Last week in class, we learned how to survey an area using a grid survey technique. This week, we learned how to conduct a survey using the distance/azimuth method. In this day in age, we have many different high tech instruments that can be used for precise surveying methods. Although this technology is great, it can, and usually at the worst time, does fail. This could be caused by many factors including bad weather, or something as simple as instrument malfunction. It is important to know different methods of completing a particular task, and this is what that lab this week was intended to teach the class.
The instrument our group used this week is called a TruPulse Laser (Figure 1). This instrument gave us the reading for both the azimuth as well as the distance we were from the object being surveyed.
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| Figure 1. This is the TruPulse Laser that was used to collect data. |
Although this instrument can be extremely useful, it can also have its downfalls. During the demo for this exercise, the class found out first hand problems that can arise while using technology. At the location where points were being collected, we discovered what electromagnetic interference (EMI) can do to your results. It caused many of our points to be inverted, or no where near where they were actually taken. After going through this experience in the demo, we learned it was important to keep EMI in mind when collecting data so it does not ruin a survey.
Study Area:
The exercise this week called for the students to survey an area that ranged from a quarter to one hectare in size, as well as collecting a minimum of 100 data points. Given the parameters of the assignment, our group decided that our study area should be the parking lot south of Phillips Science Hall. We decided that the best option would be to collect data on the vehicles that were in the parking lot. Since it is a large open area, it also offered excellent visibility to enable easier access for data collection. The figure below (Figure 2) shows the area of study for our group.
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| Figure 2. This is a map showing the study area for the exercise. |
Although this parking lot offered many areas for collecting data, we were not able to collect all the data from one point. The figures below (Figures 3 & 4) show the view we had while collecting some of our data.
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| Figure 3. This image shows the parking lot from the third survey location. This image was taken facing east. |
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| Figure 4. This image was taken from the third survey location. This image was taken facing west. |
Methods:
Survey Methods:
In order to get the most accurate reading from the TruPulse Rangefinder, it should be mounted on a tripod. This allows for a constant height to be maintained throughout the duration of data collection. Although this may be the best method, you may not always have a tripod, which was the case for our group. Since it is important to keep a constant height while collecting points, we tried to keep the rangefinder in the same position while collecting our data, to minimize human error. Furthering this effort, one person read off the azimuth and distance, while another group member recorded the results in a spreadsheet. Not only did this allow for smooth data collection, it also helped increase our accuracy by minimizing the amount of times the rangefinder changed hands.
Data Entry and Preparation:
As mentioned earlier, adding our data to a spreadsheet in the field saved us time when coming back to the lab with our results. Since we did not have to transfer from a written copy to a digital form, we were able to start performing analysis much sooner. After loading the spreadsheet on the computer, there were a few errors within our data. Some of the errors that we encountered were misspelled words or the same words with different letters capitalized. In order to perform effective analysis, these errors had to be converted into a standard format.
Once the spreadsheet was all corrected, a geodatabase had to be created in order to store all of our features that would be created later in the exercise. After the geodatabase was created, the excel spreadsheet then needed to be imported into that particular geodatabase. The image below (Figure 5) shows how our data was structured.
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| Figure 5. This table shows how the data was organized while collecting points in the field. |
Although the method described above is the most ideal, things do not always work as planned. I encountered an error later in the lab that related to my excel spreadsheet. After numerous attempts to fix the problem, and advice from Dr. Joe Hupy, I realized I needed to convert my excel spreadsheet into a text file. The text file provided the same data that was in the spreadsheet, just in a different format. Once that problem was solved, I was able to continue on with the exercise as planned.
After the text file was imported into the geodatabase, the bearing distance to line tool needed to be ran. The figure below (Figure 6) show what is used within this tool.
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| Figure 6. This shows the different inputs needed to run the bearing distance to line tool. |
This tool created lines that connected our survey points to the data points we collected. The input table corresponds with the text file that was imported into the geodatabase. The X Field, Y Field Distance Field and Bearing Field all correspond to the data that we collected while in the field. Once the lines were created, the features needed to be converted into vertices. The features to vertices tool enabled the student to complete this task. The tool inputs are show below (Figure 7).
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| Figure 7. This shows the different inputs needed to run the features to vertices tool. |
Once the features were converted to vertices, they were added to map, as well as the lines created from the bearing distance to line tool. The vertices represent the actual location of the features surveyed. The figure below (Figure 8) shows both results.
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| Figure 8. This maps shows the results of the bearing distance to line tool as well as the features to vertices tool. |
Although it is important to know where the features were located, this lab also was meant to teach the student how to collect attribute data. The data attribute data our group collected was the color of the vehicles being surveyed. In order to show the different colors on a map, the vertices needed to be classified by the color attribute. The student needed to go into the properties of the feature class, and change the symbology of the points. Originally the vertices were shown with one color, but the symbology needed to be changed to show the points by car color. The map below shows the different car colors (Figure 8).
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| Figure 9. This map shows the attributes for vehicle colors of the points we collected. |
Results and Discussion:
After looking at our data points and comparing them to the aerial imagery, they seem to be fairly accurate. Most of the points are on the edge of the parking spaces, instead of the middle parking spot, but that is because of our surveying method. Since we were eye level with the vehicles, the point closest to us is what was measured, which would have been the edge of the parking spot. There was one data point that was extremely inaccurate. It showed up on the roof of Phillips Hall. Since it is not the parking lot, nor did it have a vehicle on the roof, this was most likely cause by human error. It could have either been a data entry error or the settings on the TruPulse Laser could have been different for that specific reading. There are also points that are very close to one another. This could have been caused by data entry error, but more than likely it is related to our survey method. Since we could not get all of our data points from one survey location, we had to change locations, but always looked at the same parking lot. After moving locations, the same vehicle could have been measured again accidentally.
Conclusion:
This exercise taught the class different ways to collect data, specifically a technique that was not dependent upon technology. Although we were fortunate enough to use a TruPulse Laser, this same technique could have been used with a compass and measuring tape. It also taught the class different factors that could cause data collection error, such as EMI. All of these skills will prove valuable during data collection in many different situations.
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