Distance Azimuth Survey

Introduction:

This lab was an introduction to surveying using the distance azimuth technique, which is simple but usable in many situations. With today's technology, it is possible to acquire very precise location data, but it is important to recognize the fact that this technology can fail. Professor Hupy stressed this, saying that it WILL fail, whether it be from low battery life, adverse weather conditions or other failures. Because of this, it is vital to know the basic techniques in field methods to be able to function effectively, independently of advanced technology. This exercise included using a TruPulse laser distance finder to record distance and azimuth readings for a minimum of 100 data points of our choosing. 

TruPulse laser that was used to record distance and azimuth readings for our features. 

My partner Emily Moothart and I chose to survey cars in the Phillips parking lot of UW-Eau Claire.

This is an aerial image of our study area. Note that this photo is not current, but the parking lot that we were studying still is very similar to this. 

This image shows a panorama view of the parking lot we surveyed.

We were also advised to take note of the concept of declination before starting the survey. This basically refers to the difference between magnetic North and true North. This can cause error in taking azimuthal measurements based on location changes over time. Luckily, our particular location here in Eau Claire, WI has a small declination value, so it is negligible in this study. For more information on this concept, see the below video. 

The other concept that is important to consider is the difference between explicit and implicit data- in this case as it relates to grids or coordinate systems. An explicit system uses real coordinates to delineate features, where an implicit one uses arbitrary grids to do so. The result is that implicit grids only show relativity between features, without reference to its spatial location, where explicit grids do include that. This particular exercise is a kind of a cross between the two. We will calculate the locations of features without using any actual coordinates, but afterwards we will assign our starting point real GPS coordinates so that the whole thing will be usable in the GIS. 

Methods: 

Once we decided our area of study, we went to our starting point to begin surveying. We set up the TruPulse and Tripod and began taking readings of cars from left to right. The process was slow at first, but picked up relatively quickly as we became accustomed to the process. I personally operated the TruPulse as Emily recorded the readings as well as the type and color of each vehicle, on paper. 

An image of me firing the laser at nearby parked cars.
Photo by: Emily Moothart

Another image of data collection with TruPulse unit.
Photo by: Emily Moothart

It was a very warm day, but heavy winds made data collection difficult at times. The tripod would shake in the middle of firing the TruPulse, which would not allow it to get valid distance or azimuth readings. We switched halfway through so that each of us got experience in operating the laser and recording. Due to time constraints, we weren't able to collect as many points as would have been ideal. Another group of students needed to use the equipment, so we settled with just 92 features. 

After returning inside, we transferred our data into an Excel spreadsheet for later use in ArcMap. A preview of some of our points is shown below.

This shows the excel document containing points for each feature we surveyed. 

An important thing to note before proceeding into ArcMap for mapping the surveyed data is to take note of the point of origin. This means noting the exact GPS coordinates of where we were when we were firing the laser distance meter. Using one that is easily identifiable by satellite imagery is a good idea. To find ours, we added a placemark to Google Earth imagery to yield the coordinates of where we had just been conducting our survey outside. Once found, it was added into our Excel table in x and y fields. 

Next, the Excel file was imported into the geodatabase, and we used the Bearing Distance To Line tool to convert the its information into a line feature class. 

Bearing Distance To Line tool. The first field requires the table with the inputted distance/azimuth data. The X and Y fields are the GPS coordinates mentioned above of the starting point. Logically, the Distance Field asks for the distance reading, and the Bearing field requires the azimuth reading. The rest of the options should remain default so that the distance unit remains meters, the bearing unit remains degrees, and the Spatial Reference remains GCS_WGS_1984. This spatial reference operates well with the coordinates used for the starting point. 

This tool's output results in a number feature class of lines, heading to the angle recorded in Bearing and ending at the distance recorded in Distance. This is shown below in the results section. Once this tool creates the lines, the Feature Vertices to Points tool can be used to create a point feature class from their endpoints. 

This image shows the location of the Feature Vertices to Points tool. The tool is simple, but it is important that the "Point Type" field is set to ENDPOINT so that only the endpoints are created, rather than the endpoints and beginning points. 

The results of this tool are shown below. 

Results:

This is the final map showing the results of the distance/azimuth survey and its integration into ESRI ArcMap. Features are classified by type.

Discussion:

The outputs from the tools indicate that there is substantial data associated with our surveyed points. After searching through our dataset, I don't believe that there is any input error. This means that the error must have come from taking our readings. When looking at the image of our lines, it becomes obvious that even a little bit of error in calculating the azimuth will result in a large margin of error for the resulting point. An even more probable source of error comes from recording distance. If we didn't get a proper fix on the feature we were aiming at; say we missed and fired at a tree behind the car, this would become apparent on the above map. 

A strange pattern is shown in the final map where the points as they get farther away seem farther and farther south of their actual locations. Though it may be worth noting that the aerial image used as a basemap is not current, it really should not affect the distribution of our points because cars still park in the same places as they did when the image was taken. A more likely explanation is the fact that the laser's accuracy decreases as features get farther away. This comes from the TruPulse's decreasing accuracy with distance, but more significantly with the user's error as distance increases. It takes a couple of seconds to hold down the fire button on the device, while maintaining a fix on the desired feature. At high distance, this becomes difficult. Also, the adverse weather conditions should be noted as a potential source of error. If the tripod was moved by wind, all subsequent readings would be slightly off. 

Conclusion:

Though we did use an expensive laser distance finder device in this lab, it could have been conducted using much simpler tools. The purpose of conducting this survey was to familiarize ourselves with alternate methods for calculating spatial relationships among features using an implicit coordinate system. This type of survey is useful in situations where access to advanced global positioning may not be feasible either because of lack of resources, or because of technological failure.