Distance Azimuth Survey

Goal and Background:
In today's world, technology is involved in every aspect of life.  Most people wake up from an alarm on their phone, cook breakfast on an electric stove, follow directions from their phone on the easiest way to get to work, then use a computer or tablet to assist in whatever kind of work they do.  Technology has reached a point where it is involved in pretty much everything that happens in this world.  The world of GIS is no different, changing and advancing technology is changing the way GIS is used and keeps it growing everyday.  Surveying is closely related to GIS and is also constantly changing with technology.  The old ways of pulling tape measures is in the past and the era of GPS surveying is here.  GPS technology is used in almost all surveying applications.  It is accurate and reliable.  But how reliable is it? There is always a chance that technology can fail and it is important that people know what to do if the technology is not working.  The distance azimuth surveying technique is a way of mapping distributions based around a single known point.  This would not work for mapping the location of objects but only for mapping spatial distributions.  This report will go through the method used for mapping the distribution of trees in Putnam Park on the University of Wisconsin- Eau Claire.

Methods:


The distance azimuth survey method was used to collect tree data in Putnam Park.  Groups of 3 went into the field equipped with a Bad Elf GPS, Tape Measure, Compass, and Distance Finder. (figure 1)

Figure 1

  With equipment in hand, the next step was to go out into the field to collect data.  The task was to collect data about 10 trees surrounding a certain location.  Once the location was determined, the GPS was used to get the coordinates of that spot.  These coordinates were recorded in a field notebook.  Once the spot was determined, a tree was picked and information about that tree was taken.  The distance from the recording location in meters, tree circumference, tree type, and direction from the reference point.  To get this information the tools that were provided were utilized.  To get the distance to a tree, the distance laser was used.  This device (middle right tool in figure 1) uses a laser to get distances in meters.  It is shown in action in figure 2 below.

Figure 2

To get the circumference of a tree, a simple tape measure was used.  This is an age old method that had withstood the test of time. (Figure 3)

Figure 3

The direction was collected by using a compass.  The degree and quadrant were recorded to later find the direction of the tree.  For example, the compass went from 0-90 degrees for four quadrants, this led to a calculation that needed to be done to get degrees 0-360.  Lastly, the tree type was recorded by asking our professor (who has lots of tree knowledge!) and by using best judgement.  Once all the data was collected it was normalized into a table in order to be brought into ArcMap.  The direction was converted into a format of 0-360 and each tree was given the coordinate that it was collected from.  The normalized table is shown below (Figure 4)

Figure 4

The normalized table allows the user to bring it into Arc using the coordinates of each tree.  Once the table is brought into Arc, a few tools are ran on it to get it to show the trees, reference locations, and lines that depict the direction and distance of the trees from the reference point.  The Bearing Distance to Line tool takes the coordinates, direction, and distance to create lines that depict the distance and direction from the reference point to the trees.  This tool alone does not create points for the trees or the reference location.  The Feature Vertices to Points tool can create points from the beginning or end of the line created.  This tool was ran twice to get points for the trees and points for the reference location.  The tree points were created but data about tree type and circumference was lost during the process.  To get around this a table join was done to bring in that information about the trees.  Once the join was complete the process of collecting and bringing in tree data using the distance azimuth technique was complete!

Results:

When first inputting the data, one of our locations was tens of miles away from where we actually took data.  We realized this was caused by a data entry error of one of our coordinates.  Thankfully we had a picture of the GPS when it was actually showing that location.  Once this was solved our data appeared to be in the correct location.  One thing that seems wrong is that one of our trees appears to be on the other side of the footbridge when we didn't actually take data on any trees across the footbridge from the more westerly point.   This can be seen in the map below (Figure 5)

Figure 5

Besides just being able to see the distribution of trees, the circumference data made it possible to make a map showing the circumference of the trees. (Figure 6)

Figure 6

The last map is a map showing the various tree types collected.  (Figure 7)  This map uses a unique values symbology to show the different types of trees.  There was quite a variety of trees out there!  There are only two spots where trees are grouped together.  There is a group of White Ash trees in the more westerly group and a group of Black Ash in the more easterly group.  One mistake in the data was two different spellings for white ash which resulted in two different categories for white ash. 

Figure 7

Conclusion:


The distance azimuth survey worked relatively well for this lab.  It was no the most accurate but it have some upsides.  It does not require expensive tools, can be done quick, and will work when other more advanced technology fails.  On the other hand, having an accurate GPS would allow someone to walk right up to each tree and get the location of it to within a centimeter.  This kind of GPS is expensive but would allow this job to be done much quicker and accurately.  Not just would it be more accurate the data would be exact locations and not just spatial distributions. 

Creation of a Digital Elevation Surface using critical thinking skills and improvised survey techniques

Introduction:

This lab was designed to learn how to take a sample of x,y,z data and turn that into an elevation model in Arcmap.  This was done by creating a landscape in a sandbox and taking points to be mapped at a later time.  One important aspect of this lab was deciding on what kind of sampling method we were going to use.  Since we knew the box was going to be 114x114 cm long we decided for the sake of this project a systematic sampling technique was used.  This means we took Z-data every 6 cm in the X and Y direction.  This would give the best spatial representation of what we were making.  The other sampling techniques we could have used are random sample and stratified.  Random sample is done by generation random points and taking the elevation of those points.  This is a good way to collect data but we felt that using random sample in this situation might not yield the best results.  Stratified sampling is when points are taken from certain areas in the study area and are suppose to be representative of the whole.  We decided this technique would also not yield the best results.  The objective of this lab was to use the best technique to collect the most accurate data we could.  This data will be imported into Arc and turned into a surface model.

Figure 1

Methods:

We chose to use the systematic sampling technique because we felt that taking the Z-value every 6 cm in both directions would yield the most accurate data that was representative of the whole.  Systematic and random sample did not quite fit what we wanted to do.  We built our landscape in a sandbox created by Dr. J. Hupy located in an open area directly across Roosevelt Ave from Phillips Hall at the University of Wisconsin - Eau Claire.  To create the landscape we used very advanced shaping tools called our hands.  We were sure to include a ridge, hill, depression, valley and plain.  Once our landscape was created we created a grid using thumb tacks and string.  The thumbtacks were set into the edge of the sandbox every 6 cm.  String was then attached to the tacks and wrapped around all the tacks until the grid (figure 1) was created.  One corner was chosen to be our 0,0. To determine our 0 elevation we measured to the dirt at the bottom of the box to the string.  When taking our Z measurements we measured from the string to the sand and entered this number into the Excel table we created.  The Excel table was set up before the lab with all the X,Y points we were going to collect.  After all the Z measurements were taken a calculation was done to get the true elevation.  This was done by doing 14-measurement.  This gave us the height of the sand from the zero elevation we determined before collecting any data.  The data was entered into Excel by using the Excel app on an Iphone.  The sheet was created on a computer and exported to the phone.  This allowed for easy data entry in the field.  All in all the whole data collection process took about 2 hours.

Figure 2

Results/Discussion:

We collected a total of 401 points using the systematic method. Here are some statistics on our data:

Minimum: -.5 cm

Maximum: 23 cm

Mean: 8.7 cm

Standard Deviation: 4.43 cm

We felt that the systematic sampling method will somewhat accurately represent our sandbox.  We felt that this method maybe lacked some detail in the areas that have big changes in elevation but for the most part it represents the sandbox very well.  We found ourselves making up measurement for areas that we really wanted to emphasize like the "mountain peak" or the "plain".  We realized we were doing this and it was skewing our data so we stopped doing it.  We also had areas that the sand was going above our string so it was hard to get accurate measurements.  These are the areas we found ourselves emphasizing the Z values.   We realized we were doing this and went back to taking data the way we were.  

Conclusion:

Our sampling was a great example of systematic sampling.  We took samples from throughout the area of study.  This way of sampling is hard to do when looking at an area that is not 114 cm x 114 cm.  When the area is 10 miles big, is it much harder to do a systematic sample.  This is when a random sample or a stratified sample is useful.  Sampling is hard to do in a spatial situation because land and space is always changing and ever different.  A sample will never be truly representative of the whole.  We feel that the data we have collected will effectively represent our sandbox.  To get an even better sample the measurement could have been taken every 3 cm but that would take way too long!