Field Activity: Creation of a Digital Elevation Surface

Introduction and Background
In this introductory lab to the processes of field methods, groups of students were tasked with developing a model terrain in sandboxes located on the University of Wisconsin, Eau Claire campus grounds. The size of the sandboxes were slightly greater than on square meter, and the constructed terrain was required to have a minimum of the listed terrain features: ridge, hill, depression, valley, plain. Afterwards, the group was tasked with sampling the terrain, using any on of a variety of sampling techniques.
Sampling, from a spatial perspective, means gathering a number of coordinate points (x,y,z) in order to generate a terrain map of a larger area (rgs.org). Rather than continuously mapping an entire area, sampling is used to provide a general picture by using the collected data points to generate a terrain estimate for the desired area. This is done because it is often not practical in terms of time, energy, manpower, and monetary expense to continuously map an entire area without complex and expensive equipment.
There are three types of sampling methods generally used in data collection: random, systematic, and stratified. Random refers to using a simple computer program to randomly select any number of data points for a given survey to sample. Due to the nature of this selection process, this is by far the least biased of the listed methods, as any one of the available sampling points may be selected. In systematic sampling, points are selected based on an even or regular spatial distribution. For example, every five meters over a given transect distance. This method is both more straightforward than random sampling and provides a good overall coverage of a study area, at the disadvantage of being more biased, sometimes over or under representation. Stratified sampling is a bit more complicated. It involves dividing a known area into smaller groups or subsets which are sampled separately. This is done to prevent particular areas from being missed. This technique is frequently used in surveying habitats in an area. This sampling method shares the benefits of random or systematic sampling, which it can be used with, and it can be used to make comparisons between the subsets. However, this requires the subsets to be accurately defined. However, in all of these sampling methods, a grid is usually overlayed on the sampling area to simplify the collection process, as data-points are usually taken from where the x and y planes intersect.
Methods
First, the terrain was constructed in the sandbox located near Phillips Hall of the UW Eau Claire Campus (Figure 1).

After the terrain was constructed, a 20 by 20 x,y grid was overlayed on top of the terrain using colored line, with both the x and y point 0 being set to one corner of the box (Figure 2). The points would be sampled from the intersection of the x and y lines on the grid, and the height of the line would act as the z coordinate, with terrain higher than the line being recorded as positive and below being recorded as negative.

In our sampling method, it was decided we would start on point 1,1 and sample every other column in the y row 1 (3,1;5,1;7,1;etc) until the end of the sample area was reached. From here, we would skip row 2 and move on to row 5, once again starting on point 1,5 and sampling every other point in that row (1,3;3,3;5,3;etc). This sampling method was used until a major feature was reached in the plot (valley, ridge, etc), and which point every x,y coordinate was collected (Figure 3). This is because elevation (z -coordinate) varied greatly at these locations, so it was deemed necessary to gather as much data around these features to prevent the under representation of the slope of these features later on. In addition, once row 11 of the y coordinate plane was reached, it was deemed necessary to sample every data-point in the row. This was because the majority of the constructed features, included the valley, depression, plain, and parts of the valley. In addition, to prevent valuable data from being lost with these features so closely packed together, the even y rows were no longer skipped during the sampling process. With this, sampling included all points from row 11 to row 18. Overall, we used a hybrid systematic-stratified sampling method with a 20x20 grid set up over the terrain and a distance of roughly 6 cm between any two adjacent points on the y or x planes. Total, the x, y, and z coordinates were collected in cm's from 212 points sampled over the entire plot. It was designed in this way to increase the focus on crucial terrain features while minimizing the focus on minor features.

Discussion and Results
As mentioned before, a total of 212 survey points were collected. The maximum values for the x, y, and z coordinate values (in cm's) were 114, 108, and 5, respectively, the minimum x, y, and z values (in cm's) were 5, 7, and -8, the mean values (in cm's) were 59.9, 71.2, and -2.7, and the standard deviations were 32.91, 29.16, and 2.29. Of these, the z values hold the most significance as they contain the terrain values of the sampling, while the x and y values determine how these points relate to one another. From the mean value, we can determine that most of the terrain fell below zero cm's for the z coordinate. As mentioned before, the sampling method became more precise as it moved into crucial terrain features, particularly at the top half of the plot, where sampling increased in preciseness in order to accommodate the increased density of features. While the hybrid systematic-stratified sampling method provided a balance between efficiency and precision, this left a greater focus on certain areas, leaving out those ares considered unimportant. In order to accommodate for this in the future, random sampling should be conducted on the skipped points in the future in order to decrease biased data-collection in the future.
Conclusion
In the end, this field activity provided the grounds for future sampling on a greater scale. While this particular sampling was taken from a relatively small total surface area (roughly one square meter), it still fulfilled  the basic principles of spatial sampling techniques. By overlaying the sample area with a grid and constructing a hybrid sampling strategy, a model can be generated of the sample area for later use. This model will provide both an accurate estimate of the terrain and will be generated using terrain sampling data that was taken from a collection of points out of the total sample area. This exercise provides the basis for further surveying of physical features on a macro scale (county, etc) which also function viably in terms of time, manpower, and cost. Similar sample methods could be used to survey the Chippewa Valley, with just the x, y, and z recorded values being greater in scale to accommodate. While the survey performed in the sandbox did an adequate job of sampling the area provided, it illustrated several points to take into consideration for a large scale survey. In a large scale survey, multiple survey techniques may be needed in order to create a balance of sample accuracy, minimal bias, and proper efficiency. In addition, more complex tools than line and rulers will be needed by groups of individuals in order to record survey data.
Sources

Google Earth View. Retrieved 2/7/2017, from https://www.google.com/maps/place/44%C2%B047'48.2%22N+91%C2%B029'54.3%22W/@44.796723,-91.5006167,560m/data=!3m2!1e3!4b1!4m5!3m4!1s0x0:0x0!8m2!3d44.796723!4d-91.498428

Hupy, J. (2017). Field Activity # 4: Creation of a Digital Elevation Surface using critical thinking skills and improvised survey techniques. Eau Claire, Wisconsin.

Sampling techniques. In RGS.org. Retrieved 2/7/2017, from http://www.rgs.org/OurWork/Schools/Fieldwork+and+local+learning/Fieldwork+techniques/Sampling+techniques.htm