Distance Azimuth Survey

Introduction:

Unfortunately, field work does not always go as planned—weather may be a hindrance or perhaps the field equipment necessary is unavailable or not functioning. In order to work around these situations, a back-up plan must be at hand. In this exercise, we are using an alternative, “back-up” mapping technique—azimuth and distance data collection—so we may be prepared for any problems with more advanced equipment. The general idea is to find a base point and map all other features in relation to this base point using distance and azimuth. There are a couple of ways to approach this type of mapping based on the different tools used. One approach is to use a compass and distance finder while the other is to use a laser device that will measure both distance and azimuth. This information can later be mapped as point features in ArcMap or another GIS. To keep it relatively simple, we are only mapping point features in a 50 meter by 50 meter area somewhere near/on our campus.

Methods:

Concepts:

Azimuth: Azimuth is the measurement of an angle on a spherical plane or in a spherical coordinate system.

Magnetic Declination: Magnetic declination from true north is often a factor when collecting data using a compass. True north, or magnetic north, is not always in the same direction as compass north. The declination measures the angle between true north and compass north from a certain point on the globe. It varies with geographic location and also changes over time. If the declination is not accounted for while collecting data, the location of the features that have been mapped will not match up with the actual features in the real world. Luckily for us, the magnetic declination for the city of Eau Claire is insignificant. Magnetic north in Eau Claire is 59 minutes west of compass north; therefore, we did not have to account for the declination while using the compass.

Data Collection:

               Measurement techniques:

Before we were able to begin our own projects, we had to learn the advantages and disadvantages of the measuring techniques and how to use them. With both of the methods, a point of origin must be established from which to measure the distance and azimuth. The first technique includes using a compass to find the azimuth from the point of origin and a distance finder to measure the distance. By holding the compass up to our eyes while also looking at the object, it was fairly simple to find the azimuth. The distance finder was also simple to use. It has two parts—a receiver and a “shooter”. The receiver should be held near the object that is being mapped and the shooter will compute the distance by pointing it at the receiver. The second technique is using a laser gun that can record both the azimuth and the distance of the object in question. It only needs to be set to the correct setting and pointed toward the feature.

               Both of these measuring methods have advantages and disadvantages. The first method using the compass and distance finder can be inaccurate because it relies heavily on human judgment. It is also more difficult because it requires two tools rather than one and at least two people to measure the distance. However, it requires the least amount of technology and can be used in many different situations. The second method, involving the laser gun, is much more convenient because it only relies on one tool and can be done with only one person. The downsides to the laser include a greater dependence on technology and it can be harmful if pointed at the human eye.

               Once we had learned about the techniques, we headed out to collect a few points outside of our science building. We chose a tree as our point of origin from which to measure the rest of the points. We were in an area that had a few sculptures and trees placed between the building and a large parking lot. We tried out both techniques on these points and found that they had slightly varied measurements—likely due to human error in finding distance and azimuth.

               Area:

              The first step in this project was to select an area near or on campus at our university that has a general size of about ¼ of a hectare (50x50 meters) with a significant number of features to be mapped. We chose a small area behind one of the dorm buildings that is part of a disc golf course (Figure 1). We chose this area because it seemed to have an ample number of features to be mapped. The area is near the edge of a large hill with a steep drop off and has a triangular shape. Though it is covered in snow this time of year, in the summer most of the area is covered by lawn with trees spread throughout. It has a small picnic area and a couple holes for the disc golf course.

Figure 1: This is a photo of the disc golf course area we were surveying with the azimuth-distance method. This photo was taken from the perspective of our origin point.

             Data Collection:

            Next, we had to actually collect the data. Instead of choosing only one method of data collection, my group and I paired up with another group and did both techniques for the same area. We thought it would be interesting to compare the two and see the geographic differences. The point of origin was established at the back corner of one of the buildings framing the area. With the compass and distance finder, we had one person go to each point and hold the receiver so we could record the distance from the point of origin and had another finding the azimuth using the compass provided to us by our professor (this compass did not need to be adjusted for the magnetic declination as mentioned previously). A third person was in charge of using the laser gun which recorded both distance and azimuth, and the last person recorded the data for both methods. This took about an hour to complete as we had to get a bit imaginative in finding a sufficient number of points. 

            Data Management and Mapping:

           After going out into the field, we had to enter in our data into an Excel file and ArcMap. The Excel file contained five categories: point number, X and Y coordinates, distance, azimuth, and point description. The description was kept fairly simple—most points being trees (Figure 2). The X and Y coordinates for every point were the decimal degrees of the point of origin. We found the coordinates using Google Earth, though this is an imperfect method of finding the coordinates. In ArcMap, we uploaded the Excel file and saved the data point of the origin as a feature. Once this was accomplished, we used the Bearing Distance to Line tool in the Data Management toolbox to create lines with vertices ending where each data point should have been in relation to the origin. This tool takes the distance and azimuth field and creates a line from the origin at the appropriate distance and angle for each point (Figure 3). Then, we used the Feature Vertices to Points tool. This tool simply creates a multipoint feature with the points being at the vertex of each line. Lastly, we added a basemap of our area so as to see the relation between the real-life features and the point features we mapped in the field.

Figure 2: These Excel files contain all of the data for the points we collected in the distance-azimuth survey. The table on the right has the data collected using the laser technique while the table on the right used the compass and distance finder.

Figure 3: This map shows the results from the Bearing Distance to Line tool. The lines are arranged so that each entry in the Excel file has the appropriate distance from the origin and azimuth from the origin.

Results:

               The results of this survey were not as accurate as I had hoped. Most of the points are relatively close to their features; however, for a map with this large of a scale, it is not sufficiently accurate. There was also a lot of discrepancy between the results for each method. Though some pairs of the points were close, there were others that are extremely off (Figure 4). This is likely due to the human error aspect, though some blame can be attributed to the fact that the two people collecting the points were standing next to one another rather than in the exact same spot. We also had some difficulty with the distance finder for certain points because they were blocked by other features—it is possible that these points may be off due to interference with the other objects.

               Another major issue was the accuracy of the X and Y coordinates for our point of origin. With the image provided by Google Earth, it was difficult to find the exact spot that we were standing and using for our point of origin. On top of that, the X and Y coordinates found using an online map are surely not as accurate as if we had a reliable GPS unit with us on the ground. These factors likely influenced the overall accuracy of the rest of our data points as their locations are entirely based off of their relationship to the point of origin.

               We were not able to collect the 50 points that were asked for either; we could only find 32. There just weren’t enough features in the area that could have been mapped and we were stretching it as it was. We collected points like the sign on a fence or the handrails for the staircase that led down the hill to fill in for some of the points.

               Another issue we came across was the available basemap in ArcMap. Unfortunately, the basemap did not have the resolution needed to create a clear map at the scale we were working on. The resulting map is very fuzzy and it is difficult to make out any of the features we recorded.

Figure 4: This is the final product of our distance-azimuth survey. The red points are the data points collected using the compass and distance finder while the green points represent the data points collected using the laser.

Conclusion:

               Overall, though the results were not as accurate as we were hoping for, the project was extremely informative and useful. Knowing how to use distance and azimuth to map points is an invaluable skill that will surely be utilized sometime in the future. With every project or field study, issues will arise and it is important to have a back-up plan so as to keep the project on its feet. Using two different techniques, my group and I were able to compare and contrast various tools within the distance-azimuth method of mapping data. We found that the laser gun was more accurate than the compass and distance finder; however, the compass and distance finder technique may be the only available option for a project and the tools necessary are much easier to come by.

               

Preparing for Balloon Launches

Introduction:

This week, the class prepared for the balloon launches we will be doing some weeks down the road. We are planning on using balloons to do both a mapping of campus and a high altitude balloon launch (HABL)We will be sending a digital camera up with the balloons and recording continuous shots or videos of the earth.  To ensure that everything goes smoothly with these two projects when the time comes, we had a lot of small tasks to complete over one three-hour class period. As a class, we had to construct rigs for the mapping and HABL balloons, test the parachute to ensure the camera will land safely, find the payload weights for the rigs, figure out how to keep the camera in continuous shot mode while up in the balloon, test the tracking device that will be used to find the balloon once it lands, and research how to fill the balloon and secure it to the rig later on.

Methods:

                Constructing the rigs:

                                Unfortunately, I was unable to participate much in the construction of the rigs, but I was able to speak with classmates and get the general idea for what we will be using. The designs for the two rigs are similar but differ in the way that they shield the camera. The mapping rig involves the top half of a two liter bottle to shield the camera. The camera will most likely be suspended by string within the bottle with the larger, open-ended side of the bottle facing down (Figure 1 and Figure 2). This way, the camera will be able to take photos of the campus unobstructed, but be blocked from any wind. For the HABL rig, we are using a Styrofoam bait warmer to shield the camera. Because the camera will be launched 90,000 to 110,000 feet into the atmosphere, the bait warmer is necessary to keep it from freezing.  There will also be several heating packs in the Styrofoam case. Another layer of Styrofoam insulation will be placed in the top of the case (I believe) as extra protection/heating for the camera as well. We are planning on cutting a hole through the case so that the camera will be able to record images of the Earth while still inside the case. Rubber bands are being implemented with both rigs to hold the button down for the continuous shot mode while it is in the air. The bottle and Styrofoam case will be attached by seven feet of rope to their respective balloons, though I am unsure of any further details in this part of the rig.

 

Figure 1: This is the basic design for the rig my class and I will use for our mapping balloon launch.

 

 

 

Figure 2: This is part of the rig that will be implemented with the mapping balloon launch. It also shows part of the design (the orange rubber bands) for the continuous shot which is discussed later.

 

Payload Weights:

                        The balloons have a specific payload weight that they can carry safely depending on their size. Therefore, we had to measure every item that will be included in the rigs and ensure that the balloon will be able to carry all the necessary equipment. We weighed items from carabiners to zip ties to the memory cards for the cameras and everything in between. We entered these weights into a spreadsheet in Excel and will add up all the equipment used in each rig for a total payload weight (Figure 3). For the mapping rig, we have already ordered a five and a half foot neoprene balloon.  It can carry a two pound payload and we made sure that we were within this two pound limit while constructing the rig. The HABL balloon will need to have a higher payload weight because the camera and bait warmer alone add up to about two pounds without any additional materials that are sure to be needed. Therefore, we decided on ordering a balloon that has a four pound payload weight maximum which should be more than sufficient for our HABL rig. It worked out nicely in a way because the balloon with the higher payload weight will also be able to travel higher into the atmosphere. Since the rigs are not entirely completed, we do not have exact weights for all the materials that will be included in the payload as of yet.

Figure 3: This list includes the description and weight of all the possible materials for our balloon launches. It is difficult to read, but it is clear that there were quite a few items to be weighed.

 

Parachute testing:

                        Another task that we had to complete in our class period was to test our parachute with a back-up rig. It is vitally important that our camera is able to safely land so that we can recover the footage (and also because cameras tend to be a bit expensive) from our launches. To test the parachute, we used a second, slightly smaller Styrofoam bait warmer in case something went awry, and tied it around the bait warmer(Figure 4). In order to simulate the weight of the equipment we will be using, we placed a full water bottle weighing exactly two pounds inside the bait warmer. We chose the two pound payload because it matched the payload for the balloon we already had (which we will be using for the mapping portion of the project), and had not yet determined the payload for our HABL launch.

 The next step was to throw the test rig and parachute from the highest point possible with a straight drop. Due to lack of access and high places in general, the best we could do was to throw the rig out of a fourth floor window in our science building (on the bright side we still got to throw stuff out of a really high window). After doing two test runs, the results were as good as we could hope for—the bait warmer was entirely intact after hitting the ground, and the water bottle was unharmed. I will discuss the accuracy issues with our test run in the Discussion section of this report.

Figure 4: This is the rig we used for the testing parachute launch with the back up bait warmer and the actual parachute that will be used in both the mapping launch and HABL.

 

Implementing continuous shot:

                        Perhaps the most important aspect of the entire project is ensuring that we will be able to remotely record imagery while the camera is in the air. To do this, we had to find a way to keep the camera in continuous shot mode for an extended period of time. The continuous shot mode will allow the camera to take pictures at a given length of time defined by the user. For example, we could set the camera to take a picture every three seconds until we bring it down to the ground. After the mapping launch, we will take these pictures and piece them together to form a single map. We were deciding between a one second and half a second time period, I believe, though I do not know our final choice.

 As far as we know, the best method of keeping the camera in continuous shot mode is by using rubber bands. Again, I did not participate much in this aspect of the project, so I do not have a full account. From my understanding, however, it was difficult to apply enough pressure on the continuous shot button for it to actually be in continuous shot mode. We tried using different types of rubber bands with varying length and width. It was also suggested that we tie a knot in the rubber band and place it over the button. I believe the best way to arrange the rubber bands was with two rubber bands wrapped around the camera with at least one holding a small pencil eraser over the continuous shot button (Figure 5).

Figure 5: This is the basic design my class and I are using to keep the camera in continuous shot mode while our camera is in the air during our balloon launches.

Implementation and testing of the tracking device:

                                In order to find the HABL balloon after it lands and follow its course as it travels through the atmosphere, we have to place a tracking device inside the bait warmer along with the camera. We are using a small, pocket-sized device that can be tracked online, though I am unsure of the brand. To test the device, we simply sent a student to walk around campus and followed them online to see where they had been. There were some issues with the battery life of the device, so we had to do this a couple times. We also wanted to test the longevity of the device, so a student volunteered to carry the tracking device with them for a couple of days (which fun for the professor to see I am sure). The tracking device was judged to be sufficient and will accompany the camera into the atmosphere.

                Filling the balloons and securing them to the rigs:

                                The filling of the balloons and securing them to the rigs will not be done until the day of the launch, but it is still important to set a strategy ahead of time. We found some instructions for this on the internet, but these seemed a bit odd and we deemed them inappropriate. Other online sources, http://www.sparkfun.com/tutorials/187 and http://www.toddfun.com/2011/02/20/high-altitude-balloon-launch-2/, used a 1 inch PVC pipe and some pipe fittings to fill the balloon, employing several zip ties to keep the balloon from floating away while being filled. Interestingly, one site stated that latex gloves should be used so as to keep oil from human hands from touching the balloon. Once the balloons are filled, it was suggested by these same sites to use zip ties to close up the bottom of the balloon. To secure the rig to the balloon we plan on using seven feet of braided rope, but I do not know how we are planning on securing the rope to the balloon. We are still unsure as to what method we will use to fill the balloon and will need to do more research.

 

Discussion:

                Although we accomplished quite a bit in our class period, there is still a lot left for us to do before we are prepared to launch our balloons. We have to finalize our rigs and make sure they will be within the payload limit of our balloons, and we have to find the best way to fill the balloon and attach the rig to the balloon. Students have been working with our professor outside of class to continue with these tasks, and we will hopefully have everything completed by the time of our launch.

I am also concerned about the accuracy of our parachute testing. Because we were only able to drop the rig from the fourth story of our building, it is possible that the parachute and rig will not hold up as well as it did in the test. The HABL will be going into near-space which is a far cry from a fourth story in a building. There will also be differences in wind strength and pattern that will play a factor in the safety of the descent. Another issue is the payload variance. We had a two pound water bottle inside our rig, but the HABL launch will surely have a higher payload than that.

Another interesting aspect of the HABL launch will be collecting the rig once it has landed. We will not be able to forecast how far the balloon will travel and where exactly it will land. It is possible that the rig will land on private property that we will not be able to access. I suppose we will have to rely on the kindness of our fellow Wisconsinites if this happens.

 

Conclusion:

                This project, though seemingly simple, is extremely complicated. It requires a lot of testing, critical thinking, and pudding around to find the best possible way to solve the issues with the launches. It becomes even more complicated when we bring the many different aspects of the launches together. Luckily we had many hands on board and were able to prepare mostly everything necessary. We have found the designs for our rigs for the most part, tested our parachute and pay loads, worked out a design for the continuous shot mode, and tested our tracking device. The mapping launch seems to have fewer variables than the HABL, but I am confident that both will go well.

Terrain Mapping Continued

Introduction:

 In the second phase of our project, my group and I digitally mapped the terrain that was created last week and made needed corrections. While interpolating the terrain in ArcMap and analyzing our results in ArcScene, my group and I learned quite a bit about what we had done well and what needed the most improvement. We concluded that our data was not sufficient enough to create a digital model in the detail we desired; therefore, we would have to completely recollect our data using a smaller grid size. We also decided to make slight alterations to our measuring methods in the field to be more efficient and accurate. Our second round of the data collection and analysis went much more to our liking, and the results are far more detailed and pleasing than what we had seen with the first.

Methods:

Part I: Evaluating the previously collected data

In order to model our terrain, we first had to import our Excel file containing all the data points we collected, which included their respective X, Y, and Z values, into ArcMap. We used a simple Add X, Y Values tool to map the points and converted them to a point feature class to use for interpolation. From this point, we used various interpolation tools from the 3D Analyst toolbox to view our data. These tools included IDW, Nearest Neighbor, Kriging , Spline, and a TIN which will be explained in Part II of this section.  We were to choose the interpolation that best represented our real-life model accurately while remaining aesthetically pleasing. To better view the results, we imported each interpolation into ArcScene so as to make a 3D model. Unfortunately, after seeing our results (Figure 4 is an example of the IDW interpolation in Part II of Methods), our group concluded that our data was not up to our standards, and it would be impossible to create any model that we found acceptable with our data.

 

Part II: Collecting data and evaluating results

 

After evaluating our previously collected data, my group and I came to the realization that our data was insufficient and changes were needed. The greatest issue we came across was that there simply were not enough data points to create an accurate depiction of the terrain. Our 3D mappings came out choppy, and certain features, like the river, were indistinguishable. Re-collecting data in a smaller grid size seemed to be the only viable solution to the problem.

We decided that a 5cmx5cm grid size would be the best option because it would give us twice as many data points, and it is an easy number to go by (and quite frankly anything smaller would have taken far too much time in the cold and resulted in several frostbitten toes). Luckily for us, the weather was in the 30 degree Fahrenheit range this time around--a good 40 degrees warmer than last week. Another adjustment we made was the measuring system we were using. In the previous week, we set up meter sticks along the side of the planter box and estimated where our data points would be, moving the meter sticks up as we moved along. This week, we used string (which is very difficult to find for whatever reason--we had to settle for clothesline and borrowed string from another group) and thumb tacks to set up our X-axis. We had 22 strings altogether crossing over our terrain (Figure 1).We then made marks along the Y-axis every 5cm. When we moved up along the Y-axis, we would use the measuring stick to create a straight path from one marking to the marking on the opposite side of the box and measured either up and down from the intersection of the string and meter stick for our Z-values.

 

                                     Figure 1

This is the final product of our string measuring system (X-axis) for our new terrain model

 

 

Because there was a week's difference in time between the first collection of data and the second, we had to slightly rebuild our terrain. Fortunately, the major features were partially preserved and the two terrains are almost exactly the same. There was quite a bit more snow to work with this week as compared to last week, so all of our features for the second week are at slightly higher elevations. To make up for this, we used -8 cm from the top of the sandbox as our arbitrary sea level rather than -13, so the elevation levels in our models should be the same. 

After rebuilding and collecting the data, which took about 2 hours to complete, we followed the same process as with the previous data to make a 3D model. We created and uploaded our Excel file into ArcMap and made a point feature class, but this time, we had data that was twice as dense (Figure 2 and Figure 3).

 

Figure 2                                                                          Figure 3

 

These are the two point feature classes created in ArcMap from the data points collected in the field. Figure 2 represents the data points from our first time measuring in the field with a 10cm by 10cm grid, while Figure 3 represents the second attempt with a 5cm by 5cm grid.

 

 With the same 5 interpolation methods used with the previous data (IDW, Kriging, Nearest Neighbor, Spline, and a TIN--shown and explained with Figures 5-9), we created various 3D representations of our terrain from which to choose our favorite.  To do this, we uploaded our interpolations from ArcMap into ArcScene just as we had done with the data from last week. As expected, all of the new 3D models were much more accurate and encapsulated all of our major features in much finer detail than with the first models. (Figure 4 and Figure 5).

 

Figure 4

As shown by Figure 4 and 5, the number of data points collected was a huge factor in creating a proper representation of our terrain. Although most of the major features can be seen in both figures, they appear completely different. It almost looks like two entirely different maps. The IDW was not the best interpolation of either of the maps, but the differences between the two grid sizes are clearly presented using this interpolation.


IDW interpolation of the first set of data we collected
using a 10cm by 10cm grid



Figure 5




 IDW: The IDW (Inverse Distance Weighted) interpolation creates values  for unknown points by taking a weighted mean of surrounding known points. Some known points contribute more to the mean than others depending on their proximity to the unknown point. IDW is best used with densely collected data points.

 

 

 

 

IDW  interpolation of the second set of data 

collected using a 5cm by 5cm grid

 

Figure 6 

Kriging: This interpolation method uses a statistical relationship, known as spatial correlation, among known points to predict unknown points. It "assumes that distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface" and uses a mathematical equation to find the unknown points (ArcGIS Help). It is fairly accurate in its predictions because it relies on these statistics.

3D model of our terrain using the Kriging method using a 5cm by cm grid                                                                                              

                                                                                         

                                                                                         Figure 7



Natural Neighbor: In order to find the value of an unknown point, Natural Neighbor takes the values of the nearest known points and creates a weighted average based on proportionate values. Because it only uses the "neighboring" points, the new point will have a value that falls within the range of its neighbors and does not create any new features that aren't already shown with the known points.
                                                                           The terrain mapped using the Natural Neighbor                                                                                                       interpolation method and a 5cm by 5cm grid



Figure 8

Spline: Spline uses a mathematical equation to create a surface that passes through each point collected in the data. Between each point, the surface bends so as to downplay surface curvature.It is best used with data that does not have vastly varying values.





Spline interpolation of our terrain using a 5cm by 5cm
grid
                                                         

                                                                                       Figure 9




TIN: A TIN (Triangulated Irregular Network) takes the data points and connects them using a series of edges to create triangulated surfaces that are all interconnected. TINs are generally used with data points that are in an irregular pattern--unlike the data we collected for this project.



                                                                                     TIN representation of our data using a 5cm by 5cm grid

After viewing the newer, 5cm by 5cm interpolations in ArcScene, we concluded that Kriging was the best representation because it most accurately modeled our real-life terrain. After reading about the different interpolation methods, Kriging seemed to be the most suitable because it uses a statistical method to calculate unknown points and tends to be exact. As far as the aesthetics go, Kriging is among the best interpolations, though it is a bit difficult to see the differences between Kriging, Natural Neighbors, and Spline.

 

Discussion:

 

Having completed the assignment, it is incredible to see the progression from last week's results to the results of this week. By simply comparing the 3D models with a 10cm by 10cm grid and those with a 5cm by 5cm grid (Figures 4 and 5), it is overly obvious that there was great improvement. My group and I were able to asses our first models, determine the major issues, and resolve them by remapping with a smaller grid size, and ensuring accuracy by employing new measuring techniques.

Despite the fact that the 3D models this week are far superior to the previous models, there are still some uncertainties with the data. Because we had to rebuild our terrain this week, the features are not exactly the same as they were before. Therefore, it is unfair to compare the models from this week and last week as the data isn't the same.  Also, our measuring technique was far more accurate than last week's, but it still was likely to be imperfect as we were rounding to the nearest half centimeter.

 

Conclusion:

 

Through this project, my group and I were able to use critical thinking and improvisation to solve the issues we had with our data and vastly improve our results. We were able to use several interpolation techniques to view our data and chose the Kriging method as our preferred interpolation of our new data. I am very impressed with our final 3D model and feel as though I learned quite a bit about what it means to work in the field. It is very important to be able to work through various obstacles like lack of materials or weather conditions, and also to go back and make improvements to the data.

 

 

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.