A Modern Approach to Geographic Problems

Introduction

            A modern approach to geographic problems has given people a very large and accessible toolset that can collect data very accurately. The advanced technology that has become at the disposal of geographers allows data to be collected in a variety of ways depending on the complexity of the information needed. Although advanced GPS units, and professional survey technology can collect incredibly accurate information in optimal conditions, adverse environments can skew and disrupt the signals used to collect the locational information. Often during field collection, the environment is not optimized for geographic signals, especially is the field of study is ecology (dense forest), hydrology (river canyons).

That is why, it is very important to collect spatially accurate data, without the use of advanced technology, in the event of technological failure or disruption. One technique to produce a simple survey is conducted using azimuth and distance. In a bind, techniques like this can make sure that a project is able to be completed, even if technology fails.

            For the purposes of this project, a location was chosen that suits the criteria of making the accuracy of data collection questionable. An area was selected at the base of the hill that surrounds Putnam Park. The steep incline of the hill, plus a dense overhead canopy of trees, greatly limits the accuracy of the data. Here, the azimuth-distance technique is implemented to collect data on the relative distances of tree species in Putnam Park.

Methods

Figure 1: Arial Image of Putnam park, centered on
source points of the study area.
Source of the image is GoogleMaps

On the day the data was collected, the weather was partly cloudy, mostly sunny, and 60 degrees F. The date was October 20^th.

Figure 2:Dr. Joe Hupy, explaining
the proper strategies to use the
laser distance finder and
azimuth collector. 

To implement the azimuth-distance technique, a source point is needed to base all consequent measurements off of. For this reason, this technique is implicitly geographic, meaning the data can only be relatively accurate. There are many sources of inaccuracy, just like the data can only be as accurate as the GPS unit used. For the technique to work, information on an azimuth reading, and distance measurement are needed. Using a laser distance finder, an individual looks down the ocular scope of the tool and points the locator arrows (like the scope of a rifle) at the tree that desired. The device that was used in class also had the capability to record azimuth data as well. Azimuth was collected in a similar format to the distance. The two data recordings were written into a notebook for the creation of a physical copy of the data. Along with azimuth and distance, several other ancillary categories of information were collected as well. To make the data point more valuable biologically, the tree species and diameter at breast height (DBH) were noted.

           

Results and Discussion

Once the distance and azimuth data was collected out in the field, the data was typed into Excel and imported into Esri ArcMap program. 

Figure 3: snapshot of the format
of the Excel spreadsheet used. Showing the
group classifications.

Using the Bearing Distance to Line command (data management, features) to import the table. Next, a Feature Vertices to Points tool (data management tool box, features tool set) is used to convert the data into points. Here is where things got a little messy. In the beginning the project, a source point was collected using a hand held GPS unit. This unit would under normal conditions be accurate to below a meter. But, with the dense canopy and steep incline of the hill, bounced several of the source points to a field a few miles away from the collection area. The correction to this error is a simple one, and that would be to use Google earth, and approximate the source point and use those coordinates to base our map off of. This does lead to a great variety of errors in spatial accuracy of the outcome product; for the purpose of the project it suits the need.