Distance and Azimuth Surveys

Using electronic and non-electronic methods to conduct distance and azimuth surveys.

Introduction:

Our objective for this week was to work as part of a two person team using different methods to gather point data including distance and azimuth. I was partnered with Amy Bartel. The data we gathered was then loaded into a Geographic Information System (GIS) mapped and examined for accuracy. Methods to gather data included the use of low tech compass to gather azimuth data, and range finder to gather distance data; and a more advanced True Pulse laser range finder which gathers distance and azimuth data at the same time.

We gathered our data on two separate dates and at two separate locations (figure 1). The first location was on Monday, 18 February 2013 at the University of Wisconsin Eau Claire (UWEC), Eau Claire, Wisconsin. We gathered data on several objects and trees that are located between the south edge of the Phillips Science Building and the north edge Phillips parking lot. This location was convenient for the time available and the number of objects present to record data for. The data for the second location were gathered on Friday, 22 February 2013 at Randall Park, located two blocks north of Water Street in Eau Claire, Wisconsin, just blocks from the UWEC campus. For this portion of the project we were to gather data from an area of ¼ hectare plot and include at least 50 points. The location of this park made it a convenient walk from campus it also contained enough points for our project. The park, however, is larger than ¼ hectare. I used the measuring tool in Google Earth to get the size of the park; the distance from sidewalk to side walk is approximately 82 meters by 142 meters, just over one hectare. This was necessary to gather the required number of points.

Fig. 1. This image shows a portion of Eau Claire, WI. Randall
Park and the Phillips Science Building on the UWEC campus
have been highlighted along with the location of Water Street. 

Methods:

Preparation for this include going to the State cartographers website, (http://www.sco.wisc.edu/mapping-topics/magnetic-declination.html) where I found a link to the National Geophysical Data Center (NGDC) part of the National Oceanic and Atmospheric Administration (NOAA). The NGDC webpage has a calculator to compute your declination. You will need the latitude and longitude of your study location. If you do not know your latitude and longitude you can enter your zip code and click the get location button. The calculator gives your latitude and longitude; if you know them you can just enter them. Then you enter the correct date and click the compute declination button. The website gives you the magnetic declination for your area. In Eau Claire, Wisconsin the magnetic declination is 0°58’. We had to adjust our instruments for the declination.

We used a compass and sonic range finder to locate several point within a narrow strip between the Phillips Science Building and the Phillips parking lot. I took azimuth readings with a compass and operated the acoustic range finder while recording the data in a notebook. Amy walked to the points being recorded with the receiver. We used a small tree near the south-east corner of the building as our origin; this should be visible from aerial images. After recording the data for several points we created an excel spread sheet with the headings (attributes): point_number, X, Y, distance, azimuth, and notes (table 1). Point number was simply the 1^st, 2^nd, 3^rd… points we recorded. Distance and azimuth were what we measured. To get the X and Y data we used aerial imagery in ArcGIS.

point_number	x	y	distance	azimuth	notes
1	-91.499610	44.796492	9.69	107	blood
2	-91.499610	44.796492	7.93	140	cone
3	-91.499610	44.796492	19.06	136	tree
4	-91.499610	44.796492	22.8	135.5	tree
5	-91.499610	44.796492	14.85	151	phone
6	-91.499610	44.796492	10.79	265	tree
7	-91.499610	44.796492	19.74	271	tree
8	-91.499610	44.796492	34.67	281	tree
9	-91.499610	44.796492	44.05	285	tree
10	-91.499610	44.796492	54.58	287	building

 Table.1. This data from the first survey was entered into excel then imported into Arcmap for analysis.

After opening Arcmap we set the data frame projection to a Geographic Coordinate System (GCS) World Geodetic System (WGS) 84, this will allow us to work with latitude and longitude. Then we loaded a base map from World Imagery and zoomed in to our area of interest (AOI). In the image we located the tree we measured from and using the identify tool found the coordinates of the tree (-91.499610, 44.796492). These coordinates were put into the excel file as X and Y for all of the points since we gathered them all from the same location. We opened Arccatalog and created a new file geodatabase (GDB), we imported our excel file into the GDB then in Arcmap using Arccatalog input the data into the data frame. Using the toolbox in Arcmap we opened Data Management, and then Features. Within features is a tool called Bearing Distance to Line. We used this tool to import our data as a layer in the form of lines from the point of origin to all the points that were recorded. The lines are directionally based on our azimuth readings and the line length is based on our distance measures. Then we used another tool also located in Features called Feature Vertices to Points, again we are able to import the data as a layer however this layer is comprised of points. The points represent the data points that we measured in the field. We now have a map containing a base map layer and point and line layers representing our data (figure 2).

Fig.2. Survey one at Phillips Science Building. The points and lines are
not accurate because: 1, we could not get the accurate location of the tree
trunk we used as our origin; 2, as we measured we moved around the tree
causing our azimuth readings to be skewed.  

During our second data gathering excursion we went to Randall Park. We used a True Pulse 360 B laser range finder made by Laser Technologies Inc. to measure distance and azimuth. The park is quite large but it was necessary to record at least 50 data points for the project. Within the park there are a lot of obstructions, mainly trees. In order to gather all the needed points we used four separate origins located at the four corners of the park.  We began at the south-east corner of the park (figure 3); Amy operated the True Pulse while I recorded the data (figure 4). We had to locate a suitable point of origin that could be located on aerial images. We used the north-west corner of the yellow ‘rumble’ strip on the side walk, the strip contrasts well with the surrounding concrete. From this point of origin we gathered data on 12 points. We then moved to the south-west corner of the park (figure 5), here we used the base of an electric pole as our point of origin and recorded 12 more points. When we moved to the north-west corner of the park Amy and I switched jobs (figure 6), she recorded the data while I operated the range finder. At this corner we again used an existing electric pole as our point of origin and recorded 12 additional points. At the last corner, the north-east corner (figure 7), of the park we gathered the remaining points using the south-west corner of the yellow ‘rumble’ strip in the sidewalk just as we had on the south-east corner (figure 8).

Fig.3. View from the south-east corner of Randall Park.

Fig.4. Amy using the True Pulse to gather
distance and azimuth data from the south-
west corner of Randall Park.

Fig.5. View from the south-west corner of
Randall Park.

Fig.6. Stacy using the True Pulse to gather
distance and azimuth data from the north-
east corner of Randall Park.

Fig.7. View from the north-east corner of
Randall Park, Note the yellow rumble strip
in the foreground of the image.

Fig.8. A corner of the yellow rumble strips used to tie the survey
data to X,Y data gathered from aerial imagery in Arcmap.  

As in the first example we built an excel spreadsheet containing our data with headings: point, X, Y, distance, azimuth and name (table 2). Point was the point number while distance and azimuth were the data we had measured and name was the object at the point we recorded the data for. Object names included a statue, trees, lamp posts, benches, picnic tables and the posts at the corners of a pavilion. We used Arcmap, setting the data frame projection to GCS WGS 84 and located our four points of origin. The rumble strips were easily located because of their contrasting color. The base of the electric poles was determined by first finding the pole and then locating the shadow of the pole on the ground. We used the point where the pole met its shadow as our point of origin. We used the identify tool to get the X and Y coordinates for each of these four points and correctly apply them to the corresponding sets of points in the excel spreadsheet.

point	x	y	distance	azimuth	name
1	-91.505758	44.804196	8.4	292.0	tree
2	-91.505758	44.804196	21.0	318.8	tree
3	-91.505758	44.804196	41.0	327.4	tree
4	-91.505758	44.804196	7.7	0.9	tree
5	-91.505758	44.804196	27.6	350.4	tree
6	-91.505758	44.804196	44.0	336.4	table
7	-91.505758	44.804196	22.3	339.1	tree
8	-91.505758	44.804196	38.5	310.7	lamp
9	-91.505758	44.804196	69.0	305.4	lamp
10	-91.505758	44.804196	91.9	305.0	bench
11	-91.505758	44.804196	86.0	308.2	bench
12	-91.507619	44.804213	127.0	307.0	lamp
13	-91.507619	44.804213	8.9	82.0	tree
14	-91.507619	44.804213	19.0	76.9	tree
15	-91.507619	44.804213	76.0	74.3	statue
16	-91.507619	44.804213	11.0	39.7	tree
17	-91.507619	44.804213	19.8	48.2	tree
18	-91.507619	44.804213	38.5	56.9	lamp
19	-91.507619	44.804213	92.9	62.9	bench
20	-91.507619	44.804213	11.1	38.9	tree
21	-91.507619	44.804213	35.0	29.7	bench
22	-91.507619	44.804213	17.9	18.5	tree
23	-91.507619	44.804213	78.3	57.8	bench
24	-91.507621	44.804952	76.0	84.5	tree
25	-91.507621	44.804952	46.2	128.2	lamp
26	-91.507621	44.804952	26.9	136.0	tree
27	-91.507621	44.804952	10.1	131.5	tree
28	-91.507621	44.804952	24.1	151.1	tree
29	-91.507621	44.804952	92.0	126.1	statue
30	-91.507621	44.804952	83.0	126.2	bench
31	-91.507621	44.804952	53.9	106.6	tree
32	-91.507621	44.804952	13.9	98.4	tree
33	-91.507621	44.804952	39.9	94.1	tree
34	-91.507621	44.804952	53.3	162.1	tree
35	-91.507621	44.804952	84.1	112.4	pavilion
36	-91.507621	44.804952	84.0	113.6	pavilion
37	-91.505759	44.804971	93.9	233.2	statue
38	-91.505759	44.804971	38.0	231.1	lamp
39	-91.505759	44.804971	75.4	243.4	pavilion
40	-91.505759	44.804971	74.5	244.0	pavilion
41	-91.505759	44.804971	36.0	219.8	tree
42	-91.505759	44.804971	11.0	213.8	tree
43	-91.505759	44.804971	26.1	219.4	tree
44	-91.505759	44.804971	22.6	238.7	tree
45	-91.505759	44.804971	42.2	243.3	tree
46	-91.505759	44.804971	32.7	248.5	tree
47	-91.505759	44.804971	48.0	251.9	tree
48	-91.505759	44.804971	14.8	253.9	tree
49	-91.505759	44.804971	23.6	258.9	tree
50	-91.505759	44.804971	28.4	266.3	tree

Table.2. This data from the second survey was used to map 

distance and azimuth lines and object points in Arcmap.

We returned to the GDB in Arccatalog that we set up earlier and imported the new spreadsheet. We opened the environments and set the work space defaults to our GDB. This makes saving and using tools much easier because we didn't have to look for our GDB each time. Then in ARCmap, in the same data frame we already have open and projected we use the Bearing Distance to Line tool located in the toolbox, Data Management, and Features to import the data as a layer showing the direction and distance from the four points of origin to each of the fifty points measured. In the tool we selected our excel file as the input, saved the output to our GDB and selected each of the appropriate headings for X, Y, distance and azimuth and clicked OK to run the tool. When it was finished we right clicked on the line symbol for the data layer and selected a good contrasting color for the symbol (sulfur yellow). While analyzing the data we were interested in the distance to each object. To add distance information about each of the lines we right clicked on the layer name and selected label features, this added the distance measure to each of the lines. Then we right clicked the layer name and selected properties then selected labels; first we selected label all features the same and selected the distance field, left the font size at 8, then we set the color of the labels to contrast with the background (figures 9,10)(tourmaline green). Because of the large number of lines in a small area it gets quite messy when viewed with labels at a smaller scale so we set the scale range to not show the labels beyond a scale of 1:1,000(figures 11,12).

Fig.9. The line data that was generated using the bearing distance
to line tool. The lines have been labeled with distances (meters).

Fig.10. The line data that was generated using the bearing distance
to line tool. Data is shown over base map layer.
The lines have been labeled with distances (meters).

Fig.11. Line and point data from survey 2 shown over a
base map layer scaled at 1:1000 with labels visible.

Fig.12. Line and point data from survey 2 shown over a
base map layer scaled at 1:1250 with no labels visible.

In order to display the points we gathered data on we used the Feature Vertices to Points tool located in Arctoolbox under Data management and Features. In the tool we selected the distance line layer as the input and saved the output in our GDB, we ran the tool. When the point’s layer appeared on the screen it was not very visible, so we right clicked on the point symbol and selected a good contrasting color (Tourmaline green). We were also interested in what the point was. To show this information we right clicked on the point layer and selected properties. In the properties tab we selected labels, we selected label all features the same, selected the name field for the labels and left the font size at 8. Again the density of the points with labels viewed at small scales gets messy so we set the labels to not show beyond a scale of 1:1,000. We selected a good contrasting color for the labels (Medium apple) and clicked OK to close. We noticed an issue with this tool; however, it also puts points on the origin ends of the lines and gives them a default label (figure 13). To remove these point and labels we opened the editor toolbar and selected start editing. Using the selector tool we selected each of the points at the origins and deleted them which automatically deleted the corresponding labels, we saved our edits and selected stop editing (figure 14). Now we were able to view the data we gathered layered over aerial imagery and analyze the data for accuracy and to see where our methods may need improvement (figure 15).

Fig.13. map of Randall Park with point and line layers, note the
points and labels at the origin vertices. 

Fig.14. The point data that was generated using the feature
vertices to points tool. The points have been labeled with the
object names.

Fig.15. Labeled points viewed over a base map layer of
Randall Park.

Discussion:

For the first portion of this project we measures a small set of objects and used a tree for our point of origin. This was more or less a practice run to become familiar with the equipment and to improve our methods. When we were gathering the X, Y data from the base map we found we had to be precise out as many significant digits as were available. When we first attempted to use abbreviated coordinates we found our plotted data about a half a mile south of where we gathered it from, by using all 6 decimal places we were able to get the data reasonably close to our study area. Another problem we ran into was using a tree as our point of origin. First, when gathering the data we rotated around the tree which progressively pushed our data off their marks. Second, when we tried to locate the exact point of the trunk it was obscured by the branches overhead so we had to use our best judgment of its placement.

During the second portion of our project we had to overcome the size of the study area, the best way to do that was to use multiple points of origin. The west points in the park were simple enough using the electric poles as our origins. To locate the base of these in the aerial image we found the pole but the base of it was difficult to see so we used the shadow of the pole to aid us. We used the point where the shadow ended or appeared to intersect the pole as our origin. Many of these points seem fairly accurate; however there were 2 points that were not. At the south-west corner we have one point that shoots off to the north-west at a considerable distance, this should have been a lamp located within the park (figure 16). There are several reasons that could have contributed to this including; the instrument could have had an error, the instrument may have been misread or the numbers may not have been communicated or written down accurately. The second point that is severely misaligned is at the north-west corner. There is a tree that should be within the confines of the sidewalk (figure 17). Again any of the same problems may have occurred and both of these points should be redone.

Fig.16. This image shows the incorrect line and point for a lamp
 post shot from the south-east corner of  Randall Park.

Fig.17. View of the north-west corner of Randall Park
and the incorrect line and point data for a tree.

On the east end of the park we used the rumble strips in the sidewalk as our origins. We carefully documented which corner of the strip was used at each corner of the park. These strips were easy enough to find in the aerial images; however, they were small enough and without shadows to corroborate their position it was difficult to get a precise reading on the corners. When we imported our data on this end of the park they are less accurate than the west end. I had commented that we should use the fire hydrants as our origins (figure 18). We decided against this because they were across the street and nearly covered in snow (figure 19). Doing this again, I would use the hydrants.

Fig.18. This fire hydrant would have made a better origin. Do to
its color and size it should have been easily recognizable in
aerial images.

Fig.19. This fire hydrant was located across the street from the
north-east corner of Randall Park. the rumble strip in the
foreground was used as the origin.

Comparing the tools used in the first part of this project to the second I believe they are equally accurate. The compass method takes more time to get precise azimuth readings while the True Pulse records both distance and azimuth at the same time; however it is not without fault. It seemed to me that it was more difficult to record data on a small object such as a lamp post at a distance with the True Pulse. The problem was holding the instrument steady long enough to get a reading. In a pinch it is quite feasible to accurately gather data with the more primitive methods. They may not fail when technology does.          

The process of adding labels to the layers in the map took a couple of extra minutes but it  gave us the ability to see the values associated with each of the points and lines. 

Conclusion:

This project allowed us to use methods that ranged from the more primitive to the more technological. Knowledge of each method is a great advantage if one method fails or another unforeseen circumstance arises. Instruments such as a compass and a tape measure do not have batteries which do not always like the environment they are being used in. Also, technology itself can simply fail without a fix in the field.

The group work is always a test in coordinating the schedules of the participants and working around each other’s prearranged activities and other priorities. This is a great skill to carry forward into the workplace as you may be coordinating projects with many people and organizations. 

This project was also an experience in dealing with changing weather conditions. Much of the week the weather was dry but cold, when we did our survey on Friday, 22 February 2013, we were dealing with several inches of new snow and light snow was falling (figure 20).

Fig.20. Several inches of new snow had fallen
before we did our survey. We did not have a ruler
with to measure the snow depth but this notebook is
about 5.25 inches to the top of the spirals.