Terrain Mapping

Introduction:

Improvisation and critical thinking are vital skills for geographers in the field. In order to gain experience with these attributes, my group and I are in the process of creating a digital elevation model from a terrain we have designed from scratch. My group (consisting of myself and two other undergraduate Geography students) was assigned a "sandbox", roughly 1 1/2 meters by 1 meter, in which to build a small-scale, varied landscape that we will later digitize in a geographic information system. We were instructed to create our own coordinate system and method for measuring data so as to develop critical thinking skills and improvise in the field in order to complete our task while accommodating present weather conditions.

Methods:

Once we bundled up for the negative ten degree weather, my group and I headed out to our sandbox to create our landscape. Unfortunately, due to current weather conditions, we used snow rather than sand to create our terrain which included several land formations--a mountain, river basin, valley, plain, and plateau.

Terrain that my group and I created  within our sandbox

With the terrain completed, we decided to use a ten centimeter by ten centimeter grid to map our landscape (something we should have decided while still in the heated building). We chose this grid size because we thought it would give an accurate and detailed portrayal of our profile without having an overwhelming amount of data points. By setting up our measuring sticks on the X and Y axis, the wooden sides of the sandbox, we were able to easily find our X and Y measuring points for elevation. We also used a string that we taped across the wooden box on the X axis at "sea level" and measured the terrain in centimeters either up or down from the string to be more exact. This process was fairly simple, though we had to take a couple breaks from measuring to reheat inside the building. Each group member was assigned a role--measurement-taker, measurement-reader, and measurement-recorder.

The group reheating inside the building before heading back out in the cold!

After the measurements were taken, the next step was to create an Excel file of the data. Because most of the land features we created were below sea level due to lack of snow, we decided to adjust our sea level to thirteen inches below the previous level. This makes for a more realistic model of the terrain.  We also chose to represent one centimeter as one meter for our digital elevation model, though we have yet to enter the data into a geographic information system. Our Excel file has both the original Z-coordinates and the modified (plus thirteen centimeter) coordinates.

Recording our measurements in the field

Discussion:

Although this field exercise is yet to be completed, we can interpret some results from what has already been done. Most of the original data fell below sea level because there was lack of material to work with, but after adjusting the sea level the majority of the data points were above sea level. The highest point of the data was thirteen and one half meters above sea level near the peak of our mountain at 145 meters North and twenty-five meters East. The lowest point was negative four and one half meters in a valley at 125 meters North and 105 meters East. The mean Z-value was two meters above sea level.

Unfortunately, it was difficult to collect exact values using only a meter stick to measure height, so we had to estimate some of our values. We also rounded our data to half or whole centimeters. There was some difficulty ensuring that the string we used was perfectly parallel to our X axis, as well. Another problem we had was that we could not record every point of the key landscape features. For example, the river basin will not be entirely mapped because some of the data points skipped over sections of the basin. Thus, these results are not entirely accurate, and the digital rendering of the terrain will not be exact.

Conclusion:

Overall, this exercise has already provided the opportunity to improvise and use critical thinking in the field by allowing us to create our own measuring system and adjust it properly. At this point, we have conducted the field work and have yet to map the data points collected. My group and I will be able to draw more results once we have completed the mapping portion of the  project and better determine where we excelled and where we can improve. The most important outcome thus far, is that there is a lot of flexibility in creating/choosing one's own coordinate system, and that adjustments can be made in order to find the best results. For example, we changed our sea level value to better suit our Z-values.