Before the time of high-grade GPS units and geospatial technology that is available to most of us today, measurements had to be taken in much more of a manual fashion. Even in todays world, it is still important to understand and be able to use these techniques due to the fact technology can fail, or depending on the area, may not be available. With the use of a laser, you can find the distance and azimuth (angle) to an object, and then manually plot these points off of your initial location.
Study Area
To complete this exercise, a study area of large size must of been chosen to survey. For our group, we chose the parking lot of Phillips Hall on the UWEC Campus (Fig 1). What the group surveyed was up to them. In our case, we chose the survey the type (car, van, SUV, etc.) and color of vehicles parked in the lot. To complete this task, two different survey points were chosen to get a vast amount of data. The first point was on the southeast corner of the building, and the second on the southwest corner of the building.
Figure 1. Aerial Photograph of study area on the University of Wisconsin-Eau Claire campus. The study area included the west and south parts of the Phillips Hall parking lot.
Methods
To perform this lab, the methods were pretty straight forward. We were to pick an area large enough to collect 100 points of data and provide attributes for each data point. When starting out, it is important to set up the tripod and laser on a known location that is easy to identify like a corner of a building or plot of land. You want to do this because you will need to establish the coordinates of the shooting location to be able to plot the points being collected. For our group, we set up on the southeast and southwest corners of Phillips Hall. To collect data, a TruPulse Laser was used (Fig 2). This laser provided the distance based off the time it takes for the beam to return and also the azimuth to the object. In our case, the laser was being used to locate cars in the parking lot (Fig 3).
Figure 2. TruPulse Laser used to take measurements during survey. The laser provided both the azimuth (angle) and distance (in meters) to the object of interest.
Figure 3. Photo showing the set up of the tripod and laser for data collection on the UWEC Phillips Hall parking lot.
All of the data points had to be recorded by hand in a notebook and then later put into a excel spreadsheet (Fig 4). In the spreadsheet you also need to include the starting coordinates for each data point in X, Y format (the location in which you were at). It is important that when you collect your starting coordinates that the map you use to do so is in the same coordinate system as the basemap you are using in ArcMap. Then by importing the spreadsheet into ArcMap, two tools needed to be run to project the data. First, a Bearing Distance to Line tool was run to project the lines from the starting point to the object (Fig 5). This tool is fairly simple to run and can be found under the Data Management section of the toolbox. This tool uses the X, Y, distance (HD), and azimuth (AZ) to create a line to the object. Next, the Vertices to Points tool can be used to take the feature class created by the Bearing Distance to Line tool and create points on the ends of the lines marking the objects surveyed (Fig 6).
Figure 4. Excel spreadsheet used to import the data into ArcMap for analyzing.
Figure 5. Map showing the lines created by the Bearing Distance to Line tool in ArcMap.
Figure 6. Map showing the points created by the Vertices to Points tool in ArcMap. These points are marking the objects surveyed.
Results
Once finished, you can create a map showing the attributes created. Since each tool creates a new feature class, you can easily add symbololgy to the data. On the map below, each point designates the color of the vehicle surveyed, while the lines designate the type of vehicle (Fig 7).
Figure 7. Map showing the classification of vehicles in the Phillips Hall parking lot. Each point classifies the color of the vehicle, while the line classifies the type.
You are also able to easily create graphs of the data showing the attributes found in the survey. The number of vehicles for each color can easily be graphed (Fig 8). This can also be done for the types of vehicles (Fig 9).
Figure 8. Bar graph of attribute data showing the number of each color of vehicle in the survey.
Figure 9. Pie graph of data showing the number of each type of vehicle in the survey.
Discussion
Although this exercise seemed very simple in terms of data collection, there were still many implications that occurred. First, the accuracy of the survey was not very high. When the data was projected into the lines and points, many of the points were not in the correct location. Although they were within feet of each other, a GPS unit would be much higher in accuracy. Some of the points seemed to be overshot, while a few were undershot of true location.
Another implication was the importing of the table into ArcMap. It definitely takes time to format the table in the correct matter for it to easily work with the programing. There were also many implications with getting the tools to properly run. Majority of the issues came from the X, Y coordinates. First, they were not in a highly accurate location, but then once corrected, ArcMap had issues with the table in the Bearing Distance tool. After numerous attempts, the tool finally worked. It is important to make sure the coordinates are in meters and not decimal degrees, and also to keep the X,Y coordinates in the correct order.
While performing the survey, the only implications were holding the button down long enough, trying to remember what objects were previously surveyed, and making sure to be using the correct settings on the laser.
Conclusion
Overall, this exercise was a great way to learn a field technique to use when high-grade technology is not always available. It is important to know these basics for future reference and they still can be implied to many surveys today.
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