Digital Elevation Survey-Revisited

Introduction
This week, we revisited our "Digital Elevation Survey" activity.  The goal was to refine our survey area and tweak our methods for the best representation of our terrain.  Before we could refine our data, we imported our X and Y coordinates into ArcMap.  In ArcMap we used the IDW, Kriging, Natural Neighbor and Spline Raster Interpolation tools to visualize the terrain.  We also used ArcScene as a 3D interpretation.  Below is an image of the Spline technique in ArcScene (Figure 1).  This method was the best representation of our terrain.  The spline tool uses a 2D "minimum curvature technique" to interpolate the raster surface.  This tool differs from the other because the resulting surface passes through the input points exactly and creates a smoothing effect.

Figure 1-Spline Interpolation of the first digital elevation survey

Methods
Once we had visualized our survey, we came together as a group to discuss ways to better our research.  We decided to tie string at 10 mm intervals on the Y axis so our coordinates would be more precise (Figure 4).  The next step was to replicate the activity from the previous week.  Because it had snowed, we had to recreate our terrain (Figure 2).  After the terrain was recreated, we tied the string at 10 mm intervals on the Y axis (Figure 2 and 3).

Figure 2-Creating the terrain

Figure 3-Tying string at 10 mm intervals

Figure 4- 10 mm intervals on the Y Axis

  

After the string was tied, we began to collect our survey data in the same process as before.  For most of the survey, we took X, Y and Z coordinates at 5 mm intervals.  This would give us more data points and therefore a more precise digital elevation survey.  Like the previous time, we used a mobile X axis (Figure 5) to measure from X, but because we had string tied at every 10 mm on the Y axis, the measurements were more precise.  In areas where the terrain was flat, we took measurements every 10 mm (Figure 7).  We used a Microsoft Excel spreadsheet to record the data points.

Figure 5- Mobile X Axis

Figure 6-Laurel and Phil collecting data points

Figure 7- Meter stick with mobile X Axis to collect data

Once the entire planter box (112.5 cm by 224 cm) was surveyed, we converted the Excel spreadsheet into a digital copy.  We then imported this spreadsheet into ArcMap.  Again, we used IDW, Kriging, Natural Neighbors and Spline Raster Interpolation tools to visualize our data.  In the first attempt at this activity Spline Interpolation resulted in the best terrain model, this technique produced the best visual as well in the second attempt (Figure 8).

Figure 8- Spline Interpolation for the second survey

The second time we conducted the survey elevation features were more pronounced.  This was most likely because we took more data points and the data points were more precise.  The features were not vague representations the second time, they were replicated extremely similar to the real world features in our planter box.

Discussion
By revisiting our activity, we were able to use our previous data and outcomes to produce a better product the second time around.  We made two major changes between the first survey and the second.  We collected more data points and used string to collect more precise Y locations.  There is an obvious difference between the first Spline interpolation visual and the second.  The changes we made allowed for a more detailed representation of our digital elevation survey.  To make our survey even better, we could have used a measurement tool other than a meter stick.  A meter stick is rather wide, so our measurements would only account for the general elevation of a point.  If we used a thinner tool, the data points would be represented more accurately.  We were somewhat restrained from using this tool because we were instructed to use a meter stick.  Also, I didn't think of this until we were about halfway done collected the data for the second survey.

Conclusion
This activity really advanced my ability in the Geographic realm.  It is easy to sit at a computer and import XY coordinates and project them into a visual display, but it is harder to come up with your own data collection technique and import that into ArcMap to produce a visualization of our survey data.  This activity pushed me to think more critically about data collection and the importance it has on the desired outcome.  My group performed very well together.  We were able to account for each other's weaknesses and use our strengths to come up with the best possible survey.