Lab 2: Conducting a Distance Azimuth Survey

Introduction

On September 19, 2017 my field partner, Anthony Ducosin, and I went out to Putnam Park near the University of Wisconsin Campus and conducted a Tree Survey using Distance and Azimuth. The purpose of this lab is to find the location, type, and circumference of several trees in Putnam Park to practice the Distance Azimuth Method using particular equipment. 

Methods

Data Collection

Anthony and I established two field sites from which we collected data about 10 nearby trees. Data collected included location relative to the field site using distance and azimuth, diameter, and tree type.

Using the GPS Unit shown in Figure 1, we established the site coordinates. It is from these points that all geographic data was collected. The location of the field sites, Site 1 and Site 2, are shown in Map 1. Their coordinates are (44.796047N, 91.501241E) and (44.795523N, 91.500121E), respectively.

Figure 1: GPS Unit

Map 1: Site Locations

We used distance and azimuth to find the positions of 10 trees relative to our site locations. Distance was measured with the laser distance finder shown in Figure 2. Azimuth was measured with a Geographer's Compass pictured in Figure 3. It should be noted that values of the compass are measured by an angle, 0-90 degrees, and a cardinal direction NW, NW, SW, or SE. The values are not in degrees 0-360, and the cardinal direction for each measurement is a necessary piece of data to collect.

Figure 2: Laser Distance Finder

Figure 3: Geographer's Compass

We also collected the diameter of each tree by finding its circumference in the field using the tape measure pictured in figure 4. The value collected was later converted to diameter in excel.

Figure 4: Tape Measure

Neither Anthony nor myself are biologists, so collecting information about they type of tree was rather challenging. We were confident in the fact that all trees measured were deciduous, but we only recorded a data more specific that that when professor Hupy walked by and told us about the tree we were identifying. 

In the field, all data was collected by hand in a notebook. We created a spreadsheet using that data once we got to the lab. The Excel spreadsheet is pictured in Figure 5. When entering longitude, be sure to include a negative sign reflecting that the value is east of the prime meridian, unless you want the data to show up in Mongolia. Azimuth is converted from a 0-90 degree value and direction designation to a 0-360 degree value. The diameter column is populated by Diameter = Circumference / Pi. 

Figure 5: Excel Data Sheet

Map Production

The data from the spreadsheet in Figure 5 was used to make a series of maps. I brought the data into a new file geodatabase as a table. Using the search window, I found Bearing Distance to Line tool, see Figure 6 for more details about this tool. See table 1 for a list of values I entered when using this tool. Any field not explicitly noted I left as default. The results of using this tool is shown in Map 2.

Figure 6: Bearing Distance to Line Tool

Creates a new feature class containing geodetic line features constructed based on the values in an x-coordinate field, y-coordinate field, bearing field, and distance field of a table (Source 1).

Table 1: Bearing Distance to Line Tool
In_Table	Excel table (Figure 5)
Out_Featureclass	Tree_dist_as
X_field	Longitude
Y_field	Latitude
Distance_field	Distance
Distance_units	Meters
Bearing_field	Azimuth
Bearing_units	Degrees

Map 2: Result of Bearing Distance to Line Tool

Next, I used the search window to find the Feature Vertices to Points tool, which creates a point feature class "containing points generated from specified vertices or locations of the input features" (Source 2). For Point_Location, I used "all" which is incorrect. I should have used "end" so that the Site position was not included in the final feature class. The result of this tool is shown in Map 3.

Map 3: Result of Feature Vertices to Points Tool

The final step was to symbolize the data so that the tree locations represented tree type and trunk circumference. The results of this are Map 4 and Map 5.

Results

Map 4: Data classified by type of tree

Map 5: Data classified by circumference of tree

Discussion

The Point-Quarter sampling method (Source 3) collects data in a way very similar to how Anthony and I collected our data for this lab. However, we collected the specific geographic location of each of 10 trees by using azimuth and distance. We did not discriminate against quadrant when selecting trees. The method described in Source three picks the 1 closest tree in each of 4 directions to collect data on and does not measure geographic position. Using our method would allow biologists to map out sections of forests, collect more data, and select the four closest trees to measure, rather than the 1 closest in each of 4 directions.

The method described in this lab is excellent for collecting data about features that do not move. This method measuring the position of an object with reference to another, using a wide range of tools, has been used for ages by land surveyors, geographers, and astronomers alike.

With the advent of GPS units, which are increasingly precise and accurate, GPS is the favored method of collecting geospatial data. However, when that technology fails, for whatever reason, collecting data with distance and azimuth is still an option. The distance-azimuth method laid the groundwork for GPS, which uses control points - survey markers measured off one-another and the stars.

Sources

1. http://pro.arcgis.com/en/pro-app/tool-reference/data-management/bearing-distance-to-line.htm
2. http://pro.arcgis.com/en/pro-app/tool-reference/data-management/feature-vertices-to-points.htm
3. http://www.saddleback.edu/faculty/steh/bio3afolder/Point-Quarter%20Lab.pdf

Lab 1: Sand Box Lab

Lab 1: Sand Box Lab

Introduction

The Sand Box Lab is an exercise in improvised surveying. Each group is given a sand box, approximately 1 square meter in size, into which we sculpted the terrain. With limited equipment we then surveyed the terrain, collecting data that will later be used to create a digital elevation model. On Wednesday September 14, 2017 my group, group 1, conducted the exercise in a sand box located approximately 400ft west of Phillips Hall and 300ft south of Schneider Hall on Roosevelt Avenue, Eau Claire Wisconsin.

Methods

Study Area

The study area was a sandbox approximately 1 square meter in size located on the UW Eau Claire campus approximately 400ft west of Phillips Hall and 300ft south of Schneider Hall on Roosevelt Avenue, Eau Claire Wisconsin.

Sculpting the Terrain

The first step was to sculpt a terrain that included at least some hills, valleys, ridges, and plains. We decided to honor our professor, Joe Hupy, by sculpting his name, as seen in Figure 1.

Figure 1: Terrain sculpture of "JOE"

Sampling

Sampling is a method of gathering data from a few locations that can fairly accurately represent the larger area, since measuring elevation by had across the whole are is time consuming and impracticable. There are three sampling techniques, random sampling, systematic sampling, and stratified sampling. Our group employed a systematic sampling technique, where we created a system to decide what points to measure.

Elevation varies greatly across the middle portion of the sand box but is relatively flat along the top and bottom. Therefore we wanted to a denser sampling of the middle portion than of the top or bottom. Using string held across the sandbox by pushpins, we created a grid system, as shown in Figure 2.

Figure 2: Using a ruler and pushpins to set up a grid system

With the origin, (0,0) placed in the bottom right corner of the frame, we began to constructed an X,Y coordinate system with 5cm intervals. Due to limited amounts of string, and knowing that the sample size was going to be different for different parts of the sandbox, it is not a perfect grid. Along the X-axis, the length of the sandbox is sectioned of by 5cm intervals. Along the Y-axis, 25cm to 90cm is sectioned off by 5cm intervals. In this section we took 1 measurement within each 5cm^2 box. In the lower section, everything below Y=25cm, the grid was larger, 25cm x 25cm, with the exception of the leftmost square, which was 15cm x 25cm. One measurement was collected in each section. In the upper section, everything above, Y=90cm, grid sizes were 25cm x 20cm with the leftmost square 15cm x 20cm. See Figure 3 to see completed grid.

Figure 3: Completed Grid

Measurement Technique

The height of the string grid was our arbitrary zero. Since the strings trending North-South were placed on top of those trending East-West and exhibited significantly less sagging, their height was our standard. All measurements were collected as centimeters below zero with a ruler. In the middle section of the grid, measurements were collected in the north-eastern corner of each box. In the upper and lower sections, measurements were collected in the middle of each box. 

Figure 4: Measurement Technique. All measurements in the middle section were collected at the north-eastern corner of the boxes, as exhibited in this picture. The camera is facing west. 

Discussion

The data collected is presented in Figure 5: Data Table. In a later lab this data will be used to create a Digital Elevation Model of the terrain. At that point the data will be analyzed for its accuracy in modeling the terrain. 

Figure 5: Data Table

Error Sources

There were several potentials for human error in conducting this study. The string grid lines exhibited more sagging in the center of the sandbox than at the edges, so the elevation values may be smaller at the center data points. If pressed too hard, the ruler sometimes caused a depression in the sand, artificially increasing elevation values.

Conclusion

For now the data and collection methods seem appropriate. The data will be analyzed further in a later lab, and then I'll be able to better determine if the outcome is valid and our methods hold up.