Data Collection with ArcCollector

Introduction:

The purpose of this lab was to give the student experience using ArcGis online as well as creating domains related to a particular feature class. The feature class created was used to collect data using the ArcCollector app. Information about different types of trees as well as squirrel nests were collected during this survey.

Study Area:

This week, the study area was at a location other than campus. I was traveling for our Thanksgiving break, so I collected data near Plainview, MN. A family member has an old farm house, with a large lot that has many different tree species present on the property. Since I was not able to be on campus, this was the next closest place that had similar attributes.

Methods:

In order to successfully collect data for this lab, domains needed to be created within the Geodatabase used to hold all of the data. There are two different types of domains that could be used: range or coded. Range domains are used when working with numeric data. In order to set a range, a maximum and minimum value must be entered. Coded domains are able to be used with any type of attribute information. Coded values set what information can be entered for a specific attribute. This makes adding information much smoother while in the field. The domains that were set for this exercise were the size of the tree, type of tree, tree species, if leaves were present and if there were any squirrel nests in the tree.

Once all of the domains were set it was then time to create a new feature class. A point feature class was created for collecting data on different types of trees. The feature class would be used in the Collector app. In order to allow for the data to be shown correctly, the coordinate system of WGS 1984 Web Mercator (auxillary sphere) was set for the feature class' projection.

Once the feature class was created, it then needed to be published on ArcGIS online. If it was not published, it would not be able to be used on the Collector app. It was during this publishing step that I encountered a few problems. I was able to create the feature class and get it onto ArcGIS online, but it would not transfer onto my Collector app. After numerous troubleshooting ideas, I found out that I had not made my feature class and editable layer. Since I could not edit, I was not able to use the file and that is why it was not showing on the app. Once that problem was solved, everything worked how it was intended and I was able to see my map on the Collector app.

The next step was to collect data in the field. I had to walk to each individual tree and make a point and enter the specified attribute information. Since there were numerous trees, I did not have to travel very far to get the 20 points needed for collection. Total collection time took about 30 minutes. Once all of the data was collected, it then needed to be downloaded from ArcGIS online to be used in ArcGIS for desktop.

Discussion:

The data shows that the most distinct difference for the different attributes collected is related to the type of tree present. I broke this down into two different categories: deciduous and coniferous trees. The figure below (Figure 1) shows the results of where the different tree types were located.

As the figure above shows, there is no real pattern as to how the different tree types are dispersed about the property. The one thing that can be noted, from this sampling, there is a much higher percentage of deciduous trees on the property compared to coniferous trees present. Breaking this down even farther, we will be able to see the different types of trees species are present on the property. The figure below (Figure 2) shows the different type of tree species that are present on the property. 

This map shows where the different types of tree species are located across the property. Although it is dominated by deciduous trees, there are many different species present. 

After completing this lab I realized that I should have done a few things differently. One major flaw I found with my survey method is that I did not have enough domains set for the different types of trees I was going to encounter. There were many trees I was not able to survey simply because I did not have the domains set. 

Conclusions:

This exercise helped me to realize the importance of prepping your data before going out to the field. Setting different domains up correctly before going to the field saves lots of time when collecting data. It also makes you think about the data you will be collecting and what will be the most efficient method for field collection. Although the collection of the trees is the goal of this lab, I learned many lessons from it. Learning how to collect data in the field from many platforms, such as a smartphone, is a very valuable skill to learn and continue to develop. 

Conducting a Topographic Survey with a Dual-Frequency GPS

Introduction:

The last couple of weeks we have been working on two different projects, but they are closely related to each other. The first part was conducting a topographic survey with a dual-frequency GPS. The second part was was conducting a topographic survey using a total station. These two different methods can be used for surveying different types of terrain. The end goal of this lab was to be able to compare the results of both techniques in order to see if one was more or less accurate than the other.

Study Area:

The study area for both parts of this project was the campus mall at the University of Wisconsin-Eau Claire. The campus mall is located on lower campus. It is surrounded by many of the academic buildings and is bordered by the little niagara creek that runs through campus. The mall is relatively flat with a slight increase in elevation as you move north away from the creek towards Schofield Hall.

Methods:

Survey with a Dual-Frequency GPS

The first survey method that was conducted used the Topcon Hiper (Figure 1), Topcon TESLA (Figure 2) and a MiFi wireless router. In order to accurately collect our data points, the Hiper needed to be screwed to the top of the surveying rod and the TESLA unit was also attached to the surveying rod, about 1 meter off of the ground. The MiFi wireless router was connected to the Velcro strip on the surveying rod. The Mifi unit needed to be in close proximity to the the other equipment in order to allow for them to stay connected to one another.

Figure 1. This is the Topcon TESLA unit that was
used during both parts of this lab. 

Figure 2. This is the Topcon Hiper unit. 

After all the equipment was set up and connected to one another, a folder needed to be created on the TESLA to store all of the data we were going to collect. The folder contained the file name as well as other data, such as what projection was used to collect the data. The goal of this lab was to collect elevation data of our study area. The TESLA has two different data collection methods, solution and quick. Solution mode took a specified amount of points and then averaged them all to one point. the TESLA then showed the user the accuracy of that point. If the accuracy was acceptable, it would then be saved by the user.  Quick mode also took a specified amount of points and then averaged them, like solution mode. The main difference is that the averaged point was automatically saved. The accuracy would be shown to the user, but they did not have to option to save it or discard it.

Once all of the equipment was set up, turned on, connected to one another and a data folder was created, we were able to survey the study area. The assignment called for 100 points to be collected. While collecting the data, it was very important to keep the survey rod level. The survey rod needed to be leveled out each time before a point was collected. If the leveling of the survey rod did not take place, it could throw of the results of the survey. The class ran into a problem where the TESLA unit would only save 25 points for each folder created. In order to work around this problem, each group had to create 4 folders for their project in order to gather a total of 100 points.

Surveying with the Total Station:

The second part of this lab involved surveying using the Topcon Total Station (Figure 3). The total station was used to collect elevation, GPS location data as well as distance. Before the total station could be put in place, two important points needed to be collected. The first point was the occupy point. This point is where the total station will be placed and will stay throughout the duration of data collection. The second point is the backsight point. This point is what the total station will use as a zero point in order to calculate the azimuth values. Once these two points have been collected, the total station can then be set up for data collection.

Figure 3. This is the Topcon Total Station. This piece of equipment was used to complete the
second part of the lab. 

Setting up the tripod for the total station is very important. The first step is to set the tripod up at a height that will be comfortable to use for group members. After the tripod is set up, the total station is then attached to the top, making sure it is centered over the occupied point. Once it is centered over the occupied point, it is critical to ensure that the tripod is level. There is a bubble level on the tripod, and it must be centered to ensure data accuracy.

After the unit is level, the data collection can then begin. The total station is connected via Bluetooth to the TESLA. A new folder then needed to be created for the project and the occupied point as well as the backpoint were then entered into the setup. This was done to ensure the accuracy of the data being collected. The heights of the total station as well as the reflector needed to stay consistent in order to maintain data accuracy. Our group was made up of 3 members which made data collection fairly easy. One person moved throughout the study area with the reflector, another member looked through the total station to make sure they were lined up while the last member operated the TESLA.

Once all of the points were collected, the data then needed to be exported to a flash drive. A flash drive was connected to the TESLA unit in order to extract the data. The data needed to be exported as a .txt file in order to be used later in class. Once the file was brought into ArcMap it could then be edited to show what was collected.

Discussion:

The elevation data that was collected on the campus mall was fairly accurate. The more accurate model of the two was creating an elevation model using the Topcon Hiper. Figure 4, shown below, shows the results of that survey.

Figure 4. This map shows the results of the dual frequency GPS survey method. 

The extra accuracy of this model can be attributed to more points being collected. There were 75 more points collected using this method as compared to using the total station. Although the points were randomly collected within the study area, there is a fairly even distribution of points throughout the study area which also helped to create a more accurate model. Figure 5, shown below, is the result of using the Topcon Total Station.

Figure 5. This map shows the results of surveying the study are with a Topcon Total Station.

This model is fairly accurate, but does not model the terrain nearly as accurate as figure 4. As shown above in figure 5, there were much fewer point collected with the total station. Although they seem to be fairly even in how they are spread throughout the study area, it still does not give the most accurate representation of the study area.

Results:

This lab added to my knowledge of conducting geospatial surveys. After going through both methods I am now able to know which method could be better used in certain situations. The dual frequency GPS survey may take a little more time while collecting the data, but it is highly accurate, with much less opportunity for problems to arise. The total station provides extremely accurate data, but takes much longer to set up. There is also a great possibility that something may not work as it is supposed to which can take more time away from data collection.