Surveying of Point Features Using Dual Frequency GPS

Introduction
     This lab used survey grade GPS to take elevation points of a small knoll in low campus of the University of Wisconsin - Eau Claire. Survey grade GPS is one of the best ways to gather location data in the field because of the incredible accuracy. Depending on the signal that the GPS unit receives it can be accurate up to the centimeter. The down side to survey grade is the expense of the units. The University only has access to one unit, so the entire class had to take turns using the unit gathering one point at a time. Figure 1 below shows the location of knoll that was surveyed. The area

(Figure 1: A map of lower campus of the University of  Wisconsin - Eau Claire. The small area in red represents the study area that this lab took place and is located in the middle of the campus mall.)

 in the northern part of the study area is flat and the land progressively rises in elevation the further south it goes. There are four benches that stretch west to east through the middle of the study area. For the most part it is a very open area with only a few small trees. This vegetation would not have effected to signal to the GPS unit. Below in figure 1.1 is an photo of the study area. It is easy to see in the photo the drastic change in elevation from a lower elevation in the left (north) side of the photo the right (south) side of the photo.

(Figure 1.1: A photo of the study area take the day after the data was collected. The photo was taken by the author of this blog. The photo is a real world representation of the elevation change that can be used to compare with the interpolation maps that are found later in this blog.)

Methods
     As mentioned briefly above the entire class ventured out to campus mall to gather elevation data points of the study area. Do to technical issues only 20 points were possible to gather the day that the class had access to the survey GPS unit. The random sample method was used to gather the data points. Each student picked a spot of their choosing and after the data point was gathered, they passed if off to the next student. When the data was collected it uploaded in a text format to a desktop computer. The text was converted to an excel file using the Get Data From Text function in excel. The headings were changed to match the respective data and the table was transferred to ArcMaps. The Display as XY data function was to display the data points. The coordinate system that was used was NAD 1983 UTM Zone 15N. The data was then exported to a geodatabase so that it could be edited and interpolated. The same interpolation methods from the Sandbox Survey lab were used in this lab, those being Spline, Inverse Distance Weight (IDW), Kriging, Natural Neighbor, and TIN. The interpolations were cut down to fit the study area by using the raster to mask tool. The data was displayed using the stretched symbology and set to Bilinear Interpolation to help improve the smoothness of the elevation maps.

Results
     The first map that was created was the Spline interpolation. This interpolation uses a mathematical function to estimate the surface by minimizing surface curvature in the hopes of creating a smoother finished product. Figure 2 below shows a depiction of the spline interpolation of the study area. The areas that are red represent areas of higher elevation while the areas in green represent areas of lower elevation. Unfortunately the map is not a very good representation of the actual landscape the areas in the northern part of the study areas should be rather flat with a rather drastic rise in the middle, followed by a drastic decrease in the southern portion of the study area. This pattern holds true for all of the interpolations. The spline interpolation does an excellent job of creating a smooth transition from one elevation to another. This smoothness is most noticeable in the middle of the study area where the patch of high elevation dips down to a lower elevation before rising again to an island of higher elevation. The other interpolation methods, which focus on different aspects of interpolating, lack this smoothness.

(Figure 2: The Spline Interpolation of the study area)

     The second interpolation method is the Inverse Distance Weighted (IDW) Method. The IDW uses a method of interpolation where it takes a sample of values surrounding a cell and gives a higher weight to those values that are closer to that cell in the sample. This done for every cell in the finished raster. The IDW interpolation gave the best results for the study area due to the fact that the highest point isn't to the southern portion of the study area. The highest point is in the center of the map which best reflects the actual landscape. It even gives shows a slightly higher elevation if the middle eastern portion of the map which would represent the knoll. Figure 3 below shows a map of the IDW interpolation.

(Figure 3: The IDW interpolation of the study area)

     The third interpolation is the Kriging which uses the z-scores of the data points to create a surface model. While the Kriging looks very nice and really amplifies the hill shade effect. It did a rather poor job of recreating the study areas landscape. The Kriging method was unable to detect the knoll in the center of the study area. It also amplifies the mistakes that were made in the random sampling of the surface area. Figure 4 below shows a representation of the Kriging interpolation.

(Figure 4: The Kriging Interpolation of the Study Area)

     The fourth interpolation is the Natural Neighbor method. The Natural Neighbor method uses a subset of data points surrounding an area and weights them based on the proportion of surrounding areas. This method is by far the most difficult method to understand. It is unclear why the Natural Neighbor method did not completely fill the study area like the previous three methods. It potentially have been due to lack a sufficient amount of data points to properly interpolate the rest of the study area or an error could have occurred during the interpolation process. However, when examining the area that was interpolated it is easy to see that this method was not a successful as the IDW and Spline methods. It does show a fair representation of the western portion of the knoll but was completely unable to detect the eastern portion of the knoll. It also does a decent job of showing that the southern most portion of the study area has a slightly higher elevation than the northern most portion. Figure 5 below shows a representation of the Natural Neighbor interpolation method.

(Figure 5: The Natural Neighbor Interpolation of the Study Area)

     The final interpolation method is the Triangular Irregular Network, more commonly known as TIN. TINs can be created from feature classes that contain elevation data or from a raster that contains elevation data. The TIN ran into the same problem as the Natural Neighbor method where it was unable to fulling interpolate the study area. The same possibilities for why it failed to completely interpolate that were mentioned for the Natural Neighbor method apply for the TIN as well. The TIN provided a poor representation of the study area. It was completely unable to detect any raised elevation in the eastern portion of the study area. Figure 6 below shows a representation of the TIN interpolation.

(Figure 6: The TIN Interpolation of the Study Area)

Conclusion
     Overall none of the interpolations did a great job of representing the study area. This is mostly contributed to the lack of data points collected in the eastern middle portion of the study area. A few contributing factors to the lack of sufficient data points is first the that due to licencing issues that were outside of the classes control their was a limit to the amount of data that could be collected. This factor could have easily been worked around by choosing a different method other than random sampling. The class was rather unorganized in the collecting of the data points and no one noticed that there were no data points collected on the eastern middle edge of the map. This was area was a very important part of the landscape because it is where the eastern half of the knoll is located.

Sources
     For more information on the different methods of interpolation check out the two links below;
- For Spline, IDW, Kriging, and Natural Neighbor click here.
- For Triangular Irregular Network (TIN) click here.