Exercise 8 - Distance & Azimuth

Introduction

In this exercise we learned how to map out objects within a area using only distance and azimuth to find there locations. This method of mapping can be used as a substitute for when the collection of GPS coordinates is not possible. We split the class into groups of two and each picked an area to map out. We needed a 1/4 by 1 hectare area as our study area. This exercise will help us troubleshoot problems when we are faced with situations when we don't have access to the proper GPS equipment and need to map an area out using different methods.

Distance and azimuth is a method of mapping that using a benchmark location where the data collector stands and collects information of objects nearby without actually mapping those objects. To do this we use a laser distance finder which when pointed at an object can give use the distance away from us that object is (in meters) as well as the azimuth or direction that object is from us (in degrees). We then use ArcGIS to map out these objects given their distance and azimuth from the benchmark location.

A TruPulse Laser Distance Finder that we used to find the distance and azimuth. (fig 1)

Methods

The study area that we chose to map was a local park near campus called Randall Park. It is a small park the size of a city block with trees, benches, sidewalks, a statue and a swing-set. We chose this area because it meet the 1/4 by 1 hectare plot requirement and had a lot of open space for us to map out the area easily. This area was also used in previous classes which is where we got the idea.

We were going to first chose starting locations for use as a benchmark. We chose do have four benchmarks in each corner of the study area and we used the left corners of the sidewalk as these benchmarks. We first needed to collect the coordinates of these points and write them down. We chose to use a laptop with excel instead of a pen and paper in order to streamline the post processing needed later. We had a table with four attributes: point number, distance, azimuth and attribute information. Each point will also include the coordinates of the benchmark that corresponds to that object. We were to collect 100 points.

Out in the field we started by collecting the coordinates of the first benchmark location using a Trimble GPS unit. Once we had our starting location we set up our TruPulse laser unit up on a tripod directly above the benchmark and starting collecting information on each object. We picked trees, benches, lamp posts and other things and collected their distance and azimuth from our benchmark. The TruPulse worked by clicking the collect button when the cross-hairs in the eyepiece focused on the object we wanted and it would collect the distance in meters and azimuth in degrees and display the information above. We repeated this process for all 100 points and moved to three more benchmarks with a total of four benchmarks. Once we had finished we had a table with all of the points with their distance and azimuth from their respective benchmarks and their attribute information (what kind of object there were).

Next we needed to input the information into ArcGIS. To do this we first needed to set up a geodatabase for the project because the tool we ran needed to have a table that is in a geodatabase. We then put our excel spreadsheet table into this geodatabase. The first tool we ran Bearing Distance to Line tool (fig. 2) which could be found under Data Management and Features in ArcToolbox.

The Bearing Distance to Line tool which gives us the line features based on the benchmark locations and the distance/azimuth from those points. (fig. 2)

The image above shows the tool with the table as the input, the x field as our longitude of our benchmarks, the y field as our latitude of our benchmarks, distance field as our distance column and bearing field as our azimuth column. Below (fig. 3) shows the line features that were created from the table.

The output from the Bearing Distance to Line tool showing the line features created from the table. (fig. 3)

Next we needed to then create point features from the lines that we created in the previous step. To do this we use the Feature Vertices to Points tool (fig. 4) which can be found under Data Management and then Features in the ArcToolbox. 

Feature Vertices to Points tool used to create points from the line feature class from the previous step. (fig. 4)

We just input the line feature class from the previous step and the output (fig. 5) should give us points at the ends of all the lines. Below you can see the output and the point feature class that was created which is all of the objects that we collected data on in the field.

The line feature class including the point feature class created from it, showing where all of the objects we collected data from in the field. (fig. 5) 

Discussion

The data collection in the field went off without any major problems and we really didn't have any trouble with the equipment. There were some problems that we faced in the post processing however. The first problem was with the table and running the tools because we couldn't get the Bearing Distance to Line tool to work until I realized that it was not in a geodatabase which is required for these tools.

Next when we go the line feature class to show finally the benchmark points were off by a meter or two due the inaccuracy of the GPS unit we had been using to collect the coordinates for the benchmark locations. To fix this I used the satellite image that we used as a base map and found the coordinates of the corners of the sidewalk using the identify tool and moved the benchmarks to those locations. This solved the problems with the benchmark being off, but the accuracy of this method was still not great.

The final map (fig. 5) shows points that are off of the study area that we used and this can either be inaccuracy of our data collection (most likely) or shortcomings of the data collecting technique. These points are minimal however and the majority of the points seem pretty accurate after I moved the benchmarks to their proper locations.

Conclusion

This technique has many benefits such as when you are without a proper GPS that could map out these objects but you have a benchmark of something that you can find on a satellite image later or you already know what the coordinates are. This technique has been surpassed by technology like the total system GPS units and other GPS units that can map out object very accurately without having to take down distance and azimuth. This technique also isn't as accurate as other more recent methods however, when the time comes that you don't have access to the proper technology this is a very viable alternative.