Surveying with a Topcon Total Station and the Tesla GPS Unit

Field Activity #10

Introduction-
    The field methods class was introduced to collecting points using a total station the the Tesla GPS unit again. In last weeks lap, the class was only using the Tesla GPS unit. This weeks field activity goes in more depth, for more accurate data collection with using the Topcon Total Station (figure 1). The benefit of using a total station is that it can collect not only the x,y location values, but it can also collect the z value allowing the data to show elevation.

Fig. 1 shows a Topcon Total Station as it braves the elements. This device is very sensitive, needs to remain level and sturdy, and can't be bumped or moved during the recording process. 

One very important point is that the total station remains completely level and stable throughout the collection process. If the station is moved it throws all of the points off then. The device used to actually collect the points is called the prism rod (figure 2). This device allows for a laser to be shot out of the total station to the prism rod head and reflected back. By collecting and adjusting the data of the height on the rod, the elevation value is collected.

Fig. 2 is of a prism rod similar to what was used during this field activity. 

One major key to using the prism rod is that if the height of the rod is ever adjusted, it is very important to relay that information to whomever is recording the data with the Tesla. The last device that the class was familiar with already is the Tesla (figure 3). This device is what is used to collect the data for this field activity. The program used is called Magnet. It allows for the x,y, and z values to be recorded while maintaining data integrity.

Fig. 3 is of the Tesla GPS unit used to record the data in this field activity. 

Study Area-
    The study area for this field activity was nestled down by Little Niagara Creek between the Davies Center and Phillips Hall on campus. It was relatively a small area, about one hectare, that the data was being recorded from. Seeing as this was the classes first time using the total station, it was a perfect size. Study area to the East of the small bridge.

Methods/ Results-
    Before collecting data there is some very important things to do with the total station to make sure your points are accurate when collecting them with the Tesla. The first process is to get all the gear setup and pick the starting point that you are choosing to collect the data from. This start point is known as the occupancy point (where the Topcon Station will be sitting). Along with the occupancy point, the back site point also needs to be collected. The back site point is used as a spatial reference and used to calculate the height of the total station.

    Once these two points were collected, the total station was put over the occupancy point and is leveled. It is important to have the total station as close as possible to being directly over the occupied point along with having it as level as possible. There is a bubble level on the total station which is used to keep it level. After it is leveled, the three legs can be stepped down on securing it in place.

 

    Now that the total station is ready, the points in the study area can be collected. Unlike the HiPer when the entire station was moved around, only the prism rod (figure 2) is needed to collect the points. By aiming with the iron sight located on top of the total station, then using the magnified sight in the total station, it is key to line the sights up with the middle of the prism (figure 4). By doing this the distance, azimuth, and elevation is all collected on the Tesla (figure 5). 

Fig. 4 Total Station with Iron sight on top and magnified sight in the middle











Fig. 5 Tesla while recording points from the total station

    After all the points were recorded and collected from the different groups, it was time to process the data. The data was brought in as a '.txt' file (figure 6) allowing it to be easily brought into ArcMap. Once the data was imported into ArcMap, it was time to map it. To show the different elevations from the data an interpolation tool was used. The tool that was used was the Triangulated Irregular Network tool or TIN for short. This gave us a 2D imagery of the points along with different elevations heights (figure 7).

Fig. 6 .txt file of the data

Fig. 7 2D TIN imagery of the data gathered with the lighter colors being higher elevation. The dark area slopes down towards little Niagara creek while the lighter area is towards Phillips Hall.

After creating a 2D TIN imagery in ArcMap, it was time to see what it would look like in 3D imagery. By bringing the data into Arcscene it is possible to see the different rise and fall in elevation. Using the same interpolation tool as before, TIN, the 3D image was created. One thing however of this image is that there isn't a big difference in the elevation of the area that the data was gathered from. This leaves a 3D image that is hard to tell a big difference in elevation other than the color (figure 8).

Fig. 8 3D TIN created in ArcScene allowing us to see the difference in elevation. This was the view from the total station with the left corner being North.

Conclusion-
    After comparing the 2D imagery and 3D imagery, it is hard to really see an elevation shift between the two. Yes, the 3D imagery is easier to tell when zoomed in, but to really show the difference's in elevation there could be a few things to do. One would be to use a different interpolation tool that would allow from more elevation change, and another would be to use the total station to gather data in a more sloped area along Little Niagara Creek. If the goal of the lab was to show slope and elevation change, I would explore both possibilities.
    By using a total station for this assignment we were able to collect very accurate data. Although it takes more time to prepare the total station for collection compared to Collector or another device, it does allow one to collect very accurate data. It is important however to know if one needs this accurate of data for the job description they are doing. It's always important to understand and use the right equipment for the job. 



Surveying of point features using Dual Frequency GPS

Field Activity #9

Introduction-

    This weeks lab taught how to engage in a survey of various point features on campus using a high precision GPS unit. Features will be selected based off of codes that are already created for this project. The collection of these points allows of maps to be created for the study area on lower campus.

Study Area-

    The study area that was being focused on was the lower portion of campus for University Wisconsin- Eau Claire. Click here to see the study area of the lower campus portion. One thing to note of is that sometimes GPS units can be thrown off when standing under trees when they are full of leaves. Since this field activity is being conducted in early spring, there won't be an issue with collecting the points and possibly have even a higher accuracy then if this was conducted in the middle of summer.

Methods/ Results-

    With the class being broken into teams, each team would collect points outside on campus of either light poles, trees, garbage cans, or bike racks. The dual frequency GPS unit used to collect these points was called the Topcon HiPer (figure 1).

Fig 1. the Topcon HiPer located on top of the pole and the Tesla which is attached at the middle/ handheld.

The Tesla is the handheld device used to store the points collected in this field activity. One major key to note is making sure that the devices were as level as possible, mainly the HiPer. Without the HiPer being level, it would cause for the data to be skewed. When the data was ready to be collected, the Tesla was activated  to collect the GPS point wirelessly from the HiPer through Bluetooth. While the HiPer was collecting, it would give a reading of horizontal and vertical distance of how much it was off. The Tesla would continue to record 20 points in the same location as the horizontal and vertical distance continued to shift (figure 2). It would then take the average and give the reading of the point.

Fig. 2 Rachel collecting a light pole point. She is waiting for the 20 points to average out so she can save the point.

After completing the collection process of the various points, the data was then exported as points in a '.txt' file (figure 3).

Fig. 3 is the location of all the points recorded from the class.

This file would give X and Y coordinates allowing it to be used with the import XY tool to bring the points into ArcMap. After the point coordinates were brought in, an interpolation tool was used allowing the points to show the different elevations of the lower campus portion. The interpolation tool that was used was the triangulated irregular network or TIN (figure 4).

Fig. 4 is of the TIN showing the different elevation of the points we gathered, mostly of the parking lot.

 Looking closer at figure four, some interesting features are shown. There is a high spot relatively in the middle of the parking lot, assuming this is for drainage purposes. The high point is only a meter above the low point run offs, but that is all that is needed to keep the water flowing. There is also a high spot in the northwest corner of the study area. Here was an elevation increase from the sidewalk and landscaping made to this area. This area allows for drainage then to run along the base of it to different lower areas. Figure 5 compared to figure 4, shows just the points collected in the parking lot area of Davies. This gives a relative reference to show why certain areas have higher elevation than others.

Fig. 5 giving locations of the points in the Davies parking lot.

Conclusion-
    After completing this field activity there were a few things that were learned. Using the Topcon HiPer and Tesla is a good way of recording points if one has a wifi connection. Without that connection there would be a problem with trying to save the points of the location. This device is used to get very accurate data.

Distance/ Azimuth Survey Methods

Field Activity #8

Introduction-

    This lab is intended to show us that you can't always rely on GPS because there can be technical difficulties at times. When the GPS goes down, it is a good idea to have the knowledge and know how to be able to still collect points in the field that are relatively accurate. One way to collect points is through using angles and distance to calculate points, this is called using the azimuth. 

There is a few different techniques used to collect the azimuth, but the easiest and seemingly most accurate happened to be a Tru Pulse Laser (figure 1). The azimuth is a reading between 0 and 360 degrees, and can also be found with using a compass.

Fig. 1 TruPulse Laser 360 allowed for us to read the distance in meters, along with being able to find out the azimuth

Study Area-

    The study area was an exact point outside Phillips hall that all the measurements were conducted from. Since the weather was crappy this day, this lab was a condensed version but still were able to collect enough points to make a detailed map using the azimuth. Click Here below and you can see roughly the location that this experiment was conducted at. Click here. The location was right at the 'Y' in the sidewalk looking towards the north, northeast. The reason this location was chosen was because there was a good starting point were the sidewalk made the 'Y', and there were plenty of trees that we could gain an azimuth reading off of. 

Methods-

    For this activity more than just the azimuth of the tree locations was collected. Also collected were the diameter of the trees, distance from starting point to trees, species, and X,Y location. Figure 2 shows the table that was used to bring the points to life in ArcMap.

Fig. 2 the table and attributes that were collected during the activity.

The first step to collecting these points was to have one person go stand next to the tree while the other was standing at the starting point. The person by the tree would collect the diameter and species type of the tree. The person at the starting point would use the laser finder to collect the distance to the tree and the azimuth. There was a couple of different settings when using the laser finder, but one thing that had to be done was to make sure the distance was read in meters and to have it on the proper setting when recording the azimuth. Looking back at figure 1 there is two buttons on the side of the laser, these buttons would allow one to scroll through the setting until they would reach distance and azimuth. One trouble that the group seemed to experience was reading the different settings and numbers in the laser. It was a chilly, windy, and a rainy day out. These combined to fog up the lenses along with leave moisture on them causing it difficult to read. The only way around this was to continually wipe off the lens before reading the next tree. 

Results-

    Upon the completion of the collection of the points outside of Phillips Hall, we were able to then come back inside to process the points that were collected. A table was setup (figure 2) with all the attribute data collected. It was important to keep this table as simple as possible for the benefit of the tool that was about to be used. The tool used was 'bearing distance to line'. This would give the lines from the starting point to the tree location based upon the azimuth. The next tool used was the 'feature vertices to points' tool. This tool was ran twice seeing that it can select the starting point or the end points "tree points" in this case. The starting point was based off of the X,Y location from the attribute data. The tree points were calculated using the distance data from the X,Y location and the azimuth. With these two tool, a map was constructed for interpretation of the points and data that was gathered. Figures 3 and 4 are two different maps created from the data gathered during this activity.

Fig. 3 diameter of tree points collected based off of the attribute data.

 

Fig. 4 different species of trees collected.

Conclusion-

    The purpose of this lab was to expand knowledge in a scenario when a GPS or ground station stops working. An easy and fast remedy would be to use azimuth to collect points of interest. It is always smart to have a backup plan before going into the field and conducting an activity. Technology is not always reliable, as this lab would prove, but if one has a backup plan that works they would be prepared for any situation.

Gathering Data Using Arc Collector

Field Activity #7

Introduction-

    For the 7th field activity we were asked to pose a question, create a database, and collect and project data that was collect to try and answer the question that was posed. This would be the first lab that we create and use our own database to answer a question that we came up with. The question I wanted to find out was: which faculty parking lot on  campus has the most full sized trucks parked in them. When thinking of this question many thoughts went through my head. Is there enough trucks on campus to validate my question, which parking lot would have the most, would weather pose a factor, and how could this data be used to benefit insurance companies. 

    When it came to the creation of the database there were a few guidelines that had to be followed. In the feature class there had to be at least three fields to enter attribute data, one of the fields should be a text field for notes, one should be a floating point or integer, and one should be a category field. With following these guidelines along with properly setting up the database, it would be easier to compile the data in the field. Having proper database design is essential to keeping data organized and valid while collecting the data. By properly creating a database, collection time of data can be cut down on. Another thing to keep in mind is that it's not always the creator collecting the data. By having proper alias fields for the collection crew, one can make sure that they know what the collector means during the collection of the data. It is also important to have a notes field so the collector can document anything they believe that the database creator had missed. 

Study Area-

    Since the question I posed was about the campus faculty parking lots, my study area would be on campus. I chose two of the major parking lots to look at for my question, the Hibbard parking lot, and the Davies parking lot. When deciding on these parking lots, I knew that these were the largest ones on lower campus. I also knew that they would have to most traffic in them letting me to obtain the most data for my question. Here you can see the study area of the lower campus region that the data was collected from. Hibbard lot is in the upper right corner, while the Davies lot is in the lower left.

Methods-

    The data for this project was collected on a Friday from 2:30 pm till 3:30 pm. Since sections of these parking lots open to the "no pass" vehicles at 3 pm, I wanted to see how many trucks would be in these sections before and after 3 pm. I also knew that by collecting this data around these times that there wouldn't be an insane amount of trucks in either of the parking lots, but was surprised by the data that I found. Before the collection of the data I believed that Davies parking lot would have more data collected than the Hibbard lot. The Davies lot is much bigger so I figured this would come into play for more data. I was wrong and Hibbard actually had more trucks in the lot. 

    Another problem I had when collecting the data for this project question was maintaining the integrity of the location of the points. When collecting the data I wanted to stand directly behind the trucks allowing for all my points to be consistent. Although I stood behind the trucks directly while gathering the points, another problem then came into play. Arc Collector is not all that accurate, in figure one you can see what I mean. 

Fig. 1- Location of three points collected using Arc Collector that are not accurate. 

Figure 1 displays three different points from data collected in the Davies parking lot. Clearly there is not trucks parked on the sidewalk or on the grass, but when using Arc Collector since it is not all that accurate at times I was left with these points. This leaves some of my data skewed, because at other times as in figure 2, the points collected are exactly where I was during the collection of the points.

Fig. 2- Location of three points that are accurate with Arc Collector.

Figure 2 displays points that were collected using Arc Collector that were accurate to where I was during the collection process. Although these are accurate, and the points in figure one are not, this leaves my data integrity up in the air. One way to prevent this would be to use a more accurate data collection process. Another way that this could have been avoided was to use a more accurate location fix by being connected to WiFi.

Results-
    The data that was collected for each of the parking lots was a shock to me. As I mentioned before, I first believe that Davies lot would have more full sized trucks considering that it was a larger parking lot and seems to have more traffic flow. With the more traffic flow, I also believed that there would be more accidents in this lot due to having large vehicles parked there. Having a larger vehicle would be prone to dinging doors of other cars, or side swiping them when trying to park/ leave a parking space. If data like this would be presented to an insurance company, they could potentially come up with a policy stating that large trucks could only park in designated parking spots. Although this may be unpractical, it would be a way for insurance companies to save money potentially. Figure 3 shows all the full sized trucks parked in the Davies parking lot.

Fig. 3- All the full sized trucks parked in the Davies parking lot. 

    After the collection of the data in the Davies lot was complete, I moved onto the Hibbard lot. The Hibbard lot is located right on a busy street and tends to only allow faculty parking until 6 pm. Since only faculty could park here, I believe that this would have a factor in not having as much traffic and not as much full sized trucks in this lot. Figure 4 shows all the trucks parked in the Hibbard lot. 

Fig. 4- All the full sized trucks parked in the Hibbard lot.

After looking at the data collected it is easy to see that the Hibbard lot had more full sized trucks parked there between 2:30 and 3:30 pm. This shows that there was more traffic during this time of the day at the Hibbard lot for faculty members. The final map displayed in figure 5 shows both the Davies and Hibbard lots and all the trucks parked in them. 

Fig. 5- Davies and Hibbard parking lots.

    Although I only focused on the Davies and Hibbard parking lots, there are also other lots on lower campus that can/ do have trucks parked in them. Some of these lots are meter lots, and some of them are small and have campus work trucks parked in them. It would be interesting to expand my research question to incorporate these lots and to find and determine the consequences that these large vehicles could have in these lots as well. 

Conclusion-

    Without proper database design it would have been difficult to gather all the data I needed in the field. The database I created allowed for me to have a drop down of the make of the truck and how many trucks there were. I also had a notes field and incorporated an estimated year field. I could make lots of different maps incorporating all these fields together, but focused on how many trucks were in each lot based off of my question. One thing I would have done differently for the question would be to incorporate SUV's and other large four door vehicles. Trucks are not the only large vehicle that has a problem in tight spaced parking lots leading to accidents and dings and scratches. By expanding and cross examining the information from insurance companies, I believe that the conclusion would be to have a section just for trucks and large vehicles to park in some parking lots. This could lead to fewer insurance claims and make everyone happy. There is nothing worse than coming out of class and seeing that you have to squeeze between cars just to get your driver door open eight inches to mutate into an octopus to get into the drivers seat.