Lab Eight: Azimuth and Distance Survey Technique

Introduction:

The focus of this lab is how to collect spatial data points with the use of an azimuth compass and distance measuring devices. Tree locations and species were surveyed on the University of Wisconsin Eau Claire Campus.

Study Area:

All measurements were taken Northwest of Phillips Hall on the University of Wisconsin Eau Claire Campus. The weather on this particular day (April 5, 2016 at ~4pm) was rainy and 2-4 degrees Celsius.

Methods:

Figure one. Field notebook used to collect tree data. Author’s Note: “Waterproof paper is not worth much without a waterproof pen.”
Field notebook for scale. 

The first step to this exercise was to collect data, a Truepulse 360 B was used to measure distance, a Sonic Combo Pro was used to measure both distance and azimuth and finally a simple compass was used to compare the results of the other tools. Tree trunk diameter was recorded with the use of a tape measure wrapped around the trunk a little over a meter from the ground. All information was recorded by hand via pen/pencil to paper (figure one) and later transferred to an Excel spread sheet for integration to ESRI ArcMap.

Each point was collected by teams of four people in operation of equipment, two would go to each individual tree, measure its diameter and position, and place the range finding device on the tree’s trunk. The other two people would work the corresponding distance measuring equipment and record trunk diameter and tree species as dictated by Dr. J. Hupy, course instructor.

The final and probably the most important part of this operation was to collect a principle measurement point via a high accuracy GPS device. This point symbolized where the distance and azimuth measurements were taken from and was used as the starting point for the following operations. 

Figure two. Tool flow model for tree location data. 

After measurements and observations were taken in the field they were transposed into an excel spread sheet and added to ArcMap using the principal point coordinates. From here a series of tools were run to determine where the trees were located (see figure two).

Figure three. Bearing distance to line tool settings. 

The first tool was a bearing distance to line transformation (Figure three). By using the principle point as a starting location, the azimuth reading for line direction, and the distance measurement, a straight line from the starting point to each individual tree could be calculated (Figure four).

Figure four. Resulting line feature class from bearing distance to line tool. 

Figure five. Feature Vertices To Points tool used to create endpoints that show the locations of trees. 

It was then possible to use this new line feature class to create another point which represents the locations for each tree. A feature vertices to points tool was used for this operation. Note in figure five that “END” was selected for the point type option, meaning that only the terminal end of the line will have a point created.

Figure six. Join field tool used to add data that was dropped from earlier operations. 

 One minor setback about using this tool process is that non-essential data fields appear to have been dropped from the attribute tables of the dataset. This issue was rectified by using the join field tool as seen in figure six. This added both diameter and species observations to each tree point.

Results: 

Map one. Tree location results derived from azimuth and distance measurements. 

This survey technique is low tech and moderately accurate in relation to the starting point. These assets can also be considered the main issues with this survey technique, it is not going to be accurate in relation to the real word if the principle measurement point is not precise. But, for this exercise, real world accuracy of each point is not as important as the accuracy relative to other points. For example, one may notice how in Map one there are tree location points that appear to be inside of Phillips Hall. In reality these trees are not in Phillips Hall. What matters are the spatial relationships between each tree.

In earlier lab reports a GPS device has been used record survey points. This is easier, takes fewer people, is faster, and often more accurate to the real world than when compared to an azimuthal survey technique. Unfortunately using a GPS device to record data can also be quite frustrating as there are typically a plethora of technical issues associated with the technology. A great deal of these issues, such as problems acquiring satellites and inflexible domain settings, do not accompany azimuth distance surveying.

Conclusion:

As mentioned in Results, the real world accuracy of the tree location points are questionable when compared to the real world, as several of the trees are placed within a building. It would seem as if all the trees have been displaced Southward by five to ten meters. The tree locations are fairly accurate in relation to one another. With only a few exceptions, such as the two White Birch trees next to the river on the East side of the map. These two trees actually grew close enough together to have a merged main trunk, meaning that the two points should be nearly on top of one another. According to the map they are displaced by an approximate 5 meters. It is undetermined what the cause of this discrepancy could be, but as these trees were amongst the last to be surveyed, it would be no surprise if a lapse of user technique was the cause.