Field Activity #6: Distance Azimuth Tree Survey

Introduction

Surveying with a grid based coordinate system works well on small plots, but when the area of study is large other methods become more dependable. One important survey method that was heavily relied upon throughout cartographic history is the distance azimuth survey. Modern GPS technology and survey stations have widely taken over as the most accurate and widely used data collection method, but a knowledge of distance azimuth surveying is still important, both for knowledge purposes and in case the GPS technology malfunctions. The survey technique used in this week’s field activity is very basic and works in many different circumstances and conditions. In a pinch, this method could be used to map areas. In the case of this activity, the subject and area of interest were the trees of Putnam park.
Azimuth means the direction of a celestial object from the observer, expressed as the angular distance from the north or south point of the horizon to the point at which a vertical circle passing through the object intersects the horizon. In geography it refers to the horizontal angle or direction of a compass bearing.

Materials

·        Sonin Multi-Measure Combo Pro with target

·        Survey grade GPS

·        Azimuth survey compass

·        DBH measurement tape

The data collection was a collaborative effort between the whole class, but analysis of the data was done in the small groups. Group one was Noah, Andrew, and Amanda (myself).

The study area was on the Putnam Drive portion of east Putnam Park. Putnam Park is owned by the University of Wisconsin-Eau Claire and was designated a State Natural Area in 1976. Incorporating southern wet-mesic and northern dry-mesic forest, varied topography, bedrock exposures, seepage springs, and a variety of soil types all in close proximity, Putnam Park possesses many plant and animal habitats. The area the survey was taken is highlighted in the map below (Fig.1).  

Figure 1 East Putnam Park is a wet-mesic forest and is dominated by river birch, silver maple, hackberry, American basswood, red maple, and paper birch. Occasional tamarack and white cedar are found in the wettest portions, at the east end of the park.

Methods

Each small group collected the location and attribute data for 10 trees. The objective of the activity was to collect the following data:

·        Distance from origin

·        Azimuth

·        Tree Type

·        Diameter

The groups were spread out along Putnam drive so as to cover more of the area circled in Figure 1, and each group recorded data in notebooks for each of their 10 trees. An origin point was chosen on the Putnam park trail and its GPS coordinates were taken using a survey grade GPS. One group member would walk up to a tree that was within 20 meters of the origin point, identify the tree from sight characteristics, and then measure its diameter using the DBH tape. The target for the Sonin Multi-Measure Combo Pro was held up against the trunk of the tree and then another group member, standing on the origin point, would point the Sonin Multi-Measure Combo Pro at the target and record the distance from the origin to the tree. Also from the origin, the azimuth angle was measured by aligning the survey compass with the tree and then recording the bearing in degrees.

Other groups used the tape measure to measure the distance from their origin point to the trees. This would be just as effective but less time efficient, especially if trees were farther than 10 meters away. The laser distance finder we used, the Sonin Multi-Measure Combo Pro saved us a lot of time but I could see how it would be difficult to use if it didn’t include a target; it would be impossible to know with certainty that you were hitting the tree you were aiming at. We also were limited in areas of thick undergrowth to choose trees that we could get a straight shot at with the laser from the origin point.

Another difficulty encountered was tree identification. I am in the Trees and Shrubs taxonomy class at UWEC, so I proclaimed to my group members at the outset that I have excellent tree ID skills. Unfortunately, our data collection was on October 19^th and many of the trees we encountered had dropped all of their leaves, making identification difficult, especially with the added pressure of trying to live up to the self-appointed title of “Tree Identification Master.” Fortunately, Dr. Hupy was able to corroborate with us and the tree type attribute data was saved.

Results

After the survey, the class returned to the GIS lab and compiled all of the data into a Google Doc spreadsheet. The resulting document was downloaded and converted into an excel doc with numeric and text formatted columns, then opened in ArcMap 10.4.1 (Fig.2). 

Figure 2 This is the completed spreadsheet. The data was compiled by the small groups in the class and has been downloaded into ArcMap.

In ArcMap, the Bearing Distance to Line tool was used to display the data from the imported survey results table (Fig.3).  The tool plotted the distances from the point of origin as lines at the given angle on the map surface.  

Once the bearing distance to line tool was complete, the Feature Vertices to Points tool was used to convert end of the line opposite the point of origin to a point.  This tool is found in the same Features folder as the Bearing Distance to Line tool.  The purpose of this is to give the feature represented by the end of the line a determinable location on the map. In this case, the features at the end of the line were the trees we surveyed.

To create the map, I used aerial imagery of Eau Claire from the USGS as a basemap. I had to project it into the same coordinate system used when we collected our data. The final map represents the results of the distance-azimuth survey; the trees we recorded are now located on the map.