ArcCollector Part 2 - Project

Introduction
The purpose of the project was to create a web map and series of maps to answer some type of research question.  The topic/focus of this project was measuring plant health at a local berry farm based on plant type, plant age, and plant height.  This type of project is a model of a type of work that can be done by setting up a database with ArcMap, using the ArcCollector app on a cell phone with GPS to collect data in the field, and making static maps and web maps as an end product to display the results. There is major emphasis on proper project design in this activity to demonstrate how important it is to set up a project that is easy for the collector to use.  If the project is set up thoughtfully and intuitively, the person collecting the data should have an easy time in the field collecting data and creating maps from the results.

The goal was to create a valuable end product that the farmer could use to keep track of which fields were planted well, as well as track plant height, which could be a factor in plant health.

Study Area
The study area is Little Berry Farm.  It is located roughly 4 miles south of the city of Eau Claire off Highway F.  Figure 1 shows the study area.

Figure 1: The Study Area Map showing Little Berry Farm and the surrounding areas.  

Methods
An ArcGIS Online tutorial ( http://doc.arcgis.com/en/collector/ ) was followed to learn how to set up the geodatabase necessary to create the maps.  This tutorial outlined the steps used in setting up the geodatabase and creating the web map.

First the database must be created using ArcCatalog.  It was created in the personal student folder for the class and then assigned appropriate domains, as shown in Figure 2.

Figure 2: The three domains were Plant Age, Plant Height, and Plant Type.  These domains were created so that when features are created they have these fields to fill in data for.  They specify characteristics about the feature and often have a list of available options to choose from for each category.  

Next a feature class is created with a name describing what will be included within it, as shown in Figure 3.  

A polygon feature was used because cell phone GPS is not currently accurate enough to measure specific plants.  Rather the plants were grouped into fields based on the type of berry and when they were planted.

Figure 3: The new feature class was created and named Plant Information.  

Next a coordinate system was chosen for the database, as shown in Figure 4.

Figure 4: The WGS 1984 Web Mercator (auxiliary sphere) coordinate system was chosen for the geodatabase because it is compatible with web maps.  

Next the corresponding fields were set up for the feature class based on the domains already created for the geodatabase.  Because data would be brought back into ArcMap later for creating the desired maps, the symbology and display details could be manipulated later.  The map was then published as a service through ArcGIS Online and shared with the class as well as the UWEC Geography department.  Capabilities and Feature Access were altered to allow the users collecting the data with the app to edit the features during and after they are recorded.

Next ArcGIS Online was used to add a basemap and the layer that was published and shared to the map.  One feature was created as a test to make sure that the correct fields showed up to be filled in.  That feature could later be deleted.  

The next step was to sign in on the app on the cell phone to download the map.  A workspace was then defined by zooming in to the farm, as shown in Figure 5.  

Figure 5: A workspace around Little Berry Farm was defined by zooming into it.  Some room was left around the farm in case the accuracy of the GPS left the boundaries of the actual fields.  

 Now that the geodatabase, web map, and basemap within the cell phone ArcCollector app were all set up, the next step was to go collect the data.

Upon arriving at the farm, it was clear that the basemap imagery was taken before the fields of the current berry farm had been planted, so the features on the maps do not exactly align with the previous farm fields.  At the Northeast corner of each field, a picture was taken looking at the row/field that was to be mapped, as shown in Figure 6.

Figure 6: The raspberry row is shown here as an example of every picture that was taken of each field mapped throughout the project.  The pictures to each corresponding field can be seen using the web map in the results section.  

Each picture was attached to each feature using the paperclip with a plus sign symbol shown near the bottom of Figure 7.  Figures 7 and 8 both show how the mapping looks during collection.  A point is recorded every 5 seconds, and a polygon is created from the string of points until the field is fully traced.

Figure 7: This image shows the points being recorded of the raspberry row.  

Figure 8: This image shows the polygon feature that resulted from walking around the raspberry row.  

The rest of the fields of interest were then mapped using the same procedure, filling in the information for each feature as shown in Figure 9.

Figure 9: The attributes are filled into this form on the ArcCollector app. 

Once the collecting was complete and wifi could be attained once again, the map was synced so that the information collected could be updated online, on the web map.  This is shown in Figure 10.

Figure 10: This cell phone screenshot shows the map being synchronized to the online map.  

Now that the data had been collected, the last step was to open the web map as well as in ArcMap Desktop so that static maps could be created to display each field that was mapped.

Results
The resulting web map can be found by following this link: http://arcg.is/9zK1L.  Within the web map there are pictures attached to view each field from when the data was collected.  It is also useful in that the user can manipulate the map to display different aspects of the data collected for the project. Figures 11, 12, and 13 show the maps corresponding to each data variable collected and mapped.

Figure 11: This is the map showing the fields based on type: Blueberry, Raspberry, and Strawberry.  Some fields are misshaped and overlapping due to the inconsistency of the cell phone GPS.  The blueberry fields and strawberry fields are similar in size because there are many more rows of them, while the raspberry section alone is skinny because it is only one row.  

Figure 12: This map displays the fields based on how many years before this they had been planted.  This map shows the years that each field was planted.  The older fields are darker while the newer fields are lighter in color as to represent younger vegetation.  This factor was chosen as a possible characteristic that could indicate plant health, size, and/or productivity.   

Figure 13: This map shows the average height of plants in each field/section.  The strawberry fields are all roughly 1 foot tall or less on average because they don't grow taller than that and the raspberry row was about 5 feet tall on average.  The height factor of the plants was mostly based on the blueberry fields because there are several of them and much more variance between them.  The middle section of the blueberries is a section of interest because it was planted originally 4 years ago, yet the same height as the section planted only 2 years ago.  This could be useful in indicating plant health or productivity, especially if one section were short in height and also not producing many blueberries during harvest season.  

Conclusion
This activity was a good demonstration of why it is important to design a project properly.  The domains, attributes, fields, etc. all have to be on point in order for things to go smoothly on the collector side of things.  If any of those are incorrect or missing it makes the job of the collector much more difficult.  This activity also taught a good lesson on what to do better in future projects that have a similar concept or end goal.  If this project were to be repeated it would be wise to speak more to the farm owner ahead of time to speculate on ideas for measurable factors that lead to productivity.  It would also be more conducive to the idea of increasing productivity for the farmer to measure/map these things during a harvest season or a time when crop yield could be measured.  This way the plants could be seen when they are most healthy and bearing fruit, rather than being prepared for winter.  This project type could be expanded in countless areas.  It could be beneficial to farmers specifically if they were interested in mapping these types of variables, or to any other type of sector that has characteristics to be measured, collected, and mapped.


References
-ArcGIS Online
-Dr. Joseph Hupy
-Gaye Brunkow (Farm Owner)