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.
Note: You should not complete this step until you have read through all of the pages in Lesson 3. See the Lesson 3 Checklist for further information.
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.
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.
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).
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.
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.
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.
Caution - watch out for similar attribute names like OID. This is not the same as Veg_ID.
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.
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.
If your data does not match this, go back and redo the previous step.
Make sure you have the correct answer before moving on to the next step.
If your data does not match this, go back and redo the previous step.
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 [7] 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.
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.
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.
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.
Links
[1] https://www.fws.gov/wetlandsmapservice/services/Wetlands/MapServer/WMSServer?
[2] http://www.fws.gov.wms
[3] https://www.fws.gov/program/national-wetlands-inventory/metadata
[4] https://www.glc.org/library/2008-great-lakes-coastal-wetland-monitoring-plan
[5] https://www.e-education.psu.edu/geog487/sites/www.e-education.psu.edu.geog487/files/activities/lesson03/glcwc_cwi_polygon.zip
[6] https://www.glc.org/wp-content/uploads/2016/10/CWC-GLWetlandsInventory-ClassificationScheme.pdf
[7] https://pro.arcgis.com/en/pro-app/help/data/geodatabases/overview/arcgis-field-data-types.htm
[8] https://www.google.com/earth/versions/#download-pro
[9] http://www.greatlakesphragmites.net/management/programs-and-projects/