Activity 3: Distance Azimuth Survey

Introduction

This week's lecture focused on problems that can occur in the field while collecting data. As technology often fails--for instance, a GPS may not work well in a densely forested region that interferes with satellite connection--we must be prepared with methods to counter any unexpected failing of technology. It is for this reason that this week's lab focused on Distance/Azimuth surveys--a survey method that does not rely heavily on technology. It is possible to create a map of a local study area from having little or no GPS signal. A researcher could potentially view satellite imagery of their study area after collecting points with a distance/azimuth device to find their origin GPS point if their origin is an easily recognizable feature. All objects within the study area will be accurate to that origin point if the researcher is able to find the distance of another feature from their origin point and its azimuth. To gain experience in this method of data collection, my partner and I went out to the campus common area to take these distance and azimuth measurements and create a data table and upload it to ArcMap where we ran several tools on the data to create a map with our feature locations.

Methods

Choosing the Study Area:

We were assigned to obtain 100 data points from a local area of our choice and create a map of our data including distance and azimuth data and attribute data of the features we were to map. My Partner, Scott Nesbit, and I chose the University of Wisconsin-Eau Claire campus commons as our study site because it was a large, open area that could easily be seen on satellite imagery which we could use as a basemap (Fig. 1). It also contained a large number of easily identifiable objects we could use to map our 100 points. Within our study area, we chose an origin point from which to gather distance and azimuth data in a standing position (Fig. 2). This origin point was a site that had an unobstructed view of most of the landscape and was hindered little by elevation differences within the site.

Fig. 1: The Study area (University of Wisconsin-Eau Claire Commons Area).

Fig. 2: Origin Point from which all our data points were collected, It was an easily identifiable and open area.

Collecting Data:

On September 3, 2015 at 3pm, we went out to our study area to collect our data points using a TruPulse 360, a laser range finder equipped with both the ability to calculate distance and azimuth direction, and a GPS unit to find the latitude and longitude location of our origin point. We used the SD mode on the TruPulse 360 for distance and AZ for azimuth calculation.

Fig. 3: Equipment used to take the Distance, Azimuth, and Origin Point data. On the left is the GPS unit, on the right is the TruPulse 360 laser range finder.

We then organized a data table in excel to include the XY coordinate of the origin point, Distance (meters), Azimuth (degrees), and a feature ID describing the type of feature being mapped (Fig.4). We entered all data directly into the laptop to be uploaded to ArcMap after collection. The objects we mapped included the large rock benches, large trees, signs, and all lamp posts within view. Some lamp posts were not recorded as the laser was obstructed by various objects in front of them. Because the lamp posts were also a narrow target to hit with the laser when collecting the distance and azimuth data, we aimed for the top region of the lamp post where it was the widest. Data was not entered for the lamp posts until several re-shots with the TruPulse 360 were taken to be sure the correct distance and azimuth data was being taken. We were able to gather a total of 107 data points.

Fig. 4: Data table in Excel showing XY, Object ID information, Distance, and Azimuth columns. 

Once out of the field, we each created our own map on ArcMap by first importing our data table from excel to ArcMap using the import (single) table tool. I then ran the Bearing Distance to Line tool found under Data Management tools in ArcMap (Fig. 5) to create the distance lines using our data table (Fig. 6).

Fig. 5: Bearing Distance To Line Tool showing selections for all parameters. 

Fig. 6: Bearing Distance To Line Tool output showing lines generated from the Distance and Azimuth data.

After obtaining our line features, I used the Feature Vertices to Points tool under Data Management tools to convert the endpoints of our distance lines into feature class points (Fig. 7).

Fig. 7: Feature Vertices to Points tool output displaying the endpoints of the previously generated lines as a new feature class containing the locations of all objects.

These feature class points are symbolized as Row 1-5 Benches, Lamp posts, trees, and ground signs. Because the Bearing Distance to Line tool does not retain the attribute data, I had to acquire this information by using a table join in which the Feature Vertices to Points feature class was the destination table and the original data table was the source table. I joined the tables using their ID, listed as "ThingID" in our data table to avoid nonrecognition by ArcMap, as their common attribute. I was then able to successfully symbolize the different objects withing this feature class. A basemap was then imported into ArcMap from the University's geospatial data file (Band 1 image from the 2709_29 folder under City_EauClaire_3in in County Data)  to show the accuracy of our data. As the last step, metadata was created for the data (Fig. 9).

Fig. 8: Symbolizing the Feature Vertices to Point feature class.

Fig. 9: Metadata of Feature Vertices to Point output.

Analyzing the Data:

The features in our map did not seem to match well with the imagery basemap (Fig. 10). To fix this problem, I thought perhaps the coordinate systems were not similar. I used the define projection tool on the distance lines as well as the feature vertices to point features. This did not change the outcome by any significant measure. The data seems skewed in such a manner that if the data were to pivot westward on its axis, it may line up better with the features in the imagery map. This inaccuracy could be due to the natural magnetic declination. However, data collected further from the origin point also seems too skewed for any compensation in magnetic declination to correct the error.

Fig. 10: Final Map showing all features within the Campus Commons. You can see the westward shift tendencies of the data points and the increasing skewness of the data points as distance from the origin increases.

Discussion:

Our study area proved to be a good place to collect our data points as it was relatively flattened and open with little obstruction in our view of our objects. There was some problem with obstruction when collecting distant lamp posts, but we tried to compensate by firing the laser to collect multiple distance and azimuth points for each object until we could gauge the most accurate reading. Even in doing so, however, there seemed to be much inaccuracy with the more distant features of our map. This could be due to the equipment. I would suggest having multiple origin points from which to collect the distance and azimuth data to lessen the effect of distance on the accuracy of the TruPulse 360 readings. Another problem with the data seemed to be the effect of the magnetic declination. All data points were shifted westward and were not corrected after defining a projection for data to math the GCS of the basemap. 

Conclusion

This lab allowed me to gain experience with the distance azimuth survey method and to be aware of any potential problems that may arise in the field when collecting data and the usefulness of alternative surveying methods to counteract problems as well as save time. This survey method allowed my partner and I to collect data points in about half the time it did for my group in previous labs. This method could be used in situations where many data points must be collected in a small area. To save time from needing to collect GPS points of every feature, a distance azimuth survey method could be used to instead calculate the locations of each feature from only one or a few GPS points.