Introduction:
On January 28th, 2016, group 2 of the UW- Eau Claire's (UWEC) Geography 336: Fields Methods course conducted a terrain "sandbox" (or in this case "snowbox") grid survey. The purpose of this survey was to create a terrain out of a specific medium, in this case snow, then design and execute a land survey of the terrain's elevation. To successfully conduct this survey a basic understanding of geospatial concepts was maintained.
Methods:
Since the purpose of this miniature land survey was to gather points of elevation, a grid survey system was chosen to collect the desired data. In choosing this land survey system, one point of elevation per grid cell was expressed as a negative or positive value when compared to sea level (SL).
Once construction was complete, the next step was to create a grid over the terrain to help collect individual points of elevation. The grid was made by using push pins to mark every 5cm. Thus the original 1m² survey plot was divided into a 5cm² grid system. This can be seen on figure 1.2.
Once the entire perimeter of the desired landscape was marked, twine was passed back and forth to be wrapped around the push pins effectively creating a 5cm² grid system. (Figure 1.3)
After creating the grid was completed, the elevation surveying process could be conducted. To accurately determine levels of elevation a reference elevation or SL had to be determined. To solve this dilemma, it was decided that the landscape's SL (or point of zero elevation) was to be determined by the survey box's natural side height. This can be better seen in figure 1.4.
Within figure 1.4 is also an example of how measurements below SL was recorded. To gather the elevation points, the meter stick was positioned in the center of each grid square, which was each labeled based on its column (A-N) and row (1-20) position, and compared with the determined SL. If the value was below SL the number would get a negative value attached to it. Then to coincide with this idea, positive values received a positive label.
Results and Discussion:
From the survey methods listed above, a total of 400 separate points of elevation were collected. This data which was originally collected by a simple pen and paper grid system where again the columns were labeled A-N and rows 1-20, was then transferred onto an excel sheet.
To do this and X,Y,Z coordinate system had to be adopted. Thus the original rows of 1-20 became the X value, the columns (A-N) became the Y values and Z corresponded with the positive or negative value of elevation. Figure 1.5 is a depiction of this new data set up in excel.
When the sample points were look at as a whole, the maximum elevation was 25cm above SL while the minimum was -14cm below SL. This creates a range of 39, a mode of zero (which consisted of 293 of the 400 points of elevation), a mean of -0.755cm and a standard deviation of 6.695.
What this shows us is that the although there was a large variation in the elevation of the land surveyed, the majority of it was equal to sea level (roughly 73% of total landscape), closely followed elevations that were below SL (44% of total landscape) and finally points above SL (26%).
The method chosen for sampling showed to be a good reflection of data collected as it compared with the actual landscape. However, it would have been beneficial to automatically adopt the final XYZ coordinate system over the one that was first used.
Conclusion:
In conclusion, this assignment became a good basis for understanding the importance of land surveying techniques and how import it is to think geospatially when conducting these surveys.This project helped propel this thought process and as a result created a relatively accurate depiction of the surveyed area. By effectively and systematically surveying land, a rendered version can be quantitatively analyzed to look at patterns within the landscape and thus creating a better understanding of it.
On January 28th, 2016, group 2 of the UW- Eau Claire's (UWEC) Geography 336: Fields Methods course conducted a terrain "sandbox" (or in this case "snowbox") grid survey. The purpose of this survey was to create a terrain out of a specific medium, in this case snow, then design and execute a land survey of the terrain's elevation. To successfully conduct this survey a basic understanding of geospatial concepts was maintained.
Methods:
Since the purpose of this miniature land survey was to gather points of elevation, a grid survey system was chosen to collect the desired data. In choosing this land survey system, one point of elevation per grid cell was expressed as a negative or positive value when compared to sea level (SL).
To conduct this survey the list of items below were necessary:
- one, meter stick
- push pins
- twine
- shovel
- paper
- pencil
After this was determined, the terrain was created, figure 1.1 shows the construction of said survey section. To create a more dynamic survey, the terrain included on of each of these geologic features:
- Ridge
- Hill
- Depression
- Valley
- Plain
 |
| Figure 1.1: Construction of 1m² snowbox landscape |
 |
| Figure 1.2: First stage of constructing grid system over terrain. |
Once the entire perimeter of the desired landscape was marked, twine was passed back and forth to be wrapped around the push pins effectively creating a 5cm² grid system. (Figure 1.3)
 |
| Figure 1.3: Final grid construction. |
After creating the grid was completed, the elevation surveying process could be conducted. To accurately determine levels of elevation a reference elevation or SL had to be determined. To solve this dilemma, it was decided that the landscape's SL (or point of zero elevation) was to be determined by the survey box's natural side height. This can be better seen in figure 1.4.
Within figure 1.4 is also an example of how measurements below SL was recorded. To gather the elevation points, the meter stick was positioned in the center of each grid square, which was each labeled based on its column (A-N) and row (1-20) position, and compared with the determined SL. If the value was below SL the number would get a negative value attached to it. Then to coincide with this idea, positive values received a positive label.
 |
| Figure 1.4: Depiction of sea level (horizontal meter stick) and the landscapes respective elevation (vertical meter stick). |
 |
| Figure 1.5: Final data entry method on Excel |
From the survey methods listed above, a total of 400 separate points of elevation were collected. This data which was originally collected by a simple pen and paper grid system where again the columns were labeled A-N and rows 1-20, was then transferred onto an excel sheet.
To do this and X,Y,Z coordinate system had to be adopted. Thus the original rows of 1-20 became the X value, the columns (A-N) became the Y values and Z corresponded with the positive or negative value of elevation. Figure 1.5 is a depiction of this new data set up in excel.
When the sample points were look at as a whole, the maximum elevation was 25cm above SL while the minimum was -14cm below SL. This creates a range of 39, a mode of zero (which consisted of 293 of the 400 points of elevation), a mean of -0.755cm and a standard deviation of 6.695.
What this shows us is that the although there was a large variation in the elevation of the land surveyed, the majority of it was equal to sea level (roughly 73% of total landscape), closely followed elevations that were below SL (44% of total landscape) and finally points above SL (26%).
The method chosen for sampling showed to be a good reflection of data collected as it compared with the actual landscape. However, it would have been beneficial to automatically adopt the final XYZ coordinate system over the one that was first used.
Conclusion:
In conclusion, this assignment became a good basis for understanding the importance of land surveying techniques and how import it is to think geospatially when conducting these surveys.This project helped propel this thought process and as a result created a relatively accurate depiction of the surveyed area. By effectively and systematically surveying land, a rendered version can be quantitatively analyzed to look at patterns within the landscape and thus creating a better understanding of it.
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