Lab One: Survey of a Sandbox

Lab One: Survey of a Sandbox 

Introduction

The first lab of Geography 336, Geospatial Spatial Field Methods, strove to introduce students to field work through the collection of data points on a relatively small scale. Students were tasked with creating a miniature landscape contained within an approximate four by eight foot (~120x240cm) planter box located on the UWEC campus. Students were tasked to create a landscape containing features such as valleys, hills, and ridges with snow or dirt, depending on the season (Snow in this case). Once the scenery was created we were required to survey the area in the hopes of eventually creating a digital elevation model of the scene with the use of ESRI ArcMap software.

A short definition of sampling is a technique of measurement that doesn’t require the data collection of all aspects of a study area. Yet it still merits significant results. Sampling measures a representative group of factors instead of recording all factors. This saves time and resources. And, when done correctly, will typically give results very close to those that would come from taking all possible measurements.

There are three major types of sampling techniques used for the quantification of spatial data:

Random sampling is used when the area of study in relatively uniform. One can assume that any sample location will have many of the same attributes as the surrounding area. Random sampling locations should not be determined by the person collecting samples; this has the potential to introduce biases into the experiment. Instead it is typically acceptable to use a random number generating computer program to create coordinates for the data collection locations.  

Systematic sampling is the collection of sample data with equally spaced distance between every point. As far as sampling techniques goes this is debatable the quickest and easiest to do for surveying spatial data. It is very useful for recording data that undergoes gradual change over the study area. One needs to be careful with the use of this technique as it is common to entirely miss areas of rapid change when point intervals are set too wide.

Stratified Sampling is best used for surveying a study area with multiple feature types. It is a technique developed to minimize the exclusion of data that helps to demonstrate variation in the area of study. This stratified sampling works at the discretion of the researcher and is reliant on their background knowledge of the area of study. This is because it focusses on the collection of points from areas that are most important to the research question. Many points are simply replicates of previous points collected in that area and when put together statically represent the feature. Fewer points are collected from surrounding areas, saving resources, but limiting background data.

Methods

For this activity my group used a systematic sampling technique so as to give every aspect of our study area equal representation. In hindsight I believe a stratified sampling technique would have been a better choice for the environment seeing as there were many areas of rapid change missed by the systematic measuring system that could have been focused on more in depth. But due to the time constraints on the experiment (daylight was fading as the study drew to a close) a systematic sampling technique worked out well as results for the entire study area were quickly quantified.

Figure 1. Planter’s box containing area of study. 

The study area itself was a planters plot located on the south side of the Philips Science Hall courtyard. This is an educational building part of the University of Wisconsin Eau Claire Campus, a public postsecondary school located in the Midwest region of the United States of America. This planter’s box was approximately four by eight foot (~120x240cm) in size but for this experiment my group utilized a little less than half of it (pictured above in Figure 1). 

Figure 2. 8cm spacing of lines to delineate y coordinates for systematic survey. 

Figure 3. Altitude measurements taken with the use of meter sticks and a small level.

With the use of lengths of string, stainless steel meter sticks and thumb tacks my group was able to create a sampling scheme of 12 strings running east to west across the plot. Each string was placed 8cm apart to form a length covering 96 cm across. For the north to south axis of the grid 14 markers were placed along the short edge of the plot (Figure 2). These markers were use as references for the x,y location for every elevation measurement. Zero elevation (Z coordinate) for each point was determined by its relative position to the height of the planter box frame. Negative vales were given for measurements lower than the frame while positive values were given for points above it. These altitude measurements were taken by a two person team with the use of two stainless steel meter sticks, one as a straight edge and one for recording measurements, and a small level. A third group member recorded the results of each measurement on a paper spreadsheet. For each point an x, y and z value were assigned. After the measurements were recorded in the field the values were entered into an excel spread sheet for the easy transfer of information to ESRI ArcMap.

Results/Discussion

 The plot measured 12x14 rows. Measurements were taken at the intersection point of each row and the immediate area adjacent to the sides of the plot. This resulted in 168 interior points and 38 border points totaling 206 points of measurement. As the experiment was conducted my group stuck to the original plan for the most part. The exception being that we reduced the number of points we were planning to measure. This choice was made in the interest of completing the project while there was still enough daylight to see what we were doing.  One far more major issue was found while laying the grid lines (Figure 4). The plot that we were using had features that were much higher than the sides of the plot (the reference level for the zero elevation value). This not only meant we had to come up with two different elevation measuring techniques but, due to the overlay of strings across features, the x,y values recorded would be very difficult to verify for their accuracy. A proper remedy for this problem would be to elevate the grid lines above the tallest features so that there would be no interference with the lines, but due the lack of obvious materials to do this, my group used an “eyeballing” approach to get the approximate location for x,y values.

Figure 4. Strings laid across the plot. Notice the displacement caused by tall features. 

Conclusion

In short our sampling exercise was a sloppy version of the systematic sampling technique. I will concede that a systematic sampling technique is useful in the collection of a representative amount of data for a large area. This makes it especially advantageous as it saves resources and is far quicker/easier than measuring every aspect of a study area while still producing useful results. Unfortunately it is common to miss areas of rapid change as the measurement intervals may be spaced to far apart to accurately represent the study area. The same techniques used in this lab are small scale versions of what have been used in the field. While the tools and materials may be different at larger scales the techniques and practices are quite similar. Yet despite this I do not believe that my group’s survey will create an accurate representation of the survey area. There simply were not enough points generated.

Sources used for background information:

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

https://www.geography-fieldwork.org/geographical-enquiry/before-you-start/2-fieldwork.aspx