Field Survey

Introduction

The project for this week’s field exercise involves taking data points a highly accurate GPS receiver. The goal of the project is to produce a continuous surface that illustrates the elevation change in a small patch of grass on the University of Wisconsin Eau Claire campus mall. To facilitate the collection of data points in the small area, a survey grade GPS is utilized to collect data with accuracy close to a centimeter. Survey GPS offers an incredibly accurate source of data collection that can be utilized to carry out a field survey. Like any advanced technology, there are a few downsides. Survey grade GPs units are often very expensive, and can be rather cumbersome. For this exercise, the entire class had to take turns working in pairs to collect points, because the university only owns one Survey Grade GPS unit. For many, this was not the first time using the Survey GPS. The GPS was also used during the mapping of the Hadleyville Grave yard to gather data on the head stone locations.

Study Area

Figure 1: Blue represents the study Area on the
University of Wisconsin Eau Claire Campus

            The area of interest for this project is a small portion of grass that is located in the middle of the University of Wisconsin Eau Claire campus mall (Figure 1). The grass is surrounded by sidewalk, and contains 3 small trees, and 3 benches. The area is located a decent distance away from any large buildings, so there should be little expected distortion of the GPS signal, especially since the collection will be carried out on a Survey grade GPS. The grass area has a gentle curve in elevation, with the lower portions of the area located on the northern portion, and the higher elevation located in the southern portion.

Methods

            To collect the data, the survey grade GPS unit (receiver and tablet) was deployed to the location on campus. Since there was only one unit to offer, students in pairs of two took turns setting up the receiver in different locations and collecting data. The grass area was sampled using a random sampling method. The collection period was limited to 20 points.

Once data collection was complete, the text file is brought into the computer. The text is then converted to an excel file, along with a changing in the attribute headings for import into Esri's Arc maps. To display the now XY data, the Excel file is changed to table and added to the map using the ‘Display as XY data’ tool. The data point are projected into UTM Zone 15N, so fit the study area.

To complete the project goal, 5 different interpolations were completed to illustrate a continuous surface of the elevation of the grass area. The techniques used are Inverse Distance Weight (IDW), Kriging, Natural Neighbor, Spline, and a TIN surface.

Results

 IDW

Figure 2: IDW Interpolation

The equation for the Inverse Distance Weighted technique takes the cells closely surrounding a sample cell at a greater weight mathematically then cells further away from the sample cell. This method provided a very real to life representation of the slope of the grass area (Figure 2). The high point of the area is located fairly central, in the south east corner. The lowest points are located along the western border.

Kriging

Figure 3: Kriging

The Kriging method uses the height attribute to calculate the continuous surface. The data we collected, the Kriging did a rather poor job of capturing the slope and direction of the grass area (Figure 3). The interpolation over generalized the high point, and the relief in the northwest corner of the surface.

Natural Neighbor

Figure 4: Nearest Neighbor Interpolation

The Nearest Neighbor Technique uses a proportionality to take cells surrounding a sample point to be weighted differently. This technique is quite complex and involved a fair amount of modification (Figure 4). This is probably a reason behind the product being a complex representation of the actual surface of the grass area.

Spline

Figure 5: Spline Interpolation

The technique using spline interpolation uses a mathematical formula that creates a very smooth output by estimating the minimal curve of the elevation points. The spline interpolation did give a very smooth output, but over compensated and added a seeming bump in the slope in the west central side of the surface that is not present in the actual grass area (Figure 5). The spline technique also placed the high point to far in the southeastern corner then it presents in actuality. The technique would improve with further customization and more data points.

TIN

Figure 6: TIN 

This method produced the best result for the illustration of the elevation of the grass area. The TIN method did not capture the relief with complete accuracy (Figure 6). Like the Nearest Neighbor technique, the TIN tool is very programmable and the result could change quite a bit with more investment of manipulating the tool parameters.

Conclusions

The products of the interpolations from the data collected with the survey grade GPS did not accurately reflect the actual surface of the grass area that was surveyed. There could be several different reasons for the lack of strength in the final product. Initially, the sampling method was carried out in a disorganized fashion by students, which resulted with no information being captured in the central western, and north eastern portions of the grass area Even though the sampling method was ‘random’, some care could have been taken to assure that at least some points for every area is collected. Another issue encountered was the data was originally uploaded from the GPS unit text file as UTM ZONE 16N, not UTM 15N (faculty error). The error was rectified quickly and replaced with the correct coordinate system UTM 15N, and placed into a temporary drive to be accessed by students. However when the time when the interpolations were done the Temporary folder was empty. The only file left was the original upload, which was in UTM Zone 16N. This meant that a basemap was unable to be loaded behind the surface to provide context to the results. More organization at all levels could have increased the accuracy of the results.