Assignment 2: Understanding Distance Azimuth Surveying

Introduction

For this lab, the goal was to work with surveying equipment, data collection methods, and improvising in the field. This initiative was done by collecting the distances and azimuths of trees from two central points. Doing such rudimentary surveying methods can be useful when access to more technologically advanced platforms are limited or not working properly.

The distance azimuth is found by collecting the distance and horizontal angle to a feature from a center point.

Figure 1: This image shows how a distance azimuth measurement is collected. 

In figure 1, the dotted green line would represent 0 degrees and therefore true north. The azimuth is the degree measurement from true north-- labeled as "bearing" in this image. The distance represents how far away the feature being measured is from the data collector and serves as the endpoint for the azimuth.

Methods

To complete the distance azimuth survey lab, a few tools were required (from top left to right): a GPS unit, a compass, a laser measure (optional), and tape measure.

The first step was to locate the survey area and objective. For this lab, the features being surveyed were distances to trees from two different center points. The circumference and tree type were collected as attributes.

Once the objective was identified, the data collection process could begin. To do this, the center point was found with the GPS unit. Writing down the center point coordinates helped to ensure the location throughout the exercise would not change. Next, the features used in the data collection process were identified and number of points collected was decided. For the purposes of this lab, between 10 and 12 points (trees) per center point would've been enough to gather a good dataset to work with. Once features to be surveyed have been identified, the distance from the center point to the tree was recorded. This could've been done using the tape measure or a laser measure. Then, the azimuth or bearing of the tree from the center point was found. This was done using the compass and recording the degree that the tree faces. Lastly, the attributes of the tree (circumference and type) were documented using the tape measure and Dr. Hupy's knowledge of wood types.

Upon the completion of the data collection, it was brought into excel and standardized-- meaning all columns have the same entry methods and conventions. From there, the excel table was brought into ArcMAP and used in the Bearing Distance to Line tool. This tool can be found in the Data Management > Features Toolset and uses the X, Y coordinates, azimuth, and distance fields to create line features. Then, the Feature Vertices to Points tool. This tool can be found in the Data Management > Features Toolset and uses the vertices of the lines that were just created to make a point feature class.

Results

After collecting the data and creating the maps, it is clear to see the simplicity involved in this method. By making a few columns of data and bringing them into ArcMAP, the user is able to create an informative map about the distances and azimuths of features from a central point very quickly.

Discussion

Overall, the technology provided worked well enough that we collected some fairly accurate data relatively quickly. It turned out that the compass we used to record azimuths had an abnormal degree system, so we had to convert our data to a convention that ArcMAP would understand.

For the most part, we used the laser measure tool to collect distances to the trees, however sometimes the laser wouldn't detect the distance because it was out of range and/or the point was too far away for the point to be visible on the tree. When this happened, we tried to have a group member stand in front of the tree for greater surface area, but this didn't work, so we resorted to using the tape measure instead in these instances. The weight of the tape measure increased as it was drawn out, which made it harder to keep straight. This most likely had an effect on the accuracy of the distance and is why the laser measure is more useful in this effort.

Another problem our group ran into was identifying the tree types. Dr. Hupy helped us out on most of them, but otherwise it was just a plain guess. Since the purpose of this lab was to become familiar with data collection, the accuracy of the tree type was arbitrary, but was an issue nonetheless.

When creating the resulting map, I wasn't sure how to best represent the circumference attribute for the trees, so I used a graduated symbology which classified the circumferences into five different ranges of values. Since the attributes weren't a huge part of this lab, I figured this would be sufficient.