Distance Azimuth Survey Method Activity

Introduction

This activity was conducted in order to learn how to conduct a survey using the concepts of distance and azimuth (direction).  These methods can be used to fall back on if other more advanced technology is not being reliable.  It works in many different circumstances and conditions and is quite simple, so knowing these methods could end up becoming useful in the field.

The objective of this activity was to work with a partner to collect spatial data in the field with a partner and create a map of the location and other attributes of the chosen trees.

Definitions:
Distance - the amount of space between two objects.

Azimuth - the horizontal angle or direction of a compass bearing.

Circumference - the girth or distance around an object.

Diameter - the distance of a straight line passing through the center of a circle to each side of the circle.

Methods

For this activity the two partners chose two sites along Putnam Drive on UW-Eau Claire's campus from which to locate, measure, and collect data about ten trees nearby.  The site map in Figure 1 shows the location of my group's two chosen sites.  The conditions of the day were sunny, humid, and warm, with most of Putnam Drive being shaded by the forest.

Figure 1: This site map shows the location of each site on UW-Eau Claire's lower campus along Putnam Drive.  

The tools used included: measuring tape, compass, GPS Pro device, distance laser, field notebook, and camera.  These items are shown in Figure 2.  The measuring tape was used to measure the circumference of the tree.  The compass was used to find the azimuth from the origin point to each chosen tree.  The GPS Pro device was used to mark the location of the origin.  The distance laser was used to measure the exact distance from the origin point to each chosen tree.  All the information collected was organized and recorded in the field notebook, and the camera took the pictures for the report.

Figure 2.01: The measuring tape was used to measure circumference of the trees.

Figure 2.02: The compass was used to find the azimuth.  

Figure 2.03: The GPS Pro unit was used to mark the point of origin.  

Figure 2.04: The distance laser was used to measure the exact distance from the point of origin to each tree.  

Site 1 was chosen by setting down the leather compass case and recording the coordinates of that spot with the GPS Pro.  This can be seen in Figure 3.  One student would stand directly above that spot and hold the distance laser to his/her chest and point the laser at the desired tree to get the distance to the tree.  That same person would stay in the same position and use the compass to find the azimuth from that spot to the same tree.  The other student would at the same time go to each tree and use the measuring tape to find the circumference of the tree and also try to determine the type/species of the tree if possible.  All this information was recorded in the field notebook.  This same method was used at Site 2.  One tip for recording trees is to go in order in one direction, say from left to right.  This way if any attributes are missed you can easily find which tree you need to remeasure or what have you.

Figure 3: Morgan is using the GPS Pro to find the location of the point of origin at Site 1.  

Once all the tree data was collected it was then manually put into an excel sheet, as shown in Figure 4.  The circumference was then used to find the diameter of each tree using a simple formula.  Also since the angles that the compass gave were divided into quadrants, each azimuth must be calculated into the corresponding angle within 360 degrees, with North being 0 degrees.

Figure 4: This image shows the excel sheet that was created to organize the data collected in the field.  

Next, a database was created using ArcCatalog as a place to import the data in the excel sheet as a table and to store all the results.  Each point was then brought onto the map by right clicking on the table in the table of contents, and setting the x field as longitude and the y field as the latitude.  The geographic coordinate system should be set as GCS_WGS_1984.  This can be seen in Figure 5.

Figure 5: This is the step to display the XY data as points on the map.  

Next the Feature Vertices To Points command was used to create a feature class containing the location of the trees as points.  The search bar was used to find this tool, as shown in Figure 6.  A basemap was also added to give context to the location of the points and the sites.

Figure 6: The Search bar was used to locate and use the Feature Vertices to Points tool. 

Then the Bearing Distance To Line command was used to connect the points of each tree to the origin point with a line, which was also found using the Search bar as seen in Figure 7.

Figure 7: The Search bar was used to locate and use the Bearing Distance to Line tool.  

Now that the data has been mapped it can be made into a more aesthetically pleasing map to be used to analyze the accuracy of the methods used in this activity.

Results

Figure 8 shows the final map created in ArcMap.

Figure 8: This is the final map result of the collected data in the field.  

This map shows that the point of origin for Site 1 was not accurate.  Each site has on the actual dirt road, and Site 1 in the map shows it being in the woods, off the road.  Site 2 seems to be accurate.

Because all the trees chosen in this activity were deciduous and the exact species of every tree was not known, the type was not specified in this map.

The distance and angle of each tree's azimuth in reference to the point of origin seems quite accurate however, and representing them by size was easy to display using ArcMap.

Conclusion

The azimuth distance survey methods incorporated in this activity were useful in locating features around a given point of origin.  The precision of the GPS unit will however affect the accuracy of the final product when brought into ArcMap.  These basic methods could be used in the field if the more advanced technology fails. 

Sandbox Activity

Introduction
The objective of this lab was to be introduced to sampling by using improvised surveying techniques.

Sampling definition: a shortcut method for investigating a whole population in which data is collected from a small portion of the population in order to get an idea of what the whole population is like.  Sampling from a spatial perspective means that data is collected in certain spots throughout a given area to get a better understanding of the area.  It can be done to create topographic maps, analyze population data, or observe various other types of information. 

Sampling techniques:
-Random: data is collected randomly.
-Systematic: data is collected systematically/evenly.
-Stratified: data is collected systematically, but data is collected more extensively in specified areas. 

Methods
Figure 1 shows the location of the sandbox site, on the eastern side of the Phillips science building on lower campus.  

Figure 1: This map shows the sandbox site location on UW - Eau Claire's lower campus.

The materials for this activity included: a sandbox (approx. 4ft x 4ft), tacs, string, a measuring stick, a field notebook, and a camera. 

Our group chose to use a stratified sampling technique.  We did this in order to get a more detailed survey of the area of interest.  It was a systematic approach, and we just decided to sample the central area of the sandbox with more data collection points.

Using the top of the sandbox border, we set up a grid system using the tacs and string, as seen in Figure 2.  The vertical strings were spaced every 5cm, as well as the horizontal strings in the central area of the sandbox in order to collect the most data points where the landscape had more features/variation. 

Figure 2: This image shows the strings being strung between the tacs that were distributed every 5cm on the top of the borders of the sandbox. 

Our "sea level" was at the level where the grid was strung across the top of the sandbox borders, so all of our measurements were measured in distance below "sea level". Using the measuring stick to measure at the intersection of each string as a point on the grid, each x/y coordinate was measured with a z record as elevation at that point.  This process is shown in Figure 3.

Figure 3: This image shows how the measuring stick was used to measure the distance from the "sea level" to the landscape floor at that specific point. 

One person acted at the measuring stick holder, another read each measurement aloud, and the third person recorded these measurements in a field notebook, which is shown in Figure 4.  This data would then be used later to create a digital elevation map. 

Figure 4: This image shows how each measurement was recorded into a field notebook. 

Results
Figure 5 displays the final result of this activity thus far.  Each point was entered manually into an excel spreadsheet. 

Figure 5: This image contains the final result of this activity at this time: all the measurements within an excel spreadsheet.

Once the group chose the sampling technique, that method did not change throughout the data collection process in order to not have inconsistent data.  This method worked quite well in collecting the data, and the resulting spreadsheet contains fairly consistent data.  One concern that was discovered was that the strings may not have been tight enough to remain perfectly level throughout the grid, so each string was re-tightened to account for this.  Other than that, the preparation for our data collection made the process quite smooth.  As the group proceeded, each member became faster and more efficient until all the measurements were collected.  Roles were even swapped in order to relieve each other of the more strenuous jobs. 

Conclusion
The sampling conducted in this activity was a good representation of the ways spatial sampling can be done on a much larger physical scale and with much larger data and more variables.  This sampling method allowed us to virtually map the sandbox quite accurately with just simple tools and the collection of a couple hundred data points.  This activity is just a small version of how data can be collected for a very large area.  When satellites or other aircraft capture images or lidar data of the Earth which are then used to analyze that area, they are also collecting specific information for specific points on the Earth as we did in the sandbox.  I believe our sampling did an adequate job in surveying the landscape within the sandbox.