the Drawing group, click Import. This will allow you to import symbology from symbology layer file. Browse to the 00s_VegGrp.lyr file in your L4 folder. Make sure the Value Field matches as shown below. Click OK to apply and import the Symbology. Repeat for the 70s_VegGrp and the 60s_Veg_Grp layers in the Contents pane. Import the 00_VegGrp.lyr as the Symbology layer for both.
      
   4. Use the 00s_Invasive.lyr layer in your L4 folder to set the symbology for the time series invasive shapefiles. Save your map.
   5. Turn the different layers on and off to explore the changes over time.
      

      One of the challenges of looking at time-series data of the same location is that all of the datasets overlap each other. It is very difficult to see all of the datasets at the same time if you have them all on the same map, especially if they are polygon files.

4. Create Time Series Animation

   1. Turning layers on and off manually is not really the best technique to visualize changes over time, especially if you have a lot of datasets or if you want to repeat the task many times. ArcGIS has a tool that allows you to set up animations of datasets that are in the same Map.
   2. To use this tool, I like to organize our data into different group layers within the Contents pane. Hold down the Ctrl key and select the “60s_VegGrp,” “70s_VegGrp,” and “00s_VegGrp” layers. Right-click > Group.
   3. Name the group “Vegetation Groups.” Repeat for the invasive species shapefiles. Name the group “Invasive.”
   4. The order in which the layers appear in the animation we are going to create is based on the order the layers are arranged Contents. Arrange the layers within each group so they increase in time from top to bottom like the example below.
      
      

      In this lesson, we arranged the layers within each group chronologically. You could also arrange them in a different order, such as by their water level (low, medium, high) to visualize how the vegetation changes correlate with water level changes.

   5. We’ll start by creating an animation of the vegetation groups over time. Turn the “Invasive Group Layer” off in the Contents pane.
   6. In the Contents pane, make only the "Study_Site" and "OttawaNWR" layers visible. Go to the View tab, Animation group, and select Add .
   7. Select the Append tool  from the Create group. An Animation Timeline window will open with one thumbnail image in the Keyframe Gallery (showing the study site and Ottawa NWR layers at zero seconds).
   8. Continue to append keyframes for each Vegetation Group layer by clicking the append next keyframe button  after updating your map by turning the layers off and on.
      
   9. Click the play button  to preview your animation. You can adjust the duration if necessary.
   10. 

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

       When you preview your animation, you should see one layer turned on at a time beginning with the VegGrp_60s and ending with the VegGrp_00s.

       If your data is not close to the example, go back and redo the previous step. You’ll need to clear the animation first by going to the View tab, Animation group, select Remove.

       
   11. Once you think that the animation is ready go to the Animation tab, Export group, and select Export Movie . Export the animation to your L4 folder (the example was exported to a .gif).
   12. Now we’ll create an animation of the invasive species data over time using the Invasive Group Layer. Make sure you uncheck the “Vegetation Group Layer” and check the “Invasive Group Layer” in the Contents pane.
   13. Go to the Animation tab, Manage group, and select Create Animation  to create a second animation within the Lesson4 project.
   14. Repeat steps g and h using the Invasive Group layers.
   15. Preview your animation. Once you are satisfied with it, save it in your L4 folder. Go to the Animation tab, Export group, and select Export Movie.
       

       If you want to be able to view your animation outside of ArcGIS, you can export your animation to a video file. You can also make your animations more sophisticated by exploring the available animation tools and options within ArcGIS. For example, you can add looping, string multiple animations together, add time-series labels, and add graphs that update over time along with your animation. You can find more information, such as help articles, sample animations, and tips in the Esri help topics.

5. Create a Map Layout with Multiple Map Frames

   Animations are great for emailing to a client or adding to a presentation. However, if you want to print your maps, you need to create a layout. We are going to create a layout with multiple map frames to make it easier to compare our data over time. When working with multiple map frames that show similar information, it is easier to set the symbology, extent, and scale in one map, then make copies of the map, instead of setting up each map separately.

   The final map layout should include all of the following elements:

   • 7 map frames:
     • The six main map frames should show the study area boundary, the Ottawa National Wildlife Boundary, and the Open Street Map layer.
       • 3 of these map frames should show the vegetation group data, one for each time period (60s, 70s, 00s), each with its own title.
       • 3 of these map frames should show the invasive species data, one for each time period (60s, 70s, 00s), each with its own title.
     • 1 map frame should contain a locator map (see instructions below). This should be at a scale to show the study area in relation to the state of Ohio. It should include the Open Street Map layer and the location of the study area.
   • Legend (do not use default layer names with “_” or abbreviations)
   • The water level during each time period (m)
   • Scale (with units of km or miles)
   • North Arrow
   • Source Information:

   1. In ArcGIS Pro, if two or more map frames reference the same map, any manipulation to the layers in the map (such as turning any layer on or off or zooming in or out) affects both map frames because the layout is referencing the same Map. To bypass this, a separate Map must be referenced for each Map Frame in a Layout. Go to the Insert tab, Project group, and select New Map. Insert six New Maps to your project (each should default to a different name Map, Map1, Map2, Map3...).

   2. Switch back to your original Map. Switch off the Open Street Map Basemap for now, as it will increase the loading time while you are setting up your layout. Adjust your scale and extent, right-click on “Study_Site” in the Contents pane > Zoom to Layer. Turn on the 60sVegGrp layer.

   3. Hold down the control key and highlight the "Study_Site", "OttawaNWR", "Vegetation Group" and "OpenStreetMap" layers in the Contents pane. Right-click and select Copy.

   4. Go to Map1, right-click on the map name in the Contents pane > Paste. Turn the Study_Site, OttawaNWR, and 70sVegGrp layers on. Do the same in Map2 but turn Study_Site, OttawaNWR, and 00sVegGrp layers on.

   5. Switch back to your original Map. Adjust your scale and extent, right-click on “Study_Site” in the Contents pane > Zoom to Layer.

   6. Hold down the control key and highlight the "Study_Site", "OttawaNWR", "Invasive Group" and "OpenStreetMap" layers in the Contents pane. Right-click and select Copy.

   7. Go to Map3, right-click on the Map name in the Contents pane > Paste. Turn the Study_Site, OttawaNWR, and 60s_Invasive layers on. Do the same in Map4 but turn Study_Site, OttawaNWR, and 70s_Invasive the layers on. And, then in Map5 turn on Study_Site, OttawaNWR, and 00s_Invasive the layers on. 

   8. Go to Map6, right-click on the Map name in the Contents pane > Paste. Turn the Study_Site, and OttawaNWR layers on.

   9. Go to the Insert tab, Project group, and select New Layout.
   10. Set up the page layout. Choose a Portrait page size of Tabloid (or another 11x 17-inch equivalent).
   11. Go to the Insert tab, Map Frames group, and click on the Map Frame tool . Select the default extent of the original Map in the gallery. On the layout, click and drag a rectangle to create the map frame for the original Map. Insert a total of 7 map frames (i.e., Map, Map1, Map2, Map3, Map4, Map5, and Map6) in your layout.
   12. Select the Map6 map frame in the layout to activate it, and then right-click > Properties. In the Format Map Frame pane, select the placement button, and adjust the Width and Height. Resize the Map6 map frame to be 3 inches by 2 inches. This will become the locator map.
   13. Next, we’ll organize the map frames in the layout. Go to the Layout tab, Show group, and check the Rulers and Guides boxes.
   14. We’ll start by setting up guides and snapping to make it easier to format your layout. To add guides, right-click the ruler and pick "Add Guide" or "Add Guides". Pick Add Guide to create a single vertical or horizontal blue guide at the location you right-clicked the ruler. Pick Add Guides to open a dialog box with options for placing guides at exact locations.
   15. At the bottom of the layout view, click the Snapping button on. Also, click on the “Snap to guides” and "Snap to other elements" while in this option.
       
   16. Drag or reposition the map frames so they look like the example in the following template example. The locator map should fit in the area that says “Insert legend and other map elements here” in the graphic below.
       
   17. If one of your map frames has a handlebar border, this means it is the active map frame. The title of the active map frame will also appear bold in the Layout Contents pane. You can change which map frame is active by either selecting it in the layout or right-clicking on its name in the Contents pane > Activate.
       
       Notice that all of the map frames are named “Map Frame.” Next, we’ll rename each map frame so we can tell which map frame belongs to which contents entry. It may help to click the arrow next to the titles in the Contents pane to hide the contents.
       
   18. In the layout, right-click on the map frame in the top left > Properties. The Format Map Frame pane will open, under General type “1962 Veg Groups” in the “Name”. Notice the map frame name also updates in the Contents pane. Rename the remaining map frames as shown in the graphic from step g above. Name the smallest map frame “Locator Map.”
   19. Remove the datasets that do not belong in each of the map frames. For example, in the “1962 Veg Group” remove the 70s and 00s VegGrp and all the invasive shapefiles.
   20. Insert a legend, scale bar, north arrow (you only need one of each since all of the map frames have the same symbology and scale), titles for each map frame, and source information.
   21. You will have to adjust the Legend to show all the layers in the map frames. After you insert the legend, right-click > Properties to see the Format Legend pane. Go to Options > Legend Items > Show properties and try turning off the Layer names to see multiple items in the legend. You may have to experiment a bit.
   22. Include the water levels for each year, from the table earlier in this document, on your map layout to aid in showing relationships between the water levels and the map layers.
   23. Turn back on the base maps in all of the map frames and save your project.
       

       Adding neatlines to your map layouts helps to visually group elements together. This is helpful when your map has a lot of information. Go to the Insert tab, Graphics and Text group, and then click on Rectangle. After you place the rectangle in the layout, you can select it and right-click to format and adjust the symbology settings of the neatline.

6. Visually Interpret Trends Using Maps

   1. Use the map layout you created in Step 5 to try to answer the following questions. We will repeat this exercise in Part II using statistical techniques instead of visual techniques.
      • How has the amount and location of emergent vegetation changed over time? For example, has it increased or decreased?
      • How has the amount and location of invasive species changed over time?
      • How has the quality of habitat changed over time?
      • How has the amount of emergent vegetation changed in response to water level fluctuations?

Part II: Statistically Explore Trends

Visually exploring your data is a good way to start interpreting your results. However, it is difficult to determine the magnitude of change just by looking at a map. Calculating statistics allows you to have actual numbers to work with, allowing you to say that “variable x increased by 12%” instead of “variable x increased.”

While calculating statistics, it is very easy to make mistakes such as typos, choosing incorrect input layers, or using incorrect order of operations. To avoid possible errors, you should first visually explore your data so you have an idea of the trends that exist in the data. After calculating statistics, you can compare your results to your visual interpretation to make sure your statistical results seem reasonable.

1. Calculate Area Statistics

   1. Use the attribute tables of the vegetation groups and invasive species data to fill in the table below. You will use this table to answer some of the Lesson 4 Quiz questions.

      Study Results Work Table

      Study Year	Water Level (High, Med, Low)	Area Open Water (sq m)	Area Emergent Vegetation (sq m)	Area Invasive Species (sq m)	Area Controlled Invasive Species (sq m)
      1962
      1973
      2005

      

      Which year has the most emergent vegetation? Which year has the most open water? Did you find it difficult to compare such complex numbers (lots of digits and decimal places)?
      
       

   
   
   2. Another technique to compare multiple datasets is to use percent of total area values instead of actual areas. It is important that all of the datasets you want to compare have the same area to use this technique, which is why we had to union and clip our starting data with the Study Area Boundary in Lesson 3.
   3. Add a new short integer field to the 60s_VegGrp named “pct_tot.” In this case, we are using an integer data type since we are not concerned with decimal places.
   4. Calculate the percent total of each vegetation group using the field calculator. (Percent Total Area = Area of Each VegGroup/Area of All VegGroups * 100). Hint: You can use the Statistics tool to easily find the combined area of all VegGroups. Right-click the SUM_sqm field. The graphics below show the area value from the 60s_Veg_Group file. There may be a slight difference in the total area values between the different layers.
      
      
   5. Repeat for all of the remaining vegetation and invasive shapefiles.
   6. Fill in the table below based on your results. You will use this table to answer some of the Lesson 4 Quiz questions.

      Study Results Work Table 2

      Study Year	Water Level (High, Med, Low)	% Tot. Area Open Water	% Tot. Area Emergent Vegetation	% Total Area Invasive	% Tot. Area Controlled Invasive
      1962
      1973
      2005

      

      Which year has the most invasive species? Which year has the least open water? How does this correlate with water levels? Which files have the most missing data? After comparing several datasets using calculated areas and percent total areas, which technique do you find is easier to detect trends between multiple datasets?

2. Create Graphs from Attribute Tables

   1. You can combine statistical techniques with visual techniques by creating graphs from your attribute tables. There are many different types of graphs to choose from. In this lesson, we will look at two options: pie charts and vertical bar charts.
   2. Let's look at a vertical bar chart. In the Contents pane of your original Lesson 4 Map, right-click the 60s_VegGrp layer > Create Chart > Bar Chart. A Chart Properties pane will open that guides you through the graph creation process. Use the settings below:
      1. Category or Date: Veg_Group
      2. Aggregation: <none>
      3. Numeric field (s): SUM_sqm
      4. Check the box “Label bars”
      5. Click Apply
   3. Click General, give the graph a meaningful title and meaningful axis titles. Note: Do not use the default names, which have “_” and abbreviations that may be confusing to your target audience.
   4. Accept the defaults for the remaining options. You may need to resize it to view all of the labels.
      
   5. Look at the output graph. Is it easy to tell how the amount of vegetation within each group compares to other groups? Notice how the y-axis defaults to the highest value in your dataset. If you wanted to compare graphs from multiple datasets, you would need to make sure that all of the graphs have the same minimum and maximum values on the y-axis. You can add the graph directly to your layout. We are not going to do this in this lesson, but you could see how this may be valuable for other projects, especially if you combined it with the available animation tools.

3. Interpret Trends Using Statistics and Graphs

   1. Use the statistics and graph you calculated to answer the following questions again. Compare them to your answers from step 6 of Part I.
      • How has the amount and location of emergent vegetation changed over time?
      • How has the amount and location of invasive species changed over time?
      • How has the quality of habitat changed over time?
      • How has the amount of emergent vegetation changed in response to water level fluctuations?

After experimenting with both visual and statistical techniques to determine trends in your data, can you think of any scenarios in which one is preferable over the other?

That’s it for the required portion of the Lesson 4 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.

Step-by-Step Activity: Overview

The Step-by-Step Activity for Lesson 5 is divided into two parts. In Part I, we will look at and obtain a publicly available historical land use dataset from the Pennsylvania Spatial Data Access (PASDA) website. We will also review the 1978 historical land cover data included with the lesson. In Part II, we will standardize the land cover data for analysis. Then we will determine the land cover area per category for each county using the Tabulate Area and Join Tools. Finally, we will calculate the percent change for land cover categories between 1978 and 2005.

Lesson 5 Step-by-Step Activity Download

Note: You should not complete this step until you have read through all of the pages under the Lesson 5 Module. See the Lesson 5 Overview and Checklist for further information.

Part I: Acquire Data and Organize the Map

In Part I, we will explore and obtain publicly available datasets from the Pennsylvania Spatial Data Access (PASDA ) website. We will also review the private data for this lesson and organize the map for analysis.

1. Download the Historical Land Use Data

   1. Download the data from the Lesson Data page and extract the files.
   2. Go to Pennsylvania Spatial Data Access.
   3. Under "Search Data by Keyword" enter "PAMAP land cover".
   4. Scroll down to locate "PAMAP Program Land Cover for Pennsylvania, 2005" and click on the Download name under data description.
   5. Review the metadata by clicking the Metadata link.
   6. Notice how the data is available both as a direct download and as an API map service. The map service will load very quickly in ArcGIS. However, it only provides a picture of the data, so we can't input it into our GIS analysis. 
   7. Click "Download." Note the large download size (approximately 167 MB) and allow sufficient time for download.
      *If you are using Chrome, you may need to switch to Microsoft Edge and proceed by right-clicking the Download button and click on "Open link in new tab" in order to download the data file. 
   8. It is helpful to come up with meaningful, standardized names when using datasets from multiple sources since different groups will usually follow different naming conventions. Rename the zip file from “palulc_05_utm18_nad83.zip” to “LU_2005.zip” so it matches the other dataset in our lesson (LU_1978). 
   9. Save the zip file in your " L5Data” folder and extract it. 
      

      When working with raster data that you have downloaded, you need to be careful when placing it on your computer. Many raster datasets have an associated Info folder that contains critical reference information. The files contained within this folder are numerically named based on the particular order in which they were originally created. As a result, it is possible that different raster datasets have identically named reference files within this folder.

      It is important to note that although these files may have the same name, they do not contain the same information. Therefore, it is possible to corrupt your data if you overwrite one set of a raster dataset’s files with another’s. You can avoid this potential problem by creating new folders for each dataset and extracting each zip file within its own folder.

2. Organize Your Map and Familiarize Yourself with the Study Area and Data

   1. Create a New Map and save your project to the L5Data folder without creating a new folder for the project.
   2. Add the "Study_Area" and "Counties" shapefiles to your map. Change the symbology for the features in each layer to hollow outlines.
   3. Add the "OpenStreetMap" ArcGIS Basemap to your map.
   4. Use the zoom and pan tools to explore the surrounding area.
      

      What are the largest towns within the study area? Where is the study site in relation to the overall area of Pennsylvania?
      
      
       

   5. Confirm that the coordinate system of the map is “NAD_1983_Albers.”
   6. Save your project.
   7. Open a Catalog pane (Go to View tab, from the Window group > Catalog Pane) and explore the contents of the “LU_1978” and “LU_2005” folders. Notice that there are actually several different raster files in the 2005 folder. One is in a TIFF format (palulc_05) and the remaining two are in GRID format. We will use the dataset found in the "AlbersPA83" folder since it is already in the format and projection we need for this analysis.
      
   8. Add the "LU_1978" and "palulc_05" rasters to your map. If prompted with "Would you like to create pyramids?" select "No."
   9. Familiarize yourself with the contents of each data set to see similarities and differences between them. In particular, pay attention to the codes listed in the "VALUE" field of each attribute table. These are coded values representing land cover types, similar to the VEG_IDs from Lessons 3 and 4.
      

      Raster attribute tables are different from vector attribute tables. Unlike with vector files, each unique value is only listed once.

      
      

      Do all of the land cover raster datasets have the same number of coded values? How many unique codes does each raster dataset contain? Are any of the codes the same? Do they have the same extent and cell size? Do all of the datasets have the same spatial reference information?
       

Part II: Customize the Land Cover Data and Perform the Analysis 

We want to figure out how land use has changed between 1978 and 2005 for several counties in southeastern Pennsylvania. We are mainly interested in the urbanization of agricultural and forested areas. You may have noticed that the land cover categories and coded values are different for the 1978 and 2005 datasets. Since we are interested in comparing land use change, we will need to standardize these categories before we can compare them. We also want to remove extraneous information from our datasets to make them easier to work with. We will use the Reclassification Tool in Spatial Analyst to perform both of these tasks simultaneously.

We will reclassify both of the input raster data layers using the standardized codes below. Codes 1, 2, and 3 collapse the existing detailed categories into broader categories. The "NODATA" (ALL CAPS) category allows us to ignore all of the land cover categories that we are not using in our analysis.

The tables below show the original land cover codes from the 1978 and 2005 land cover grids, associated descriptions, and the new codes we will use to reclassify the data.

After all of the time periods share common land cover codes, we can calculate how much change has occurred in each category over time using the workflow below:

Click for a text alternative to the image above.

1. Spatial Analyst > Reclassify: Standardize the two datasets in terms of land use codes, cell size and extent
2. Field Calculator: Assign unique IDs to each record based on the year and land use type. These IDs will be used in the next step to generate column names.
3. Spatial Analyst > Tabulate Area: Calculate the total area of each land use type by county
4. Join: Join the output tables to the counties. This creates one master table with information from both study years.
5. Field Calculator: Calculate the total area of each county. Calculate the percentage change over time by county and land use category.

Credit: © Penn State University, is licensed under CC BY-NC-SA 4.0

1. Specify Geoprocessing Environment Settings

   

   It is important to remember to double-check the environment settings within the Spatial Analyst tool pane, as ArcGIS sometimes ignores the global environment settings. A general rule of thumb is to always be certain of the environment settings used in your analysis, as they are critical to your results.
   
    

   1. Go to the Analysis tab, Geoprocessing group, Environments, verify that your workspace (Lesson 5 folder) and output coordinates (same as Study_Area) have been correctly set.
   2. We can remove portions of rasters by using the extent and mask settings. We’ll take advantage of this functionality to clip the two rasters to our study area.
   3. Under "Processing Extent", choose "Same as Layer Study_Area" as the extent.
   4. Under "Raster Analysis", choose the "Study_Area" as the mask.
   5. You typically want to use the same cell size as your coarsest dataset. Check the cell size of the 1978 and 2005 rasters (Properties > Source > Cell Size). Which one is the largest? Notice that both of the datasets have odd cell sizes with many decimal places. This is likely related to projection changes at some point during the preprocessing of the original data. We are going to pick "Maximum of Inputs".
   6. Finally, we want to ensure that we do not build pyramids for any layers. Pyramids will generalize data as you zoom out, which will reduce the visible accuracy of the displayed data. Scroll down to "Raster Storage", and uncheck Build.
   7. Click OK to save these settings. Save your project.

2. Reclassify the 1978 Land Use Data

   
   1. Within the Analysis tab, Geoprocessing group go to Tools > Toolboxes > Spatial Analyst Tools > Reclass > Reclassify.
   2. Verify the Reclassify tool Environments settings (i.e., Output Coordinates - "Study_Area", Processing Extent - "Same as Layer Study_Area", Raster Analysis - Cell Size "Maximum of Inputs", Mask "Study_Area")
   3. Within the Parameters, Select "lu_1978" as the "Input raster" and "VALUE" as the "Reclass field."
   4. Click "Unique" to populate the "Values" column with the unique values in the dataset.
   5. Using the reclassification values given in Table 2, enter the appropriate values into the "New" column. Pay strict attention to the values you are entering to ensure proper reclassification.
   6. Name the new grid "RC_lu_1978.tif" and save it in your L5 folder.
      Note: In ArcGIS, the default Output Raster format is a TIFF (.tif).
   7. Check the "Change missing values to NoData" box.
   8. Click Run to perform the reclassification. Be patient as this may take a couple of moments depending on your computer’s configuration.
   9. If you’d like to review the progress, environment settings, and inputs, go to the Analysis tab, Geoprocessing group >History.
   10.  "RC_lu_1978.tif" will be added to your map. Set the symbology so 1= red, 2= orange, and 3=green, and NoData = grey (Mask tab).
   11. Compare the output to the original raster. Right-click on the “lu_1978” layer in the Contents pane > Zoom to layer.
       

       Notice how the extent setting we used clipped the raster to a much smaller area, and the mask setting we used assigned values of NoData to all of the areas that are both outside our study area boundary and within the extent.

       Also, notice the grey areas within our study area. These are places that we reclassified the original land cover to "NoData." Keep in mind that you could also do the opposite of what we did – you can reclassify cells with starting values of "NoData" to other values.

       

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

       The cell counts in your RC_lu_1978.tif should match the examples below. If your data does not match this, go back and redo the previous step. You can double-check settings and rerun the tool in the Results window.

       You’ll need right-click the RC_lu_1978.tif in Contents pane > Attribute table to see the Count attribute.

       
   12. Change the color of NoData back to “no color.”
   13. Since we no longer need the original land use layer, remove the lu_1978 grid from your map and Save the project.

3. Reclassify the 2005 Land Use Data

   1. Use the process from Step 2 and the values in Table 3 to reclassify "palulc_05” into a simplified land cover grid.
   2. Name the new grid "RC_LU_2005.tif"
   3. Add "LU_2005_RC" to your map and set the symbology so 1= red, 2= orange, and 3=green, and NoData = grey.
   4. Compare the output to the original raster. Right-click on the “lu_2005” layer in the Contents pane > Zoom to layer.
      

      How did the extent, mask, and cell size settings affect the output raster? You can view the cell size settings by right-clicking on the output raster > Properties > Source > Cell Size.
      
       

      

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

      Your LU_2005_RC grid should match the example below. If your data does not match this, go back and redo the previous step.

      
   5. Change the color of NoData back to “no color.”
   6. Since we no longer need the original land cover layer, remove the "palulc_05" grid from your map and Save the project.
      

      Since you know the cell size and number of cells with each unique value, you can easily calculate the total area within each land cover category for the entire study area. Note that you need to use the area of the cell, not the length, when making these calculations.

4. Add Unique Identifier

   In the next step, we will use the "Tabulate Area" tool to create a table with the areas of each land cover type within each county. We will repeat this for both time periods. The "Tabulate Area" tool will automatically generate column names based on the values in the input table. Since we will have two datasets with the same land cover codes, we need to be able to keep track of each year’s corresponding table. To do this, we will add new fields to each reclassified raster attribute table and populate them with a combination of the study year and the land cover code.

   1. Right now, the land covers are represented by arbitrary codes of 1, 2, and 3. We are going to assign more meaningful names (three letter abbreviations) so we don’t confuse the numeric codes later on. Open the RC_lu_1978.tif attribute table and add a new text field called “lu” with a length of 3.
   2. Select the first row (VALUE = 1) and use the calculate field to assign a value of “Dev.”
      
   3. Repeat for the remaining rows as shown below and then Save your edits.
      
   4. Add a new text field named "ID" with a length of 8 (4 characters for the year, one character for a "_", and three characters for the land use abbreviations).
   5. Set the values of the ID field to be equal to "1978_"!LU!. This will create a unique ID for each land use code and year. For example, the first row has a value of Dev, so the ID field would be set to "1978_Dev".
      
   6. View the results to make sure your calculation worked as planned. Close the attribute table.
   7. Repeat for the 2005 data. Make sure you use the correct year in your calculations.
   8. Clear the selected features and save your map. (If you skip this step, future operations will only be run on the fields you have selected).
      

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

      Your reclassified attribute tables should have their ID values populated as shown below. If your data does not match this, go back and redo the previous step.

      

      Click for a text alternative to the image above.

      Accessible Version of Data Above, 1978

      OID	Value	Count	LU	ID
      0	1	3762517	DEV	1978_Dev
      1	2	16778194	Agr	1978_Agr
      2	3	11264313	For	1978_For

      

      Click for a text alternative to the image above.

      Accessible Version of Data Above, 2005

      OID	Value	Count	LU	ID
      0	1	6730640	Dev	2005_Dev
      1	2	10679480	Agr	2005_Agr
      2	3	14529196	For	2005_For

5. Tabulating Areas of the Land Use Grids

   Now that we have reclassified the land cover data with standardized categories and created unique IDs, we can begin our land use change analysis. We need to calculate the area for each of the three land cover categories within each county for each time period. To do this, we will use the "Tabulate Area" tool. This tool calculates cross-tabulated areas between two datasets. This tool summarizes one dataset within regions specified by a second data set.

   1. Within the Analysis tab, Geoprocessing group go to Tools > Toolboxes > Spatial Analyst Tools > Zonal > Tabulate Area.
   2. Select "Counties" as the "Input raster or feature zone data" layer and "FIPS_CODE" as the "Zone field." The FIPS_CODE is a national naming convention system (similar to zip codes), that assigns a unique code to each county.
   3. Select RC_lu_1978.tif as the "Input raster or feature class data" layer and "ID" as the "Class field."
   4. Name the Output table "TA_1978.dbf" and save it in the L5 folder. Be sure to include the .dbf extension at the end of your file name to create a dBase table. Failure to add this file extension will result in an INFO table, which has different functionality than a DBF file. You will encounter trouble later in the lesson if you skip this small step.
   5. Make sure to read the embedded help topics about what each parameter controls.
   6. Leave the default processing cell size and click Run to tabulate the areas.
      
      

      Open the "TA_1978.dbf" table in your map. Notice the names of the columns. What are the units of the tabulated areas?
      
      
       

   7. Repeat this process for the 2005 reclassified raster using "ID" for the class field. Name the output table "TA_2005.dbf.”
      

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

      Your tabulated area tables should match the examples below. Both of the tables should have 19 records and 5 columns. If your data does not match this, go back and redo the previous step.

      

      Click for a text alternative to the image above.

      Sample Data 1978

      OID	FIPS_CODE	A_1978_Dev	A_1978_AGR	A_1978_FOR
      0	025	60044775.0275	140076533.603	764621228.308
      1	029	283115147.234	1221605255.57	451162509.312
      2	041	108895573.345	864337176.226	448917213.30
      3	043	142616070.306	607690006.007	604278011.426
      4	071	187182416.169	1895813331.92	374069736.292

      

      Click for a text alternative to the image above.

      Sample Data 2005

      OID	FIPS_CODE	A_2005_Dev	A_2005_AGR	A_2005_FOR
      0	025	85861467.5195	101130340.659	771302030.31
      1	029	557661328.688	673340415.676	659276515.52
      2	041	239732484.125	630922677.737	531524170.633
      3	043	236418388.277	400440924.138	703767243.941
      4	071	433833969.022	1367605682.98	608866798.469

6. Create a Master Table of the Two Tabulate Area Tables


   We will use the Join function to create a "master table” that contains the information from both of the Tabulate Area tables and the attributes of the counties. Since a joined table contains only virtually referenced information, we will export this dataset, thus permanently saving the joins.

   1. Right-click on the "Counties" shapefile and choose Joins and Relates > Add Join. Use the settings below and click "Validate" and then "OK". There may be a warning about the .dbf file not being indexed, it is OK to proceed.
      
   2. Open the "Counties" attribute table to view the join. Notice how the FIPS_CODE’s match up with County Names.
   3. Right-click on the "Counties" shapefile again and create another join between the TA_2005 table based on the "FIPS_CODE." Open the "Counties" attribute table to view the second join. Your "Counties" attribute table should now have fourteen columns.
   4. You may notice that some of the field names are redundant. We will remove these by using a trick before exporting our data to make the joins permanent.
   5. Go to the Table > View tab and select Fields by unchecking the highlighted fields below and click Save.
      
   6. Close the Counties attribute table if it is open and then open the attribute table again to see the results.
      

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

      Your attribute tables should match the examples below. If your data does not match this, go back and redo the previous step.

      

      Click for a text alternative to the image above.

      Sample Data, Counties

      FID	county_Nam	FIPS_code	A_1978_Dev	A_1978_agr	A_1978_for	A_2005_Dev	A_2005_agr	A_2005_for
      0	Carbon	025	60044775.027	140076533.603	764621228.308	85861467.5195	101130340.659	771302030.31
      1	Chester	029	283115147.23	1221605255.57	451162509.312	557661328.68	673340415.676	659276515.52
      2	Cumberland	041	108895573.349	864337176.226	448917213.304	239732484.125	630927677.737	531524170.633
      3	Dauphin	043	142616070.306	607690006.007	604278011.426	236418388.277	400440924.138	703767243.941
      4	Lancaster	071	187182416.169	1895813331.92	374069736.292	433833969.022	1367605682.98	608866798.469
      5	York	133	144054770.453	1585057521.64	609945566.225	451855625.63	1025137908.13	837911659.598
      6	Philadelphia	101	322253436.539	9576897.87968	4153583.7766	275763193.022	18073754.4639	31790615.6787
      7	Lebanon	075	70680207.6218	582750540.97	277862437.60	134141054.505	423053914.346	364983291.654

   7. Right-click on the "Counties" shapefile and choose Data > Export Features. Be sure to export all records and name it "LU_Change" in your L5 folder. Select "Yes" to add the shapefile to your map. Review the results.
   8. Right-click on the "Counties" shapefile and choose Joins and Relates > Remove Joins > Remove All Joins.
   9. Save your project.

7. Calculate the Area of Each County

   1. To identify the percent change over time for the three land-use layers, we first need to calculate the area of each county. Open the "LU_Change" attribute table and add a new float field called "TotAreaSQM" and Save. We need to use a float type since our numbers exceed the limits for short and long integers.
   2. Close the "LU_Change" attribute table.
   3. Reopen the "LU_Change" attribute table and populate the "TotAreaSqm" field using the "Calculate Geometry" tool. Make sure you use units of square meters.
      

      Sometimes your calculated values will have too many digits to be stored in a long integer field. In these situations, you can use a data type of "float" instead.

8. Calculate the Percent Change Over Time By County and Time Period


   As we saw in Lesson 2, it is much easier to compare numbers using percent areas vs. calculated areas. In this step, we are going to calculate the percent change within each land use type between 1978 and 2005.

   1. Before we can complete the calculations, we need to add new fields to hold the results. Add three new short integer fields using the names below.
      • PctChg_dev
      • PctChg_agr
      • PctChg_for
   2. We are going to use a semi-complicated equation to avoid the extra steps of calculating the percent area of each category, in addition to calculating the percent change over time. The basic equation we will use is:

      ([ tot land use in later time] - [ tot land use in earlier time]) / [TotAreaSqm]) * 100

      Note: Although you can represent a percentage as a fraction, multiplying that fraction by 100 will give you a range of 0 to 100%.
   3. Calculate the percent change for each of the three new fields using the equation above. For example, to calculate the field "PctChg_dev," the equation would be:
      
      

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

      Your calculated values should match the example below. If your data does not match this, go back and redo the previous step. I have only included the values for Adams County. You may need to sort your results to find this county.

      

9. Visualize Your Results Using Maps

   Create a map layout with the 4 map frames below. (Note: You will not turn in these maps. However, you will need to consult them to complete the Lesson 5 Quiz).

   • One data frame showing the agricultural land cover change between 1978 - 2005.
   • One data frame showing developed land cover change between 1978 - 2005.
   • One data frame showing forest land cover change between 1978 - 2005.
   • One data frame with a locator map.
   • Label each county with the % land cover change.
   • Select a consistent color scheme that allows you to compare the three maps (e.g., red = increase, green = decrease, gray = no change)

In Lesson 5, we used the Reclassify Tool to collapse complex categories into simpler versions. We also used it to eliminate portions of our starting data that we did not need for our analysis using the "NoData" code. Can you think of any other ways you could use this tool?

That’s it for the required portion of the Lesson 5 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.

Step-by-Step Activity: Overview

The Step-by-Step Activity for Lesson 6 is divided into two parts. In Part I, we will create a point shapefile from our starting data tables. We will then use the field calculator to calculate the carbon sequestration for each tree and create totals by plot. In Part II, we will use the Spatial Analyst extension tools to interpolate the plot data to a raster grid covering the entire study area. We will use the results to calculate the total carbon for the study forest. During interpolation, we will experiment with several different toolbar settings to see how they affect the results.

Lesson 6 Step-by-Step Activity Download

Note: You should not complete this step until you have read through all of the pages in Lesson 6. See the Lesson 6 Checklist for further information.

Part I: Create Shapefile from Field Data Tables

In Part I, we will create the shapefile we will use to interpolate our data (a point shapefile of plots with the total carbon as an attribute). To create this, we start with the two CSV files "GPS.csv" and "Tree _Measurements.csv".

1. Familiarize Yourself with the Study Area

   1. Create a New Map and save your project to the L6 folder without creating a new folder for the project.
   2. Add the parcel boundary and forest boundary from your L6Data folder to your Map.
   3. Change the parcel and forest boundaries symbols to hollow outlines.
   4. Go back to the Map tab and add the "OpenStreetMap" and “Imagery Hybrid” Basemaps to your Map.
   5. Use the Explore tool to pan and zoom within the surrounding area. Notice how the imagery shows that only the southern portion of the parcel is forested.
      

      How far is the study forest from the city of Ann Arbor, MI or State College, PA? What is the surrounding land used for (commercial, agriculture, residential, etc.)?
      
      
       

   6. Confirm the coordinate system of the Map is set to “NAD 1983 UTM Zone 16N.” If yours does not match, right-click on the Lesson 6 Map in the Contents pane > Properties > Coordinate System > Projected Coordinate Systems Folder > UTM > NAD 83 > NAD 1983 UTM Zone 16N. Save your project to lock in the settings.

2. Create Plot Shapefile from GPS Data Table

   1. Add the "GPS.csv" file from your L6Data folder to your map and explore the attributes. 
   2. Open the table and explore the attributes. What spatial reference do you think the x and y coordinates refer to?
   3. Right-click on the GPS table in the Contents pane > XY Table To Point
      1. X Field: LONGITUDE
      2. Y Field: LATITUDE
      3. Z Field: <None> (since we are not interested in height)
      4. Coordinate System: GCS_WGS_1984
         
   4. Right-click on the "GPS_XYTabletoPoint" layer in the Contents pane > Data > Export Features and export to a new shapefile in your L6 folder. Name it "Plots." Make sure you establish the output coordinate system under Environments as NAD_1983_UTM_Zone_16N.
   5. The "Plots" shapefile will be added to the Map, remove the GPS_XYTabletoPoint layer, and the GPS table from your Contents pane and Save the project. 

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

      Check the Properties > Source Tab > Spatial Reference to make sure the Plot shapefile was projected correctly to NAD 183 UTM Zone 16N. If your projection doesn’t match, make sure you remove the base maps, and choose the coordinate system of the Map.

      

      

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

      Check the location of your plots by comparing your plot shapefile to the map below. Note: Your map will not look exactly like this by default. I changed the symbology of the points, added labels of Plot ID's, and added the Imagery layer in the background to make it easier to compare your data to the example. If you add the imagery base map again, make sure you remove it from your map and Save before moving on to the next step.

      

3. Calculate the Carbon Sequestered by Each Tree

   1. Add the tree measurements CSV file from your L6Data folder to your map and explore the attributes. What does DBH mean?
   2. We need to add several new fields to the table to calculate the carbon values for each tree. ArcGIS will not allow you to add new fields to a CSV file. Export it to a .dbf file of the same name using the export option listed in the dropdown menu in the upper left corner of the attribute table. Make sure to select the file type as dBASE table so it has the “.dbf” extension. Add the resultant file to your map.
      
   3. Remove the "Tree_Measurements" CSV file from your Map and Save the project.
      

      We are going to use a somewhat general set of equations to estimate the carbon stored in each tree. For this lesson, we do not need a high level of accuracy. The important part is to demonstrate the concept of how one can calculate carbon credits using GIS. You can read more about the method we will use at: How to calculate the amount of CO₂ sequestered in a tree per year.

      There are more sophisticated methods you can use that take into account the tree species, age, climate, and other factors. The paper, “Methods for Calculating Forest Ecosystem and Harvested Carbon with Standard Estimates for Forest Types of the United States” highlights an example of a more complex methodology. An example of a simpler method is highlighted in the “Landowner’s Guide to Determining Weight and Value of Standing Pine Trees”.

   4. Add 7 new double fields to the Tree_Measurements dbf table. Save  the changes after you have added the fields. Use the names below:
      1. DBH_in (this is to convert units from cm to in)
      2. Height_ft (this is to convert units from m to ft)
      3. Wa_lbs (above ground tree weight)
      4. Wt_lbs (total tree weight with roots)
      5. Wd_lbs (dry tree weight)
      6. Wc_lbs (weight of carbon)
      7. Ws_lbs (weight of carbon dioxide sequestered)
   5. Use the calculate field tool and the equations below to populate the new fields from step d.

      Variable	Description	Units	Equation
      D	Measured tree diameter (DBH)	Inches	See Tree Measurements Table (be careful with your units here).
      H	Measured tree height	Feet	See Tree Measurements Table (be careful with your units here).
      Wa	Total above-ground weight of the tree (w/o roots)	Pounds	Wa = 0.15D2 *H
      Wt	Total weight of the tree and roots	Pounds	Wt = 1.2 Wa
      Wd	Dry weight of the tree	Pounds	Wd = 0.725Wt
      Wc	Weight of carbon in the tree	Pounds	Wc = 0.5Wd
      Ws	Weight of carbon dioxide sequestered in the tree	Pounds	Ws=3.6663Wc

      Tips for Success:

      • Don’t forget to convert units when necessary. Check Online Conversion.com if you are unsure of the conversion equations. Round the conversion factors to the nearest 4 decimal places (e.g., 0.3937007874 = 0.3937). What is the conversion factor for meters to feet?
      • Make sure to use double as the data type.
      • Remember that you have the option to undo. 
      • To calculate D2 in the Python 3, multiply D * D or D**2 (e.g., 0.15 * !DBH_In!**2 * !Height_ft!)
      • The example below shows the equation you would type into the calculate field (Python 3) to convert the DBH values from centimeters to inches.
        
      

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

      Compare your data with the summary statistics below for the "Ws" variable.

      

      Click here for an accessible version of the data above

      Statistics Shown in the image above

      Mean	801.0505393089
      Median	337.511190219
      Std. Dev.	1,171.7755087661
      Count	278
      Min	0
      Max	6,748.03406916
      Sum	222,692.04992788
      Nulls	0
      Skewness	2.6994064237
      Kurtosis	10.3340354971

      If your data does not match this, go back and redo your calculations. Pay special attention to unit conversations (make sure to round to the nearest 4 decimal places), data types of the fields you used, and typos in equations.

4. Combine the Carbon Calculations with the Plot Shapefile

   
   Ultimately, we want to join the calculations from Step 3 to the plot shapefile we created in Step 2. However, we can’t do this directly, because there is more than one entry in the tree measurement table for each plot in the shapefile. We know this is true because there are 278 trees but only 18 plots. Before we can join the two files, we need to summarize the tree data to the plot level.
   1. Calculate the total carbon sequestered per plot. Open the "Tree_Measurement" dbf table. Right-click on the "PLOTID" field>Summarize. Use the settings shown in the following figure. Make sure the extension is a .dbf file.
      
       
   2. The "Carbon_per_plot" table will be added to your map. Notice there are only 18 records now instead of 278. The "FREQUENCY" is the number of trees within each plot. (Now is a good time to change the field alias to “Number _Trees” so you remember what this means later on. Right-click on the field > Field > alias.) The "Sum_Ws_lbs" is the total carbon sequestered for all trees within the plot.
   3. Now we know the total carbon sequestered for each plot. However, we still need to normalize this data before we can interpolate it since we’re estimating carbon values across the entire area where there aren’t any additional plots. Because the diameter of each plot is 10 meters, we know the area of each plot is 78.5 square meters. By dividing each carbon total by this area, we derive a “carbon per square meter” value, which can be interpolated across the entire study area.
   4. Add a new double field named "c_lbsqm" to the "Carbon_per_plot" table. Calculate the pounds of carbon sequestered per square meter for each plot.
      

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

      Compare your data with the summary statistics below for the "c_lbsqm" variable.

      

      Click here for an accessible version of the data above

      Statistics Shown in the image above

      Mean	157.6023000198
      Median	109.55277128
      Std. Dev.	162.8283983546
      Count	18
      Min	6.420046445
      Max	646.682030534
      Sum	2,836.8414003566
      Nulls	0
      Skewness	1.5234771786
      Kurtosis	5.4280093282

      If your data does not match this, go back and redo your calculations.

   5. Join the "Carbon_per_plot" table with the "Plots" shapefile based on the PLOTID field. Open the attribute table to make sure your join worked properly.
   6. Export features to a new shapefile in your L6 folder named "Plots_carbon.shp" Be sure the new shapefile is added to your Map.
   7. Remove the "Tree_Measurements" table, "Carbon_per_plot" table, and "Plots" shapefile.
   8. Save the project.

Part II: Interpolate Point Data to a Continuous Raster

In Part II, we will use the Spatial Analyst extension tools to interpolate the carbon sequestration data we calculated for each plot to the entire forest. We will run the same interpolation tool several times to see how altering the extent, mask, and cell size settings affect the results. We will start by accepting all default settings. Then we will change the settings one at a time to see how each one affects the results.

1. Look for Trends in the Data

   1. Remember from Lesson 3 that it is wise to be aware of trends and spatial patterns in your data before you start to modify it using automated tools.
   2. Open the "Plots_carbon" attribute table and explore the data. You may need to set the alias of the Cnt_PLOTID again.
      

      Do some plots have more trees than others? Is there a lot of variation in the total amount of carbon or carbon per square meter value? If so, why do you think this may occur? Hint: Look at an aerial image basemap.
      
       

   3. Change the symbology to Proportional Symbols. Use the "c_lbsqm" as the Field value. Accept the remaining defaults.
      

      Do you see any spatial patterns in the data? For example, do some areas of the forest have higher values than others? If so, why do you think this may occur?
      
       

2. Explore Spatial Analyst Toolbox.

   1. Go to the Analysis tab, in the Geoprocessing group select Tools.
   2. In the open Geoprocessing pane, select Toolboxes  and scroll to the Spatial Analyst Tools, and browse through the available tools. We will be using these tools for the remainder of this course.

3. Interpolate Data Using All Default Settings

   Remember from the Background Information section that the Spatial Analyst tools are governed by user-specified settings. Two of the most common errors when using Spatial Analyst tools are to either completely ignore these settings, or to set them improperly. Let’s try to interpolate our data using all of the defaults and see what our results look like.

   

   Make sure you double-check ALL environment settings before running ANY tools in Spatial Analyst! The program often resets your cell size, extent, and mask to program or data layer defaults.
    

   1. Within the Analysis tab, go to Tools > Toolboxes > Spatial Analyst Tools > Interpolation > IDW. Use the settings below to interpolate the carbon per square meter values. Save the output raster in your L6Data folder and name it "default." Click Run.
       
      
      

      Click the Show Help >> button to help define particular input parameters.

      You can review the specific input and environment settings you used in the Analysis tab, Geoprocessing group > History. This can be helpful if you are not sure if you made a mistake somewhere along the way during a complex workflow.
       

      
   2. Change the symbology of the default raster as shown below (5 classes, colors light to dark red).
      
      

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

      If your map does not match the example below, go back and redo the previous step.
       

      
   3. Compare the results with the "Plots_carbon" shapefile. Areas near plots with high carbon values should be darker than areas near plots with low carbon values. Confirming your output results assures you that you selected the correct input dataset and field to interpolate.
   4. Notice how the interpolated raster does not match the forest boundary, how the shape of the output grid is a perfect rectangle, how the extent of the raster matches that of the smallest rectangle that can contain all of the plot points, and that the cell size is 0.87999… meters.
   5. Why did this occur? Let’s take a look at some of the environment settings utilized by Spatial Analyst to find out. Go to Geoprocessing > Environments. As noted earlier in this course, environment settings are the system-wide default settings that are used by every tool. Spatial Analyst utilizes Workspace, Output Coordinates, Processing Extent, Raster Analysis, and Raster Storage settings.
   6. Read the embedded help articles for:
      1. Processing Extent > Extent
      2. Raster Analysis > Cell Size
      3. Raster Analysis > Mask
      4. Raster Storage > Pyramid (scroll down)
         
   7. Pyramids allow for the rapid display of large raster datasets at multiple scales. While this may prove useful for very large datasets, creating pyramids has two important drawbacks. First, because of its design, it will generalize data as the scale gets larger, which will reduce the visible accuracy of the displayed data. Second, because of this generalization, layouts created at large scales may not properly represent the actual data, and may appear coarser than it actually is.
   8. I recommend NOT creating pyramids unless working with a particular dataset significantly degrades the performance of ArcGIS. For this course, we will disable pyramid creation when we set the Spatial Analyst environment settings. To do this, go to Analysis tab, Geoprocessing group,  Environments > Raster Storage (scroll down), and uncheck Build. Click OK to save this setting.
      
      

      What is the default setting for analysis extent?

      What is the cell size of the  "default" raster we created? Why? 
      
       

      

      Raster Attribute Tables

      You may notice that the option to open the attribute table of the "default" raster is grayed out. ArcGIS Pro only builds raster attribute tables if certain conditions are met. One of the conditions is that the values in the raster have to be integers. Since the values in our raster have decimals, it is not possible to view the attribute table. 
       

4. Interpolate Data – Set Extent

   Now that we’ve explored the default settings, let’s see what happens if we alter just the extent settings. Unlike vector files, rasters will always have the shape of a perfect rectangle. The size and location of the rectangle is defined by its extent.

   1. Within the Analysis tab, Geoprocessing group go to Tools > Toolboxes > Spatial Analyst Tools > Interpolation > IDW.
   2. Name the output raster C:\GEOG487\L6Data\forest_extent.
   3. Notice the default cell size is currently listed as 0.879999994163401.
   4. There is an oddity within ArcGIS where it does not apply all of the global environment settings from the Analysis tab,  Geoprocessing group > Environments to some tools within Geoprocessing Toolbox. We’ll need to set the extent settings using the Environments option within the IDW tool itself. Click on Environments > Processing Extent, choose "Same as Layer Forest_Boundary" as the extent.
      
      
   5. Notice how the output cell size changed to “1.17340837377682.” Make sure to review the embedded help topic to understand why this happened.
   6. To keep the output rasters consistent throughout the lesson, we need to manually override the cell size setting in Parameters. Copy and paste 0.879999994163401 into the cell size input and click Run.
      

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

      If your map does not match the example below, go back and redo the previous step.
       

      

      
   7. Change the symbology of the forest_extent raster as we did in Step 3b, except use light to dark blue for the color ramp.
   8. Compare the "forest_extent" raster to the "default" raster. You may need to rearrange the files in the Contents pane. Notice how the extent of the "forest_extent" raster matches that of the smallest rectangle that can contain the forest boundary polygon.
   9. Repeat Step 4, except use the "Parcel_Boundary" file as the extent. Make sure you use the same cell size as the default and forest-extent rasters. Name the output raster "parcel_extent."
   10. Change the symbology just as we did previously, except use an orange color scale. Compare the output with the parcel boundary. Save your map.
       

       How do the extents of the "parcel_extent," "forest_extent" and "default" rasters compare?
       
       
        

5. Interpolate Data – Set Mask

   Now, let’s see what happens if we alter the mask and extent settings. Even though all rasters are defined as perfect rectangles, you can still represent your data as a sinuous shape. The computer creates this illusion by assigning cells outside the sinuous shape values of "NoData." There is not a direct equivalent to this concept in vector files.

   1. Within the Analysis tab, Geoprocessing group go to Tools > Toolboxes > Spatial Analyst Tools > Interpolation > IDW.
   2. Change the output name to “for_msk_ext.”
   3. Click on Environments > Processing Extent > choose "Same as layer Forest_Boundary" as the extent.
   4. Within Raster Analysis > select Forest_Boundary as the Mask. 
   5. Make sure the cell size is 0.879999994163401 under the Parameters. Click Run to run the tool.
   6. Change the symbology as before except use a purple color scale. Compare the output raster to the forest boundary. Your raster should now match the shape and size of the forest boundary polygon.
   7. What happened to the cells outside of the forest boundary? By default, ArcGIS displays raster values of NoData without a color. Change the color of NoData to black on the symbology tab and examine the results.
      
   8. 

      What would the output raster look like in the following scenario?

      • Mask: forest boundary; Extent: parcel boundary
      • Mask: parcel boundary; Extent: forest boundary
      • Mask: "Plots_carbon" shapefile; Extent: forest boundary

6. Interpolate Data – Set Cell Size

   In Step 3, we learned that the default cell size will depend on the input data. If you are using one or more rasters as inputs, the cell size will default to the coarsest raster resolution. If you are using a vector file, it will calculate the cell size based on the extent of the file to create 250 cells. The default for rasters seems appropriate since GIS best practices dictate that you should always go with the cell size of your coarsest input dataset. However, the default for vector files is quite arbitrary.

   How do we choose a more meaningful cell size for our analysis? One rule of thumb is that you don’t want to "create" higher resolution data than what exists in your measured values. We know that the tree data was collected by measuring trees that fell within 10 m diameter circular plots. A cell size of 1 cm would not be appropriate, because we do not know how the data varies at that scale. A cell size of 1,000 m would be too large, since it is larger than our study area. For this project, we will use a cell size of 1 m, since our carbon values are in pounds per square meter.

   1. Within the Analysis tab, Geoprocessing group go to Tools > Toolboxes > Spatial Analyst Tools > Interpolation > IDW.
   2. Change the output name to "carbon1m."
   3. Click on Environments to Set the "Forest Boundary" as the mask and extent.
   4. Change the output cell size to 1. Click Run.
   5. Change the symbology as before, except use a green color scale. Compare the output to the "for_msk_ext" raster. At first glance, the output raster may not look very much different than the "for_msk_ext" raster, since they have the same mask, extent, and very similar cell size settings.
   6. Right-click on the "carbon1m" raster in the Contents pane > Properties > Source. Browse through the available information. Notice the extent and cell size. Compare this information to the properties of the "for_msk_ext" raster as shown on the right.
      
      "carbon1m" properties
      
        "for_msk_ext" properties
      
7. Interpret Results – Calculate Carbon for Entire Study Area

   Now that we have an understanding of how the spatial analyst environment settings function, we can return to our original question. We want to figure out how much carbon the study forest sequesters. To accomplish this, we will use the "Zonal Statistics" tool in Spatial Analyst. This tool allows us to calculate statistics of the cell values of one raster (e.g., carbon1m) within zones specified by another file (e.g., forest boundary). We will use it to sum the carbon values in each cell to create a total for the entire forest.

   1. Within the Analysis tab, Geoprocessing group go to Tools > Toolboxes > Spatial Analyst Tools > Zonal > Zonal Statistics as Table. Use the settings below to find the total carbon for the forest. Name the output table "carbon1m_sum" in your L6 folder.
      
   2. After you click Run, the summary table will appear in the Contents pane. Right-click to Open the table. Notice the field "SUM." This value represents the total carbon in the forest. Close the table.
   3. Notice that this table can be joined to the "Forest_Boundary" layer since they share the field "Name". You may find it useful to join these tables together if you wish to visually map the zonal statistics.
      

      ArcGIS may not show all the digits in a table by default. If your numbers do not match the numbers in the quiz, expand the columns in your table to display all the digits.

   4. One credit is earned for each metric ton (mT) of carbon sequestered. How many carbon credits does the study forest qualify for? Note that 1 metric ton = 2,204.6 lbs.
      

      The monetary value of each carbon credit fluctuates based on the current market conditions. Check out the more information about the California’s cap and trade system at Center for Climate and Energy Solutions(C2ES).

      

      One of the main take away points from this lesson is that Spatial Analyst is a modeling tool. Models don’t give exact final answers; rather they give you estimates of reasonable answers based on a set of assumptions.

      Environments settings allow you to easily alter the underlying assumptions of your model (cell size, mask, extent) and then quickly recalculate your results.

      Selecting environment settings in Spatial Analyst tools can be confusing and seem somewhat arbitrary. If you don’t know which Environments setting you should use for a particular scenario, you can try experimenting with a variety of options. This type of sensitivity analysis will help you understand how changing model assumptions affect your final results.

   That’s it for the required portion of the Lesson 6 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.

   Try This!

   Try one or more of the activities listed below:

   1. In Lesson 6, we used the defaults for many of the input parameters of the Interpolation Tool such as "z value field," "power," and "search radius type." Alter some of these parameters to see how they affect your results.
   2. There are several other interpolation methods to choose from in addition to Inverse Distance Weighted, such as "Spline" and "Kriging." Explore some of the other options to see how they affect your results.
   3. Export the study boundary to a KML file using Toolboxes > Conversion Tools > KML > KML to Layer. Open the KML file in Google Earth, zoom to the study boundary, and explore the historical imagery for the study site.

   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.

Step-by-Step Activity: Overview

In Part I, we will review the historical data and organize the map for analysis. In Part II, we will use the roads dataset to create rasters of habitat quality and forest patches. In Part III, we will generate statistics about the size, shape, and habitat quality of each forest patch. We will also generate statistics of habitat quality by land management type (conservation vs. logging areas). In Part IV, we will share our analysis results using ArcGIS Online.

Lesson 7 Step-by-Step Activity Download

Note: You should not complete this step until you have read through all of the pages under the Lesson 7 Module. See the Lesson 7 Checklist for further information.

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

In Part I, we will review the data and organize the map for analysis.

1. Unzip the Data for Use in ArcGIS

   1. Unzip the Lesson 7 data in your L7 folder. Since all of the data is included in this zip file, you do not need to worry about how you unzip the data.
   2. Familiarize yourself with the contents of the data included in this zip file. Refer to the Lesson Data section for additional information.

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

   Since all of the datasets used in this lesson have the same projection, we do not have to be concerned with the order in which we load the data.

   1. Start ArcGIS and create a new blank map and save the project in your Lesson7 folder.
   2. Add the "Study_Boundary," "Management_Units," and "Roads07" feature classes from the "L7Data.gdb" geodatabase located in your L7Data folder.
   3. Change the symbology of the layers as follows: Study_Boundary – hollow red line; Management_Units – unique values by ‘Use’; Roads07 – black line.
   4. Rearrange the layers so the study area boundary is on top and the roads are on the bottom.
   5. Explore the attribute tables of the three feature classes.
   6. Update your Environments: workspaces should be the L7 folder, and the output coordinates, mask, and extent should be the same as layer "Study_Boundary," the cell size to 100 meters, and uncheck the “Build Pyramids” box.
   7. Add the "Imagery Hybrid" ArcGIS Basemap to your map and drag it below the boundaries. Notice the location of the study boundary in relation to the country of Cameroon and the Congo Basin.
   8. Save your project. You may want to turn off the basemap if you experience any slow-down during the lesson.
      

      How many of the management units are used for logging? What about conservation?

      Using the "Imagery Hybrid" layer, can you see the approximate extent of the rainforests located in the Congo Basin? What kind of details can you see in the forest if you zoom in very close?

Part II: Create Habitat Quality and Forest Patch Datasets

In Part II, we will use the roads dataset to create raster data layers of habitat quality and forest patches. In Part III, we will generate statistics about the size, shape, and habitat quality of each forest patch.

We will use the following coded values:

• 1- Low Quality Habitat (Road Clearings)
• 2- Medium Quality Habitat (Forest, Edge Habitat)
• 3- High Quality Habitat (Forest, Interior Habitat)
   
  

  Road Dataset Flow Chart

  Click here for an accessible text alternative to the image above

  Roads → Road Buffers → Edge/Interior Habitat → Spacial Statistics Roads → Forested Areas → Forest Patches → Spacial Statistics

1. Create Grid of Low-Quality Habitat (Road Clearings)

   1. Open the "Roads07" attribute table and add a new short integer field named "HabitatCode." Save your changes.
   2. Use the Calculate Field tool to assign all of the roads a HabitatCode of "1." Clear the selected records.
      

      When you convert a feature layer to a raster, you have to choose a field in the feature layer from which to base the grid cell values on. You often need to create a new dummy field and assign a value that is consistent for all of the records you want to convert (like we did above).

      It is also important to note that if there are any selected records in the vector layer, only those records will be converted to a raster layer. Therefore, be sure to clear any selected features before performing the conversion.

      

      The data type of the field you choose is very important. For example, if you choose a numerical field that contains decimal values, the resultant grid will not have an attribute table. However, if you choose an integer field, the resultant raster will have an attribute table. If you choose a text field, ArcGIS will automatically assign each unique text value an integer code in a new field named "VALUE."

      The new raster layer will be created based on all defined Spatial Analyst environment settings. Always check these settings before converting features to a raster to avoid potentially undesirable results.

   3. Convert the roads feature class to a raster named "RoadsGrid.tif" using the settings below: Analysis > Tools > Toolboxes > Conversion Tools > To Raster > Feature to Raster.
      
   4. Click Run. Compare the "RoadsGrid.tif" to the road centerlines. Make sure you zoom to several different scales. Open the "RoadsGrid.tif" attribute table to view the results.
      

      It is important to note that although the extent setting is utilized by Feature to Raster, the mask setting is ignored. Although you will not notice this with the "RoadsGrid.tif" layer, you will see the effects of this when you create a buffered grid later in this lesson.

      

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

      The "RoadsGrid.tif" raster should have the following information. If your data does not match this, go back and redo the previous step.

      

2. Create Edge Effects Grid

   Remember from the Background Information section that edge effects can occur up to 2 km from roads. We will consider all areas 2 km from roads as "edge habitat" and areas farther than 2 km from roads as "interior habitat." To do this, we need to create a buffer of the road centerlines.


   1. Create a 2 km buffer of the road centerlines. Go to the Analysis tab, within the Tools group and click Buffer . Using the settings specified in red boxes below (allow other settings to default). Save the file inside the Lesson 7 geodatabase.
 Click Run.
      
   2. Compare the buffer to the road centerlines. You may want to use the measuring tool to double check your buffer is the correct width.
   3. Add a new short integer field named "HabCode" to the "Roads07_Buffer" feature class and assign it a value of "2" using the field calculator. The value of "2" corresponds to medium quality habitat (forested areas within 2 km of a road).
   4. Convert the road buffer to a grid named "EdgeGrid.tif" based on the "HabCode" field. Be sure to pay attention to the cell size.
   5. Compare the "EdgeGrid.tif raster to the "Roads07" and "Roads07_Buffer" datasets. Notice how the conversion tool did not follow the mask setting, as the raster cells with values extrude beyond the study area boundary. It may be easier to see the effect if you assign values of NoData in the EdgeGrid raster a color as we did in previous lessons.
      
      

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

      The "EdgeGrid" raster should have the following information. If your data does not match this, go back and redo the previous step.

      

3. Create Interior Forests Grid

   1. Open the "Study_Boundary" attribute table and add a new short integer field named "Value." Save your changes.
   2. Use the Calculate Field tool to assign a Value of "1" to the study boundary.
   3. Convert the Study_Boundary feature class to a raster named "Study_Boundary.tif" using the Value field and an output cell size of 100: Analysis > Tools > Toolboxes > Conversion Tools > To Raster > Feature to Raster.
   4. Reclassify the "EdgeGrid.tif" and use the "Study_Boundary.tif" as the mask. See the other settings below. Name the output grid "InteriorGrid.tif" and click Run.
      
   5. Compare the "InteriorGrid.tif" raster layer to the "EdgeGrid.tif" and "RoadsGrid.tif" layers. Notice how we were able to "flip" the areas with NoData. It is easier to see the effect if you turn off all of the layers except the Roads, InteriorGrid, and Study Boundary. It’s important that you choose appropriate mask and extent settings when using this technique.
      

      Did the Reclassify Tool honor the mask and extent settings?

      Hint: Compare the InteriorGrid.tif and EdgeGrid.tif rasters along the study area boundary.
       

      

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

      The "InteriorGrid.tif" grid should have the following information. If your data does not match this, go back and redo the previous step.

      

4. Create Final Habitat Quality Grid

   In steps 1, 2, and 3, we created three individual grids, one for each level of habitat quality. To continue the analysis, we need a way to merge all of the data sets into one grid. The Mosaic to New Raster tool in Toolboxes will allow you to mosaic multiple raster data layers together by stacking them on top of one another. The values in the output raster will be determined based on the order the files are specified during the mosaic. Cells will first be assigned according to the cell values in the first input raster; all remaining null values will be filled in with the middle input raster, and so on. We want the roads to be on top of the stack, the edge habitat in the middle, and the forests on the bottom.

   1. Go to the Analysis tab, Geoprocessing group and select Tools > Toolboxes > Data Management Tools > Raster > Raster Dataset > Mosaic to New Raster and enter the settings as shown below. Name the new grid "HabMosaic" and save it to your L7 folder. When adding the input rasters, pay attention to the order in which you add them. Along with the output location and the raster dataset name, you will need to assign a cell size and number of bands. The number of bands refers to a color map. Since we are not dealing with multiple band data, enter "1" to identify the new raster dataset as a single band layer. As mentioned above, we want the raster to be created based on a hierarchy from first to last. Therefore, we need to set the Mosaic Operator to "FIRST" so that the analysis runs as intended. You can leave the Mosiac Colormap Mode setting to "FIRST" since we are dealing with single-band data.
      

      This tool does not honor the Output extent environment settings. If you want a specific extent for your output raster, consider using the Clip tool. You can either clip the input rasters prior to using this tool, or clip the output of this tool.

      
      

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

      The "HabMosaic" raster should have the following information. If your data does not match this, go back and redo the previous step.

      
      

      What value was assigned to areas with roads, since they have data in both the "RoadsGrid" and "EdgeGrid" rasters?

      Which habitat type (roads, edge, or interior) covers the majority of the study area?

      How can you calculate the area of each habitat type?

   2. Notice that some edges of the "HabMosaic" grid fall outside of the study boundary. As mentioned earlier, this is because the Mosaic to New Raster tool does not utilize the environments extent setting. Therefore, we need to "clip" the data to the extent of our study boundary. To do this, we will use the Raster Calculator. Open the Raster Calculator (Toolboxes > Spatial Analyst Tools > Map Algebra > Raster Calculator), click the "HabMosaic" grid to enter it into the expression window, set the output raster as "HabitatGrid" and click OK to run the expression.
      
   3. Compare the "HabitatGrid" to the "HabMosaic" grids to see how the Raster Calculator "clipped" the data. Hint: Zoom into the study area boundary,
      

      The Raster Calculator utilizes all raster environment settings, so it is highly useful when working with raster data. As displayed above, simply selecting a raster layer and running the Raster Calculator will generate a new raster layer based on the current environmental settings. Try changing these settings to see the differences when running the Raster Calculator on a particular raster layer.

      

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

      The "HabitatGrid" raster should have the following information. If your data does not match this, go back and redo the previous step.

      

5. Create Grid of Forested Areas

   We now have one grid with values showing the range of habitat quality within the study area. The next step is to create a grid of forested areas, which we need to create the forest fragments. We will use the "RoadsGrid.tif" raster we created in Part II Step 1 to create a new grid representing forested areas (cells that are NOT roads).

   1. Reclassify "RoadsGrid.tif" using the settings below:
      
   2. Compare the "ForestGrid.tif" raster to the "RoadsGrid.tif" raster.
      

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

      The "ForestGrid.tif" raster should have the following information. If your data does not match this, go back and redo the previous step. You may need to adjust for the Mask and Processing Extent here as well.

      

6. Create Grid of Individual Forest Patches


   1. Examine the "ForestGrid.tif" attribute table. Notice there is not a way to distinguish groups of contiguous cells from one another. We need to be able to do this to determine which cells belong to the same forest patch.
   2. To accomplish this, we will use the RegionGroup tool. RegionGroup is an operation that takes adjacent cells with the same value and assigns them a unique value. So, in essence, it creates a grid with groups of cells similar to polygons in a feature class layer. This is an important operation since it enables further analysis with expressions and operations that require grouped regions, such as calculating the area and width of forest patches.
   3. Go to Toolboxes > Spatial Analyst Tools > Generalization > Region Group, select "ForestGrid.tif" as the input raster, name the output raster "ForestPatches.tif", leave the number of neighbors to use as "FOUR", the zone grouping method as "WITHIN", leave the link and excluded value setting, and click Run.
      
   4. Compare the ForestPatches.tif attribute table to the ForestGrid.tif attribute table. Notice how the attribute table now has multiple rows, one for each forest patch. The "Rowid" and "VALUE" fields both contain unique ID numbers for each contiguous forest patch. The "COUNT" field shows the number of cells in each forest patch.
      

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

      The "ForestPatches.tif" grid should have the following information. If your data does not match this, go back and redo the previous step.

      

      Click here for an accessible version of the image above

      Data to Compare your results to

      OID	Value	Count	link
      0	1	64201	1
      1	2	58	1
      2	3	122867	1
      3	4	19	1
      4	5	30	1

   5. The "VALUE" field is very important since it uniquely identifies each forest patch. However, the default name assigned by the computer is not very meaningful. It would be very easy to forget what it means later on. It’s also easy to confuse the "VALUE" and "Rowid" field since they contain similar numbers.
   6. To prevent these issues, let’s create a more meaningful attribute to keep track of the forest patches. Add a new short integer field named "ForestID" to the "ForestPatches.tif" attribute table. Populate it with the numbers in the !VALUE! field.
   7. Change the symbology to "unique values" based on the "ForestID" field. Notice how groups of contiguous cells are now considered one unit. Also, notice how the default colors assigned by ArcGIS are not very meaningful. We will address this later in the lesson.
   8. The "COUNT" field is also very important since it tells us how many cells are within each forest patch. As we saw in Lesson 5, we can use the number of cells and the size of each cell to calculate area values.
   9. Add a new float field to the "ForestPatches.tif" attribute table named "AREA_SQM." Use the calculate field tool to populate the field. 
You can also delete the Link field.
      
      

      Why did we use the number "100" to calculate the area?
      

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

      The "ForestPatches.tif" grid should have the following information. If your data does not match this, go back and redo the previous step.

      

      Click here for an accessible version of the image above

      Data Values to Compare To

      oid	value	count	forestid	area_sq
      0	1	64201	1	642010000
      1	2	58	2	580000
      2	3	122867	3	1228670000
      3	4	19	4	190000
      4	5	30	5	300000
      5	6	1	6	10000
      6	7	13	7	130000
      7	8	1	8	10000
      8	9	318427	9	3184270000

      

      How many individual forest patches are there? Which forest patch is the largest? Which forest patch is the smallest? Why do you think there are so many patches with an area of exactly 10,000 sq m?

Part III: Calculate Spatial Statistics of Forest Patches

In Part III, we will use two Spatial Analyst tools to bring together the raster layers we created in Part I (habitat quality) and Part II (forest patches). Zonal Geometry calculates several geometry measures, such as area and thickness, for zones in a raster. We will use it to generate a table of statistics about the size and shape of each forest patch. We will also use the Zonal Histogram Tool to tabulate the number of cells of each habitat type within each forest patch and management unit.

1. Calculate the Geometry of Each Forest Patch

   1. Go to Toolboxes > Spatial Analyst Tools > Zonal > Zonal Geometry as Table. Use the settings shown below. Name the table "PatchGeometry.dbf" and save it in your Lesson7 folder. Make sure to include the .dbf file extension. 
      
      

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

      The "PatchGeometry" table should have the following information. If your data does not match this, go back and redo the previous step.

      

      Click here for an accessible alternative to the image above

      Accessible PatchGeometry Data Set

      OID	Value	Area	Perimeter	Thickness	Xcentroid	ycentroid	Majoraxis	minoraxis	orientation
      0	1	642010000	350600	6343.9	1688100	359206	24878.6	8214.21	81.9311
      1	2	580000	3800	212.1	1696040	379519	631.205	292.488	84.2196
      2	3	1228670000	907000	6250.3	1756350	335775	36173.2	10811.8	134.675
      3	4	190000	2400	150	1698610	378516	270.871	223.275	140.531
      4	5	300000	2800	170.7	1699130	378353	401.382	237.911	110.363
      5	6	10000	400	50	1699560	378016	56.419	56.419	90
      6	7	130000	1600	150	1700800	377131	219.193	188.785	166.224
      7	8	10000	400	50	1698360	377216	56.419	56.419	90

      

      Which field in the "PatchGeometry" table is the equivalent to the "ForestID" field? What are the units of the fields "AREA," "PERIMETER," and "THICKNESS"? What do the values in the fields "XCENTROID," "YCENTROID," "MAJORAXIS," "MINORAXIS", and "ORIENTATION" mean?
       

   2. Add a new short integer field named "ForestID" and populate it with the values in the "VALUE" field using the field calculator. This step will make it easier to compare the Patch Geometry table with other outputs later in the lesson.
   3. Add three new float fields named “TotAreaSQM,” “PerimeterM", and “ThicknessM.” Calculate them to equal the values in “AREA,” "PERIMETER,” and "THICKNESS,” respectively. This will help us remember the units of the calculations later on.

2. Calculate Habitat Statistics by Forest Patch

   The Zonal Histogram tool will create a summary table that contains one row for each unique value in the "Value raster" and one column for each unique value in the "Zone dataset." The tool will calculate the total number of cells for each combination of a unique row and column. The tool can also create a graph based on the output table, which we are going to skip.

   1. Open the Zonal Histogram tool (Toolboxes > Spatial Analyst Tools > Zonal > Zonal Histogram). Use the settings below and click "Run." Make sure to add the .dbf extension.
      
   2. Open the "Habitat_by_Patch" table. The "LABEL" field contains values equivalent to the "Rowid" field within "ForestPatches." The "VALUE_2" field contains the number of cells of edge habitat for each forest patch. The "VALUE_3" field contains the number of cells of interior habitat for each forest patch (Remember that we used codes of 1, 2, and 3 to represent the different habitat types throughout the lesson).
   3. These field names are not very intuitive, and we may forget what they mean later on. Let’s add a few new meaningful fields to address this potential problem.
   4. Add a new short integer field called "ForestID" to the "Habitat_by_Patch" table. Use the calculate field tool to populate it with the values in the “LABEL” field.
   5. Add two new float fields named "Edge_SQM" and "Int_SQM." Calculate the fields as shown below (# of cells * cell length * cell width):

      
      
   6. Remember from Lesson 4 that it is a lot easier to compare multiple area values if you use percent of the total area instead of actual area values. Add two new short integer fields named "PctTotEdge" and "PctTotInt." Calculate the fields as shown below. Notice the 100 in the equation is used to create a percent value and is not related to the 100 value we used in step e, which corresponds to the length and width of the raster cells.
      
      
      

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

      The "Habitat_by_Patch" table should have the following information. If your data does not match this, go back and redo the previous step.

      

      Click here for an accessible alternative to the image above

      Accessible Habitat_by_Patch Data Set

      oid	Label	Value_2	Value_3	FORESTID	EDge_sqm	int_sqM	PCTtotedge	pcttotint
      0	1	22207	41994	1	222070000	419940000	35	65
      1	2	58	0	2	580000	0	100	0
      2	3	74095	48772	3	740950000	487720000	60	40
      3	4	19	0	4	190000	0	100	0
      4	5	30	0	5	300000	0	100	0

3. Calculate Habitat Statistics by Management Unit

   1. Use the Zonal Histogram Tool to determine the amount of each habitat type by management unit as shown in the example below: Don’t forget the file extension.
      
       
      

      What do numbers in the "LABEL" field of the "Habitat_by_MU" mean? Which management unit "use" has the most roads?
      
      
      
       

   2. Add a new text field (length 25) named “Habitat.” Use the field calculator and the information below to update the new field.
      • 1 - Low-Quality Habitat (Road Clearings)
      • 2 - Medium Quality Habitat (Forest, Edge Habitat)
      • 3 - High-Quality Habitat (Forest, Interior Habitat)
   3. Add two new float fields named “LogSQM” and “ConsSQM.” Add two new short integer fields named “PctTotLog” and “PctTotCons.” Calculate them using the technique we used in Step 2 e and f.
      

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

      The "Habitat_by_MU" table should have the following values. If your data does not match this, go back and redo the previous step.

      

      Click here for an accessible alternative to the image above

      Accessible Habitat_by_MU dataset

      OID	Label	logging	coservation	habitat	logSqm	conssqm	pcttotlog	pcttotcons
      0	1	35322	1428	Low Quality Habitat	353220000	14280000	96	4
      1	2	635978	44954	Medium Quality Habitat	6359780000	449540000	93	7
      2	3	302425	167611	High Quality Habitat	3024250000	1676110000	64	36

   4. Join Forest Patches to Geometry Table

      1. Since we no longer need the forest patches to be in raster format, let’s convert them to a shapefile so they are easier to use.
      2. Convert the "ForestPatches.tif" grid to a polygon shapefile using the settings below: Toolboxes > Conversion Tools > From Raster > Raster to Polygon.
         
      3. Add a new short integer field named "FORESTID" and populate it with the values in the "GRIDCODE" field.
         

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

         The "forestpatchpoly" shapefile should have the following information. If your data does not match this, go back and redo the previous step. Note that this table has been sorted based on "gridcode".

         

         Click here for an accessible alternative to the image above

         Accessible ForestPatchPoly Data Set

         Fid	Shape*	Id	gridcode	ForestID
         36	Polygon	37	1	1
         0	Polygon	1	2	2
         61	Polygon	62	3	3
         1	Polygon	2	4	4
         2	Polygon	3	5	5

      4. Join the "PatchGeometry" and "Habitat_by_Patch" tables to the "forestpatchpoly" shapefile using the link fields shown below.
         
      5. Open the attribute table to make sure the joins worked properly. Notice how it is hard to view the attributes we are most interested in since there are so many fields.
      6. On the ribbon, go to the Table > View tab >Fields , and uncheck the "Visible" box on the top left side. Add the checkboxes back to the eight fields listed below and click "Save."
         • ForestID
         • TotAreaSQM
         • PerimeterM
         • ThicknessM
         • Edge_SQM
         • Int_SQM
         • PctTotEdge
         • PctTotInt
      7. Close and then re-open the ForestPatchPoly attribute table. Notice how it is much easier to interpret the results now.
      8. To make the joins and table design permanent, export the "forestpatchpoly" to a new shapefile in your Lesson 7 folder named “Final_Forest_Patches.shp.”
      9. Examine the attribute table.

   5. Calculate the Edge to Area Ratio of each Forest Patch

      1. Calculate the edge to area ratio for each patch. Add a new float field named "EdgetoArea." Calculate it as shown below. Note: we are going to multiply the result by "100" to make the values easier to compare.
         
         

         Why is there such a large range of values for the edge to area ratio results?

         How would the results of the analysis change if we used a larger or smaller cell size?

         

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

         The "Final_Forest_Patches" attribute table should have the following information. If your data does not match this, go back and redo the previous step.

         

         Click here for an accessible alternative to the image above

         Accessible Final_Forest_Patches Dataset

         FID	Shape*	Forest ID	Totareasqm	perimeterm	thichnessm	edge_sqm	int_sqm	pcttotedge	pcttotint	edgetoarea
         36	Polygon	1	642010000	350600	6343.9	222070000	419940000	35	65	0.05461
         0	Polygon	2	580000	3800	212.1	580000	0	100	0	0.655172
         61	Polygon	3	1228670000	90700	6250.3	740950000	487720000	60	40	0.07382
         1	Polygon	4	190000	2400	150	190000	0	100	0	1.26316
         2	Polygon	5	300000	2800	170.7	300000	0	100	0	0.933333
         3	Polygon	6	10000	400	50	10000	0	100	0	4
         5	Polygon	7	130000	1600	150	130000	0	100	0	1.23077

         

         Notice how the default outputs from many of the Spatial Analyst tools are not very easy to understand. It’s worth the time to create more intuitive fields, units, and names while you are doing the analysis. That way you can easily interpret your results later on and share them with others in a meaningful format.

Part IV: Share Your Results

In Part IV, we will finalize our map in ArcGIS, then you will be asked to share your results with the Geog487 AGO group as web maps. As a final step, you will combine the output from the Step-by-Step and Advanced Activity into a web application.

1. Prepare Your Map to Publish in ArcGIS Online

   1. When you publish your map to ArcGIS Online, it preserves many of the features such as the extent and visible datasets. Let’s begin by removing all of the data we do not want to include on our final map. Remove the base map, all of the data sets, and all of the tables (you may need to switch to the List by Source view in the Contents pane) from your map except the following: Final_Forest_Patches, Study_Boundary, Roads07, and Management_Units. Save your map.
   2. Remove the underscores from the file names in the Contents pane.
   3. Change the symbology of the Final Forest Patches to Quantities > Graduated Colors based on the PctTotEdge field. Select a color scheme and number of classes you think best represent the message you want to convey about the results. You may want to consult ColorBrewer2 for advice and tips.
   4. Update the labels in the Symbology or Contents pane so the numbers in the Final Forest Patches make sense to your viewers. (What are the units? What’s being shown?)
   5. Change the symbology of the management units to hollow outlines with a unique color for each “Use.”
   6. Review your map. Ask yourself the following questions: 1) What are the main messages I am trying to convey with my map? (Remember, you want to show the relationship between logging and forest health.) 2) Does my map design communicate these messages clearly? 3) Will someone unfamiliar with my analysis be able to use my map to make a decision? Make any changes you think are necessary and save your map.

2. Share Your Results with the Group using ArcGIS Online

   1. Share your 2007, 2001, and 2021 Forest Patches maps with our GEOG487, Environmental Applications of GIS Group, through the Penn State AGO Enterprise Organization. You can either build a web application or create a story map for the web application requirement listed as Step 2 below. 
      • Sharing data as AGO web feature layers from ArcGIS Pro. 
        • Go to Share tab. In the Share As group, click on the Web Layer drop-down, then select Publish Web Layer
        • The Share As Web Layer pane appears. 
        • Provide a Name for the web layer.
        • Complete the Summary and Tags fields.
        • For Layer Type, click Feature.
        • Select or create a folder location within your AGO My Content
        • Share with the Groups (pull down to find the Geog487 Group)
        • Click Analyze to review potential problems 
        • Click Publish when ready
   2. Step 1: Publish Three Maps in Penn State's ArcGIS Online for Organizations Account
      • Forest Patches 2007 (Final Results from Step-by-Step Activity)
      • Forest Patches 2001 (Final Results from Advanced Activity)
      • Forest Patches 2021 (Final Results from Advanced Activity)
   3. Step 2: Create a Web Application in ArcGIS Online that incorporates your Advanced Activity results.
   • I encourage you to select a template that allows the reader to easily compare at least two of the maps listed above (e.g., 2007 and 2001 or 2007 and 2021).

That’s it for the required portion of the Lesson 7 Step-by-Step Activity. Please consult the Lesson Checklist for instructions on what to do next.

Step-by-Step Activity: Overview

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.

Lesson 8 Step-by-Step Activity Download

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.

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

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

1. Unzip the Data for Use in ArcGIS

   1. 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.
   2. 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.

   1. Start ArcGIS, create a new map, and save it in your L8 folder.
   2. Add the LakeRaystown, geology, soil, elev, and streams_buffer datasets from your L8Data folder.
   3. When prompted, "Build pyramids and Calculate statistics for elev," allow the defaults, which are checks beside "Build" and "Calculate." Click OK.
   4. Examine the metadata and attribute tables of all of the starting datasets.
   5. Change the symbology of the "LakeRaystown" and "streams_buffer" layers to hollow outlines, the geology layer to unique values by "rock_type," and the soils layer to unique values by "texture."
   6. Zoom to the layer extent of the Lake Raystown Watershed and look for spatial patterns in the geology and soil datasets.
   7. 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 a buffer was used to create the streams data?

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

3. Set the Spatial Analyst Option Settings

   1. Go to the Analysis tab, Geoprocessing group > Environments
      1. Set your workspace and scratch space to your Lesson 8 folder.
      2. Set the output coordinates, mask, and extent to the same as "LakeRaystown.”
      3. Set the cell size to 30 meters.
      4. Choose to not build pyramids.
   2. Save your project 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

   1. 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.
   2. Go to the Analysis tab, Geoprocessing group > Tools > Toolboxes > Conversion Tools > To Raster > Feature to Raster.
      
      

      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.
       

      
   3. Compare the "soilgrid.tif" map and attribute table to that of the "soil" shapefile.
   4. Now that the soil data is in grid format, we can reclassify the grid to assign DRASTIC Ratings to each soil type.
   5. Use the values in the table below to reclassify "soilgrid.tif" based on the "TEXTURE" field. Name the output file "s.tif" (This is the letter used in the DRASTIC acronym to represent the Soil Media). Be sure to confirm the Reclassify > Environments > output coordinates, processing extent and mask are the same as "LakeRaystown.”

      Table 1: DRASTIC Ratings for Soil Textures (S)

      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.

      

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

   1. 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.
   2. Convert the Geology shapefile to a grid. Use "ROCK_TYPE" as the "Field." Name the grid "geologygrid.tif"
      

      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.

      

      Click here for an accessible text version of the image above

      Accessible geologygrid.tif dataset

      OID	Value	Count	Rock_type
      0	1	1480784	Interbedded Sedimentary
      1	2	643096	Sandstone
      2	3	388372	Shale
      3	4	256791	Carbonate

   3. 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 .tif using the reclassify tool and "ROCK_TYPE" field. Name the new grids "a.tif," "i.tif," and "c.tif". Remember to confirm the Reclassify > Environments >output coordinates, processing extent and mask are the same as "LakeRaystown.”

      Table 2: DRASTIC Ratings for Aquifer Media (a)

      Rock Type	DRASTIC Rating
      Interbedded Sedimentary	6
      Sandstone	6
      Shale	2
      Carbonate	10

      Table 3: DRASTIC Ratings for Vadose Zone (i)

      Rock Type	DRASTIC Rating
      Interbedded Sedimentary	6
      Sandstone	6
      Shale	3
      Carbonate	10

      Table 4: DRASTIC Ratings for Hydraulic Conductivity (c)

      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.

      

      

      

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.

   1. Go to the Analysis tab, Geoprocessing group > Tools > Toolboxes > Spatial Analyst Tools > Surface > Slope.
   2. 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.tif". The resulting layer depicts steep slopes with high values and gentle slopes with low values. Remember to confirm that the Slope > Environments > output coordinates, processing extent, and mask are the same as "LakeRaystown.”
       
      
      

      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

      
   3. Examine the "pctslope.tif" 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.
   4. We want to reclassify the "pctslope.tif" grid using the DRASTIC Ratings in Table 6.

      Table 6: Ranges and Ratings for Topography

      Topography Range	DRASTIC Rating
      0-2	10
      2-6	9
      6-12	5
      12-18	3
      >18	1

   5. Open the Reclassify tool and select the "pctslope.tif" grid. Notice the default number of classes and break values listed in the "Start" and "End" columns. 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).
   6. The quickest way to change these settings is to click on the "Classify" button. Change the number of classes to "5." Manually type in the break values. "
       
   7. Modify the "New Values" in the reclassify window based on the values in Table 6. Name the resulting grid "t.tif". Make sure you check the box "Change missing values to NoData" and confirm the Reclassify > Environments > output coordinates, processing extent and mask are the same as "LakeRaystown.”
      

      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.

4. Explore the DRASTIC Rating Output Grids

   1. 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.
   2. Change the symbology of the d, r, a, s, t, i, and c grid layers so that low vulnerability ratings (1-3) are green, medium ratings (4-7) are yellow, and high ratings (8-10) are red. You can classify similar values together using "Unique Values" on the Symbology pane > Press the Ctrl key and click to select rows under Value heading to group > Right-click select Group values.
   3. 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?

   4. 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 Index

   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.

   1. Open the Raster Calculator Analysis tab, Geoprocessing group > Tools > Toolset > 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.tif" * 3 + "s.tif" * 2 + "t.tif" * 1 + "i.tif" * 5 + "c.tif" * 3
      
   2. Change the color ramp so that high values are shades of red and low values are shades of green like the example below.
      
       
      

      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?

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.

1. Explore the DRASTIC Rating Output Grids

   1. 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).

   2. Reclassify the "di150" grid using the settings below. Name the output grid "OK_Drastic.tif" and confirm the tool Environments.
       
      
      

      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.

          
       

2. Create a Grid of Suitable Surface Water

   1. Convert the "steams_buffer" shapefile to a raster using the "Id" field. Name the output "streambuffgrd.tif" and save it in your L8 folder and confirm the tool Environments.
   2. Reclassify "streambuffgrd.tif" as shown below. Name the output "OK_Streams.tif" and confirm the tool Environments.
      
      

      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.

       
   3. Compare the "OK_Streams.tif" layer to the "steambuffgrd." Notice how we have essentially flipped the areas of NoData. It is important that you choose an appropriate mask and extent settings when using this technique.

3. Combine the Suitability Grids Using the Raster Calculator

   1. Use the raster calculator to multiply the "OK_Drastic.tif" and "OK_Streams.tif" 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."
      

      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.

      
   2. 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.
   3. Go to Toolboxes > Spatial Analyst Tools > Generalization > Region Group, select " OK2criteria.tif" 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.
      

      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.

      
           

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

   1. 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.

   2. 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?
      
      
      
       

   3. 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.
   4. Go to Toolboxes > Spatial Analyst Tools > Extraction > ExtractByAttributes. Select "OK_Regions.tif" 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 "greater than or equal to", and type 500000 at the end of the expression (0.5 sq km = 500,000 sq m). Confirm the tool Environments. Click Run to automatically input the expression into the "Where clause."
      
      

      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.

          
       
   5. Reclassify "OK_Area.tif". Change the number of classes to "1," since all of the start and end values match all of the site selection criteria. Name the grid "FinalSites.tif" and confirm the tool Environments. These are your potential sludge disposal sites.
      
          
      This 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.