Lab 1: Create a Digital Elevation Surface

Introduction:

Sampling is a more time efficient way to collect data for a whole population. By analyzing data from the sample of a whole population, one can use that information to make predictions about the total whole population. Within sampling, there are three main strategies that are used:

• Random: samples are completely bias because each have an equal chance of being selected
• Systematic: samples are selected on an interval basis with regular distribution in spatial context
• Stratified:  samples are selected from subgroups of the total whole population to ensure the samples are more representative of the total population 

The objective of this lab is to use critical thinking skills to create a terrain that includes specific landscape features (Ridge, Hill, Depression, Valley, and Plain) and then survey and map the designed surface by thinking spatially (Figure 1). A 114cm by 114cm sandbox was provided to create the terrain. 

Figure 1: Creating the Terrain

Methods:

For this lab, the systematic point sampling technique was used because it allows samples to be selected systematically using a grid system which ensures that each sample will be evenly distributed throughout the total area of the sandbox. This technique was chosen because it provides evenly distributed points throughout the total area of the sand box that would better represent the created terrain. 

The location of the sample plot (sandbox) is located on the University of Wisconsin-Eau Claire campus. The plot is on the east side of Phillips Science Hall, on the opposite side of Roosevelt Avenue, and close in proximity to a shed. 

The required materials include: sandbox, sand, thumbtacks, string, meter stick, pencil, and a field notebook.

To set up the plot for systematic point sampling, a meter stick was used to measure every 6cm around the border of the sandbox and marked by a pencil mark. Next, the thumbtacks were placed at the 6cm marks which allowed there to be a grid system of 20 by 20 boxes (Figure 2).

Figure 2: Placing Thumbtacks Around the Perimeter of the Plot

Then, string was laced around each thumbtack to officially create the (X,Y) grid system. The bottom left hand corner of the sand box was (0,0) and continued on from there (Figure 3). Each intersection where the X string crossed the Y string was measured with the meter stick and the measurement was recorded in a field notebook with a table containing X, Y, Z. Sea level is represented by the top of the sandbox.

Figure 3: String Laced Around Each Thumbtack

This system worked well with terrain measurements (Z) so that they could stay organized and be identified with specific (X,Y) points on the grid. The measurements were then transferred from a field notebook to a Microsoft Excel document to be further analyzed. 

Results/Discussion: 

After transferring all of the data into Excel, the sample values could be evaluated in (X,Y) grid formation (Figure 4). The left bottom box represented in blue color displays the first (0,0) sample and the other yellow boxes show the rest of the sample points with (X,Y) coordinates on the side to show location in the plot. Each sample is a negative number because sea level was represented by the top of the sandbox on the plot. So, each sample is that distance in cm from sea level which is zero.

Figure 4: The 400 Sample Points in Grid Formation

Total Sample Points: 400

Minimum Value: -19

Maximum Value: -4.2

Standard Deviation: 2.7

Mean: -13.66

Median: -11

The sampling that was chosen related well to this method of recording the measurements because the total number of sample points was 400 which was enough elevation measurement points to accurately represent the created terrain. Although recording the samples at each (X,Y) location where the strings intersected seemed a little tedious, the sampling technique stayed the same to ensure accuracy with the samples. Taking evenly distributed samples to start the surveying process and then taking less samples during the end wouldn't have provided as precise of data. When this lab was performed, it was February 1st, so it was very cold and there was ice surrounding the wooden border of the plot which needed to be chipped away to be able to place the thumbtacks in. During the entire process of this lab, snow continued to be nudged into the sandbox and needed to be removed. Also, because of the cold temperature, the ground was frozen where the terrain was created but luckily there was enough free sand to construct the landscape.

Conclusion: 

The sampling conducted in this lab relates to the definition of sampling that was provided in the beginning of this post because specific points of the plot were chosen to be sampled to create an accurate representation of the terrain without completely taken measurements of every aspect of the terrain. From the 400 sample points recorded, predictions can be made about the shape of the terrain. Sampling is effective in a spatial situation because it would take an immense amount of time to record samples of every location in a given space. Sampling saves a lot of time while still providing enough needed information about a space. This lab is a good example of how samples would be collected over a larger spatial area because there is a lot more ground to cover. The samples that were collected during the survey of the plot were an adequate representation of the terrain. However, more samples could have been taken in areas of the plot where the terrain had greater variance in elevation like the ridges or depressions. 

Sources:
http://www.rgs.org/OurWork/Schools/Fieldwork+and+local+learning/Fieldwork+techniques/Sampling+techniques.htm