Field Activity #3: Conducting a Distance Azimuth Survey

Introduction

This week we learned how to conduct a survey without the use of expensive and often times unreliable survey equipment. Whatever the case may be, advanced technology is known to fail at times, causing you to resort to classic surveying methods. To prepare ourselves for these unfortunate situations, this week we learned how to survey using the distance azimuth method. Although less technologically advanced, when collecting implicit data, this can be used as a reliable alternative survey method.  During this lab we collected distance and azimuth values, imported the data table into ArcMap, and ran a series of tools to generate a final output map of our data. The methods and results of the project are listed below.

Methods

1) Choosing a Study Area:
After debating between a few locations, we chose the survey features within a portion of the University of Wisconsin-Eau Claire campus. We ultimately decided to survey here because the land is flat and the line of sight is wide open, making all objects readily visible. Once we headed to our field site, we located a tree south of Schofield Hall to be our point of origin. From this perspective, the most abundant and overt objects were trees and the concrete blocks on campus, so we chose to survey these features.

2) Data Collection Methods:
At the field site we had a TruPulse Laser, GPS, camera, and a computer. We began by using the GPS to collect the XY coordinate of this location, because the distance azimuth data we collect will be based off of this exact location. After scanning our view, we determined that sitting down would be the best way to eliminate errors from accidently moving while pivoting around our origin. We thought sitting may also help with measuring the concrete boxes because they are somewhat low to the ground.

Next Peter and I decided we would collect points on the trees and concrete blocks within our line of sight. Peter began sampling the concrete blocks from the east, slowly to the south, and eventually ended facing northwest. When sampling for trees, Peter began at the northeast most tree and continued sampling south until we reached 100 data points. At each object being sampled, Peter collected the azimuth and distance data. The azimuth represents the objects position in relation to the  distance measured in degree from a reference point, which this case is due north (Esri). The distance represents the distance between the object (end of our laser line) and our location (the origin) measured in meters. As Peter collected the measurements, he said the type of feature and its azimuth and distance values out loud and while I entered them into an excel file. After collecting 100 points, we decided to add an addition point to see how far the laser would be able to measure. Peter pointed the laser south at the tree farthest in the distance, which was located within a forested area within our campus call Putnam Park.




Figure 1: Peter stilling at the point of origin, whiling collecting the distance and azimuth data of a nearby tree using the TruPluse Laser.




Figure 2: View of UWEC campus to the east of our point of origin.




Figure 3: View of UWEC campus to the south of our point of origin.

 

Figure 4: Concrete blocks organized throughout campus. The blocks are laid out in an amphitheater design, which are meant to used as benches.

 

3) Organizing the Data into Excel:
Before importing the excel table into ArcMap, I made sure the table included the following fields; object ID (OID), distance, azimuth, feature type (type), X coordinate of origin, and Y coordinate of origin. Additionally I made sure that column with numbers were set to "number" field, with the appropriate number of decimals, and that the feature type field was set to "text" because it included words.

Figure 5: A portion of our excel data table.

4) Processing the data in ArcMap:
After the table was properly organized in Excel, I imported the excel table into ArcMap. The next step was to run the Bearing Distance to Line tool on the table. This was found in ArcToolbox, under the Data Management tab within the Features toolset. In order for the tool to run the distance azimuth function properly, I needed to specify which field in my table corresponded to each drop box field in the tool window. Additionally, I selected the spatial reference to be set as the WSG 1984 coordinate system. All of the settings I chose for the Bearing Distance To Line tool are show below in figure 6.

Figure 6: Setting chosen for the Bearing Distance to Line tool.

Once I ran the tool, it projected an output image (figure 7). This image has lines leading to each object we sampled. The lines could be considered an images of our laser shots to each object.

Figure 7: Output of Bearings Distance to Line tool.

Next, in order to add a point feature class to represent our survey locations, I ran another tool called Feature Vertices to Points. Once again, this was found in ArcToolbox, under the Data Management tab within the Features toolset. Once the tool is open I was required to select where feature's vertices should be transferred to along the feature. Knowing that our features are located at the end of each Bearings line, I transfer the points to the "end" of each line, or in other words at the features last vertices. The points are displayed below in figure 8.

Figure 8: Output of Feature Vertices to Points tool with the Bearings Distance
 to Line output. The points are a separate feature class than the Bearings lines.

My final steps to symbolize the data were then taken. Due to the fact the Bearings Distance to Line Output does not retain the "type" field, I was unable to symbolize the data unless I retrieved this data attribute. I did so by running at table join, using my Feature Vertices to Points feature class as the destination table, and my original table as the source table. I joined these together by using the OID fields as the common attribute. This allowed me to designate different symbols for the trees and concrete blocks. The last element I found necessary to include was our point our origin. I was able to import this point by displaying the XY data from our original table. The final symbolized maps are displayed below. Figure 9 displays the all the data, and Figure 10 is focused on the majority of our data points, excluding the southern outlier.
 

Figure 9: Final output of our distance azimuth data, displaying all data points. 

Figure 10: Final output of out distance azimuth data, zoomed in to the majority of our data in greater detail, while excluding the southern outliner data point that is visible in figure 9.   

Metadata

Who	Ally Hillstrom and Peter Sawell
What	Collection and presentation of distance azimuth data.
When	Data was collected on September 30th, 2015.
Where	Data was collected on the campus of University of Wisconsin-Eau Claire, in the campus mall directly south of Schneider Hall.
How	Data was collected using a TruPoint Laser. This collected the distance in meters the object was located for the area we were standing. This equipment also collected the azimuth degree of the object being surveyed.

 Discussion

Before beginning our independent data collect for our lab, our class practiced using the distance azimuth survey tools. Each lab group collected a handful of distance azimuth readings, and imported them into ArcMap. From here we ran the Bearings Distance to Line tool, and Feature Vertices to Point tool to generate a point feature class. Every group in the class projected points, however none of our points were in the correct location. Our points were about another 30 degree north of where they should have been. After checking settings such as the declination setting of the tools, our professor determined that the electro magnetic force within the area we were standing was significantly altering the accuracy of the point locations. Now that I have experienced the issue first hand I would be able to recognize the issue in the further.

When review the final data output (figure 9) it is clear the point locations are not entirely accurate. For example, there are trees symbolized on the roof of a build (Davie's Center) and in the parking lot. Additionally, looking closing at the concrete blocks points, in comparison to the blocks within the picture, you can see how most are shifted to the west of their real life location. One reason this could be off is that Peter was sitting down while taking points. Perhaps standing would have allowed for a larger target, and higher accuracy data values. This discrepancy could be due to the electro magnetic field within campus, or the improper declination setting. Seeing that all points are off by a similar value, I suspect there is a problem with the declination setting of true north. We did not bring a compass out with us, which would have potentially made our points more accurate. According to the WI State Cartographer's Office, Eau Claire has a declination of 1 degree, therefore this number should have been subtracted from the north reading on our compass. Another fault in our data could have been the accuracy of our origin coordinate. Unfortunately the GPS we used was highly inaccurate, forcing us to find our location in ArcMap. Although this most likely does account of all inaccuracy of our points, it very likely may play a role.

Although the points' locations are not precisely accurate, each point is within the general area of the object they are symbolizing in the image, and the distance between each symbolized point seems to accurately portray the real life distance between objects. The accuracy of this data is somewhat relative to what it would be used for. In our class, we are solely interested in collecting implicit data, and therefore are not concern with the precise XY coordinates of a point, but rather the general location of an object. For this reason, our output shows the distance azimuth survey method should not be used to collect explicit data, but implicit data instead.

Another issue that may be attributing to the accuracy was the troubleshooting we experienced obtaining our origin coordinate. Initially, we used a Juno to collect this XY coordinate, but once we entered it into ArcMap we discovered it was farther southwest then where we were standing. Using ArcMap we found the XY location of the tree we used as our point of origin. Using this new coordinate increased the accuracy of our final projected points, but as previously stated the accuracy is still not the best.

Lastly, to confirm, the southern most outlier point (seen in figure 9) had a distance of 346 meters. Our intent was to the tree furthest away in our line of sight. The accuracy of these point's distance is somewhat unknown but looking at it's location on figure 9, the point is indeed on the farthest edge of Putnam Park.

Conclusion

Through this activity I learned reliable geographic surveys can be completed without having to rely on advanced technology. I now understand that the distance azimuth method is a great back up to have in case our original method fails, as it uses simple distance azimuth values to locate features. Although my partner and I experienced issues associated with precise accuracy, the method appears to be useful when in need of relative locations of features. This lab demonstrates the importance of learning both new and older geographic data collection techniques.

Sources

http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=feature_vertices_to_points_(data_management)
http://support.esri.com/en/knowledgebase/GISDictionary/term/azimuth
http://www.sco.wisc.edu/mapping-topics/magnetic-declination.html