Surveying a plot of land can be a complicated process, full
of many possible methods of completion. If the area is small enough, or if a
high degree of detail is required, grid plots can be constructed in the area of
interest, and points can be taken in each grid, providing a large number of
data points that can be entered as X and Y coordinates in a Cartesian system.
In large areas, however, this method does take a significant amount of time,
and may not be appropriate in situations where an accurate survey needs to be
done quickly and efficiently. One such method for quickly finding direction and
bearing is using a laser rangefinder, like the TruPulse 360, as shown in Figure
1.
Figure 1: Me using the TruPulse to find the azimuth of a tree in Owen Park.
According to the product’s information website,
the rangefinder provides a very accurate compass technology to get the best
azimuth reading. The TruPulse can provide Distance, Inclination, Height,
Azimuth, and Missing Line ratings using a laser, receiving a reading when the
laser hits the object to be surveyed.
Methods:
When getting acquainted with the rangefinder, the first area
to be surveyed was the parking lot outside of Phillips lecture hall on the
campus of the University of Wisconsin-Eau Claire. A few points were taken,
including a statue, an emergency post, and a few trees. The distance and
azimuth for each object was recorded, and put into a table format, as well as
the latitude and longitude of the start point in decimal degrees. Exact coordinates
were needed to get a perfect location for the start, as we were quick to find
out, because if it was not accurate, the lines would not be in the proper
position on the map, as demonstrated in the map in Figure 2. For the actual laboratory exercise itself, the class was
split into groups of two and told to find a survey area where at least fifty
data points could be found and recorded. We decided to survey tree locations in
Owen Park, a small area near the Chippewa River in Eau Claire, near the
University. A satellite view of the park can be seen in Figure 3, and
additional pictures of the scenery can be seen in Figures 4 and 5.
Figure 2: Initial Points taken during familiarization with the TruPulse. As visible by the image, if the full coordinates were not input, the position of the lines would be incorrect in relation to the starting point.
Figure 3: Satellite View of Owen Park, the study area for the activity. Image provided by Google Maps.
Figure 4: Owen Park, with its many trees, provided a number of survey points for this activity
Figure 5: Owen Park
Using the
TruPulse, the distance, azimuth, and height of fifty different trees were
calculated and recorded. A copy of the table of the data can be seen in Figure 6,
showing the values of distance, azimuth, and height, as well as the coordinates
for the four starting points.
Figure 6: Data Table with distance, azimuth, and height values
Once the points were collected, they needed to be imported
into ArcMap for data manipulation. The Excel table that the data was input into
was brought into a file geodatabase, brought into the program, and the Bearing
to Distance Line tool was used to show the positions of the trees digitally,
with the result being illustrated in Figure 7. To ensure a truly accurate
reading, the lines were reprojected into the WGS 1984 UTM Zone 15 for maximum
accuracy. Once this was done, the Feature Vertices to Point tool was used to
attach points to the end of the lines, to better illustrate where the trees
were supposed to end, as seen in Figure 8.
Figure 7: Survey Line positions acquired from the data collected in the park, and using the Bearing to Distance Line Tool
Figure 8: Feature Point to Vertices tool being used to plot where the trees should be, compared to actual position on satellite image
Discussion:
Unfortunately, with most methods of data collection, a margin of error exists that needs to be corrected for. The first potential error that occurs in this process is magnetic declination, which is the difference between True North and Magnetic North, which the laser rangefinder uses to produce accurate readings. Luckily, in Eau Claire, this phenomenon's effect on the readings by the TruPulse is as minimal as possible, so compensation is not necessary. An error that did take place, however, was the lining up of the data points with physical trees. By examining the
image, it is evident that there is a bit of an accuracy issue from the data
collection of the points. Unless the satellite image being used as a basemap is
older, and does not show the current location of all trees, it appears that there
are some points on the map that do not belong to a tree, suggesting that there is a source of error in this process, be it the satellite image not being temporally accurate, the projections not lining up, or a combination of other factors.
Conclusion:
This experience with the TruPulse laser rangefinder was invaluable, and introduced us to an entirely new and technologically advanced way of surveying a landscape. While the rangefinder was more high-tech than survey grids, error still can occur while using it, which is evident in the inaccuracy of the location of the points surveyed. But this error could be attributed to collection by us on the ground, with the exact distance or azimuth values being recorded wrong, or the coordinates of the start point being incorrect by using an aerial photo instead of a GPS. But these kinds of error happen, and experiencing it in the field allows us to learn about the drawbacks of survey equipment, and choose the best one for the job, being fully educated on its positives and negatives, and any error that may occur when using it.
Data collection can take many forms, like researching for it in books and other published works, taking surveys, or actual field collection of it. Field collection of data is the most unique of these, however, because of the potential for error to occur while in the field, and having no resources or plan to fix the situation. To reduce this problem, whenever field methods are utilized, measures need to be taken to be as prepared as possible and try to take into consideration any mistakes that could be made. This lab involved the construction of a mapping platform and equipment that will be used to take highly detailed pictures of the balloon's trip from the ground level of the University of Wisconsin-Eau Claire to the atmosphere, where the mapping platform will then be released and glide down to the Earth's surface with the assistance of a parachute. An example of the flight of the balloon can be seen by watching the video below.
This process certainly has many aspects that could go wrong, so careful preparation during the construction of the mapping platform and subsequent components is essential to ensuring that the data collected can actually be recovered and used.
Methods:
Figure 1: Parachute and accompanying materials
The balloon kit that we are going to use only consists of the physical balloon itself and related materials, as demonstrated in Figure 1. The rest of the mapping platform needed to be built by us. To successfully construct a mapping platform, a number of tasks needed to be completed. Below are a list of some of the things that needed to be done:
Construction of mapping rig
Construction of HABL rig
Design of implementing continuous shot on cameras
Recording of weights of any and all objects that could possibly be used in the mapping platform
Parachute Testing
Implementation and testing of tracking device
Filling of balloon and securing it to the rig
Figure 2: Construction of the first mapping rig
The first step, the construction of the mapping rig, involves creating an apparatus that will house the digital camera, keeping it safe on its journey The website that sold the balloon suggested using the top of a two liter soda bottle for this, and then attach the camera to the inside of the housing, pointing down in continuous shot mode to capture all images. We decided to try to see what kind of product this process would produce, and the resulting contraption can be seen in Figure 2.
String was used to secure the camera through the top of the bottle, with the lens pointing downward. In order to keep the camera shutter pressed down to engage continuous shot mode, rubber bands were used, illustrated in Figure 3.
Figure 3: Camera set up to maintain continuous shot mode
Figure 4: Construction of the second mapping rig
The issue with this kind of capsule is that the camera is at the whim of any directional movement, and could start spinning wildly, causing the quality of the images to suffer. A more secure houseing for the camera was necessary to produce the desired results, and we would not have known this without building the initial capsule first. To solve the stability problem, an entire two liter bottle was used. The camera would sit inside of the bottle and secured through with zip ties. The opening that the camera would be put through would then be covered, and the entire housing would be closed, a significantly more secure capsule than the one that was suggested in the instructions included with the balloon kit. This new mapping rig can be seen during the construction phase in Figure 4.
Figure 5: Construction of the HABL rig
The next step required creating the HABL rig, which would act as a sort of shock absorber, and would have to be made of a material that is sturdy enough to minimize damage. To do this, a styrofoam fishing bucket was used, due to its light weight and ability to store a significant number of items inside of it. Inside of the bucket, a piece of insulation was inserted, to act as an additional barrier. Construction of the HABL rig can be seen in Figure 5.
The third step involved careful documentation of all of the weights of anything that could be attached to the balloon. This essential part was necessary because after reaching optimal height, the parachute would be deployed, and the mapping platform would then descend to the Earth's surface. Having too much weight attached to the platform could cause the parachute to be ineffective, and have the platform crash to the ground, creating a very real possibility that the data collected by the camera would be damaged. Additionally, and principally, if the payload weighs too much, the balloon would not be able to achieve a high enough altitude, rendering the entire project worthless. A simple scale was used to register the weight, and then each object's value was recorded, with the table found in Figure 6 below. According to the website where the balloon was purchased, the HAB 1000, the balloon we will be using for the experiment, works the best when the payload ranges from two to four pounds, or 900-1800 grams. That value will have to be carefully paid attention to when we decide which of these items to include in the mapping platform.
Figure 6: Weight chart of objects
Balloon
Mapping Weight Chart
Item
Weight
Balloon (Orange)
315.5 g
Balloon (Red)
322.25 g
Black rubber ring (~1 inch)
8.25 g
Camera (Biggest, black)
392.17 g
Carabineer (blue with key
ring)
4.79 g
Carabineer (silver with loop)
26.71 g
Coke Bottle (2 liters, empty,
whole with cap)
50.86 g
Coke Bottle (Top, Label
"1")
18.6 g
Coke Bottle (Top, Label
"2")
12.5 g
Handwarmers (2 in package)
54.37 g
Jif Peanut Butter (No cap,
empty, whole)
48.6 g
Memory card (16 gb)
2.16 g
Memory card (32 gb)
2 g
Minno Thermo with lid and
rope
75.85 g
Mt. Dew (2 liters, empty,
whole with cap)
52.08 g
Orange Camera (No memory
card)
185.77 g
Parachute (blue and orange)
144.7 g
Pink Rope (1 meter)
1.15 g
Rainex Bottle (Empty, whole
with cap)
141.36 g
Rope (150 ft.)
416.51 g
Rubber band (black, midrange)
2.8 g
Rubber band (blue, thin,
medium)
2.37 g
Rubber band (Extra small,
orange)
1.14 g
Rubber band (long, tan, thin)
4.7 g
Rubber band (long, white,
wide)
14.4 g
Rubber band (short, white,
wide)
5.69 g
Rubber band (thin, white)
3.5 g
Silver Camera (No memory
card)
187.5 g
Styrofoam (Pink, 1.5 by 19 by
17.5 in)
200.3 g
Yellow Cord with buckle
106.5 g
Zip Tie (Black)
1.5 g
Zip Tie (long, multicolored)
1.16 g
Zip Tie (Short, multicolored)
.31 g
7 Packs of Handwarmers
379.86 g
Cut Styrofoam+Minno Thermo
102.12 g
Green Bottle (With cannon,
grey "Hindenburg")
239.69 g
Total Pay Load for High
Altitude
944.34 g = Approx. 2.08 lbs
The next step after figuring out the weights of the objects was to test the parachute to see if two lbs of equipment would be a weight that would not be too heavy for it to support. To do this, the styrofoam fishing bucket that was going to be used for the HABL rig was filled up with a weight of two lbs, and dropped off of the fourth floor of Phillips Hall, as seen in the video in below. The parachute was able to effectively carry the target weight, and the stryofoam bucket was undamaged.
The last step that needed to be done was to test the tracking device that was going to be attached to the mapping platform in order to make sure that the device was able to be recovered. A tracker was selected that would be able to be accessed through an Apple iPhone or iPad to provide for a convenient medium in which to display where the platform had landed after the balloon was deflated and the parachute had deployed. Depending on the wind on the day of the launch, the platform could land relatively closely to the launch area, or could be carried away many miles away, so an accurage GPS tracking device is absolutely essential to providing us with the location of the platform so that we could retrieve the images taken by the camera. To test this, one person would take the tracking device,and another would use either an iPhone or iPad to find them. This would simulate the search that will happen on launch day, and see the disparity between where the device says the tracker is, and where it actually is.
Discussion:
In order to have this process go as smoothly and quickly as possible, it was necessary to have multiple groups doing a different step. Some people assisted in parachute testing, others built the mapping and HABL rigs, others recorded weights of the objects, and still others tested the tracking device. If everybody only did one thing at a time, this testing would have taken considerably longer, and then all the tasks likely would not have been completed within the allotted amount of time. If this happened, not all of the issues would be taken into account, leading to more variables on launch day that could not be forseen and planned for, ultimately affecting the finished mapping project.
Conclusion:
This lab, while only allowing us to mostly do prepatory work, was absolutely vital to the future launch date when the balloon is actually launched. Conducting this kind of work beforehand may not be as exciting as the day when the balloon is launched, but to make sure that the project is a success and is issue-free, preparation is necessary. This activity required extensive teamwork, ensuring that every group extensively documented their methods and why it was important to the rest of the project as a whole. And the concept of taking time to examine a process from start to finish before actually going out in the field to do it was an important lesson to be addressed. So many things can go wrong in the field, and the more time taken beforehand to try to solve these problems leads to everything going better at the time that the field work is done.
For this project, we were asked to create a miniature
landscape in one of the sandboxes located in the courtyard of Phillips lecture
hall, complete with geographic features such as plains, plateaus, valleys,
depressions, mountains, and/or canyons. When completed, the features were to be
surveyed, using a grid, Cartesian, coordinate system. With the measurements of
each grid block carefully recorded, X, Y, and Z coordinates were input into a
Microsoft Excel spreadsheet, to be them imported into ArcMap for further data
manipulation. An appropriate interpolation was then selected and a TIN, or
Triangulated Irregular Network, was created through the survey points that were
collected. The TIN was then imported into ArcScene, in order to visualize the
created landscape of the sandbox digitally within the computer program. However,
due to a general lack of experience in survey methods, we were allowed an
opportunity to make necessary changes in our landscape and survey methods in
order to make a more pleasing image in both ArcMap and Arc Scene. After the
second survey, the same process was followed, and the changes made in data
collection and methods produced a superior digital representation of the
sandbox landscape.
Methods:
Creation of the Landscape
Figure 1: Zach creating the river bed in the sandbox landscape
For the landscape that we had decided on creating, a mountain, river valley, plateau, depression, canyon/river bed, and several plains areas were created. To do this, it was decided that using the snow that was already plentiful inside of the sandbox would be simpler and easier to form than using the sand that was underneath the snow. In Figure 1, Zach can be seen creating the depression in the river bed/ canyon feature. Figure 2 shows myself flattening down snow to create plains. After the landforms were created, the snow was moist enough to stick together and remain that way, preserving the features that we had built.
Figure 2: Drew packing down snow to create a region of plains
The Grid System and Recording of Data
Figure 3: Laying out the string into grid sections to survey the features
For our coordinate system, our group decided
that a Cartesian system of grids would be the most appropriate for accurate
data collection. The first time we conducted this exercise, grids of 10cm by
10cm were thought to be the most accurate. To get these grids, string was laid in sections across the sandbox after the landforms were created. The entire box was not partitioned out, however. The measuring tape was used as a guide, and marks were made in ten centimeter increments, with measurements being at the middle of every single increment.
Figure 4: Hannah recording survey points of the landscape in Cartesian format
Unfortunately, after the first phase of the surveying process, it was found out that 10cm by 10cm grids was not fine enough to accurately represent the landscape, so it was decided to move to 5cm by 5cm grids, for a total of 1,056 survey points, as illustrated by Figure 5.This provided much more detailed coordinates, giving our group the data necessary to make an accurate representation of the landscape that we had created.
Figure 5: The data points collected after deciding on a 5x5 cm grid system
Data Importation, Interpolation Methods, and TIN Creation
After diligently recording the points onto paper, we migrated the points into a Microsoft Excel spreadsheet, and then moved to import those coordinates into ArcMap for the first step of data manipulation. Importation was simple due to the program's ability to integrate with Excel files. From this point, interpolation was done on the survey points, with the ultimate goal being to find a method that would produce the best looking and most visually representative model for the landscape that we had created in the sandbox. Surprisingly, our new data set was significantly better than the first one, and there was little to no difference between the interpolation methods of IDW, Kringing, Natural Neighbor, and Spline, illustrated in Figures 6, 7, 8, and 9, respectively.
Figure 6: Survey points illustrating IDW method of interpolation
Figure 7: Survey points illustrating Kringing method of interpolation
Figure 8: Survey points illustrating Natural Neighbor method of interpolation, the best looking method
Figure 9: Survey points illustrating Spline method of interpolation
After deciding on a method of interpolation that looked the best visually, it was then time to create a TIN, or Triangulated Irregular Network of the survey points. A TIN uses the Z values of elevation to assist in the creation of a three dimensional image of an otherwise two dimensional figure in ArcMap. The TIN can be then imported into ArcScene, and then a 3D model of the survey points can be created.
ArcScene and the Creation of a 3D Model of the Landscape
Finally, using the TIN that was created in the previous section, a digital, three dimensional model of the landscape created in the sandbox and recorded through the survey points can be created in ArcScene. The updated survey method of creating 5x5cm grids created a very accurate representation of the landscape that we created in the sandbox. Figure 10 demonstrates the digital landscape created in ArcScene, and when compared to Figure 11, the picture of the actual landscape created in the sandbox, it is clear that the survey method was effective, and our group's hard work was successful.
Figure 10: 3D Representation of the landscape created in the sandbox
Figure 11: Physical landscape created in the sandbox
Discussion:
The process of finishing this project from start to finish was the most time consuming part. Taking 1,056 individual survey points was extremely time consuming, taking roughly 2.5 hours to complete fully. But this was undoubtedly the most effective way to produce enough points to get an accurate representation of the landscape that we had created in the sandbox. Moving from 10x10 cm to 5x5cm grids is the sole reason for smooth looking TIN image, which also translates into an image in ArcScene that closely resembles the real landscape produced within the sandbox.
There were some difficulties in the data collection and the importation of the survey points into ArcMap. The first was the recording of the points themselves. There were so many points to record that it was inevitable that there would be some points that were recorded wrongly. In some instances, negative signs were omitted or added, and that threw the landscape off in the interpolation and TIN creation. Thankfully, this issue was easily remedied by a simple edit session within ArcMap. Another issue occurred when deciding to create a false sea level. It seemed like a good idea, considering that most of the landscape was under the wooden barriers of the sandbox, and therefore most of the values for elevation would be negative and certainly not representative of a real environment. However, when the points with the false elevation were imported into ArcScene, a grossly unrepresentative image was produced, as illustrated by the image in Figure 12. It is obvious that this was not an accurate image, so the original elevation coordinates were used instead, again demonstrated in Figure 10.
Figure 12: ArcScene image of survey points with false elevation
Conclusion:
This lab was a very useful tool in teaching about surveying a landscape and then taking the data retrieved and importing it to create a digital copy of it. Working effectively in a team was absolutely essential in producing an accurate representation of the data, and communication was vital in doing this. But simple recording of points was the easy part, and critical thinking skills were necessary to address the numerous issues that came up over the course of the project. Editing individual survey points, deciding whether or not to use a false elevation, and selecting an appropriate interpolation method were all issues that had arisen throughout the course of the project. But trial and error created a favorable result that not only completed the goals of the project, but also taught valuable skills in surveying and added expertise in both ArcMap and ArcScene.
For this lab, we were required to create a geographic
landscape of a smaller scale in a sandbox. This landscape will then have its X,
Y, and Z coordinates surveyed, and then imported into ArcMap to create a
virtual representation of the created features.
Study Area/ Methods:
This landscape in the sandbox was required to have hills,
valleys, plains, and other features present in nature. We decided to construct
a riverbed, a plateau, a mountain, several plains areas, and a basin.
The created landscape
An aerial view of the landscape to be surveyed
Hannah recording coordinates
Zac building a river valley
Drew flattening out the plains
After the
landscape is created, it then will be surveyed in a Cartesian coordinate system
with grid systems that were deemed appropriate by the group members. Our group
decided on 10cmx10cm grids, starting five centimeters from the edge of the
sandbox, to avoid the angles in the interior side of the wooden barriers. X, Y, and Z coordinates were needed for this,
and the grid system made the X and Y coordinates simple to record. Z
coordinates required measuring down from the top of the edge of the sandbox. As
a group, it was decided that we would measure at the center of each box in the grid
system, and that value would be recorded as Z for that cell. After taking
measurements, we decided to set an arbitrary sea level at -13cm, considering
that there were very few values that were actually above the barriers of the
sandbox. Below is the list of our coordinates that we measured.
North (cm)
East (cm)
Elevation (cm)
Elev Conv(+13)
5
5
-7
6
5
15
-7
6
5
25
-7
6
5
35
-7
6
5
45
-7
6
5
55
-8
5
5
65
-9.5
3.5
5
75
-10
3
5
85
-9
4
5
95
-9
4
5
105
-8.5
4.5
15
5
-9.5
3.5
15
15
-10
3
15
25
-12
1
15
35
-9.5
3.5
15
45
-7
6
15
55
-5
8
15
65
-6
7
15
75
-7
6
15
85
-9
4
15
95
-10
3
15
105
-11.5
1.5
25
5
-8
5
25
15
-8.5
4.5
25
25
-8
5
25
35
-8
5
25
45
-6
7
25
55
-14
-1
25
65
-15
-2
25
75
-14
-1
25
85
-10
3
25
95
-9
4
25
105
-11.5
1.5
35
5
-10
3
35
15
-10
3
35
25
-8
5
35
35
-11
2
35
45
-7
6
35
55
-7.5
5.5
35
65
-11
2
35
75
-9.5
3.5
35
85
-13
0
35
95
-13
0
35
105
-12
1
45
5
-9.5
3.5
45
15
-11
2
45
25
-9.5
3.5
45
35
-11.5
1.5
45
45
-8.5
4.5
45
55
-10
3
45
65
-10
3
45
75
-11
2
45
85
-10
3
45
95
-8.5
4.5
45
105
-9
4
55
5
-8.5
4.5
55
15
-10
3
55
25
-12
1
55
35
-10
3
55
45
-12
1
55
55
-12
1
55
65
-15
-2
55
75
-15
-2
55
85
-10
3
55
95
-5.5
7.5
55
105
-5.5
7.5
65
5
-10
3
65
15
-10
3
65
25
-10
3
65
35
-6.5
6.5
65
45
-7
6
65
55
-5
8
65
65
-7
6
65
75
-10
3
65
85
-14
-1
65
95
-11
2
65
105
-4
9
75
5
-11
2
75
15
-10.5
2.5
75
25
-9.5
3.5
75
35
-5
8
75
45
-0.5
12.5
75
55
-0.5
12.5
75
65
-1
12
75
75
-4
9
75
85
-12
1
75
95
-11
2
75
105
-7
6
85
5
-11
2
85
15
-11.5
1.5
85
25
-9
4
85
35
-6
7
85
45
-8
5
85
55
-10
3
85
65
-9
4
85
75
-7
6
85
85
-4
9
85
95
-4
9
85
105
-10
3
95
5
-10
3
95
15
-14
-1
95
25
-12.5
0.5
95
35
-11
2
95
45
-11.5
1.5
95
55
-10.5
2.5
95
65
-9.5
3.5
95
75
-11
2
95
85
-12.5
0.5
95
95
-13
0
95
105
-8
5
105
5
-7.5
5.5
105
15
-7.5
5.5
105
25
-7.5
5.5
105
35
-7.5
5.5
105
45
-7.5
5.5
105
55
-7.5
5.5
105
65
-7
6
105
75
-7
6
105
85
-7
6
105
95
-8
5
105
105
-7.5
5.5
115
5
-7
6
115
15
-7
6
115
25
-7
6
115
35
-8
5
115
45
-8
5
115
55
-8
5
115
65
-8
5
115
75
-7.5
5.5
115
85
-8
5
115
95
-7.5
5.5
115
105
-7.5
5.5
125
5
-13.5
-0.5
125
15
-10
3
125
25
-6
7
125
35
-9
4
125
45
-11
2
125
55
-13
0
125
65
-13
0
125
75
13.5
26.5
125
85
-14
-1
125
95
-15.5
-2.5
125
105
-17.5
-4.5
135
5
-12.5
0.5
135
15
-9
4
135
25
0
13
135
35
0
13
135
45
-8
5
135
55
-11.5
1.5
135
65
-10.5
2.5
135
75
-12.5
0.5
135
85
-15.5
-2.5
135
95
-16.5
-3.5
135
105
-16.5
-3.5
145
5
-13
0
145
15
-5
8
145
25
0.5
13.5
145
35
-1
12
145
45
-6
7
145
55
-7
6
145
65
-5
8
145
75
-10
3
145
85
-14
-1
145
95
-13
0
145
105
-12
1
155
5
-12
1
155
15
-9
4
155
25
-9.5
3.5
155
35
-11
2
155
45
-10.5
2.5
155
55
-9.5
3.5
155
65
-10.5
2.5
155
75
-13
0
155
85
-13
0
155
95
-13.5
-0.5
155
105
-10
3
165
5
-10
3
165
15
-10
3
165
25
-9.5
3.5
165
35
-8
5
165
45
-8
5
165
55
-8
5
165
65
-8
5
165
75
-8.5
4.5
165
85
-8
5
165
95
-9
4
165
105
-10.5
2.5
175
5
-9.5
3.5
175
15
-9.5
3.5
175
25
-8
5
175
35
-8
5
175
45
-8
5
175
55
-8
5
175
65
-8
5
175
75
-8
5
175
85
-9
4
175
95
-9.5
3.5
175
105
-9
4
185
5
-13
0
185
15
-12
1
185
25
-12
1
185
35
-12
1
185
45
-4
9
185
55
-4
9
185
65
-4
9
185
75
-11
2
185
85
-9
4
185
95
-11
2
185
105
-13
0
195
5
-11
2
195
15
-12
1
195
25
-13
0
195
35
-12
1
195
45
-4
9
195
55
-4
9
195
65
-4
9
195
75
-12
1
195
85
-11
2
195
95
-13.5
-0.5
195
105
-12.5
0.5
205
5
-10
3
205
15
-10
3
205
25
-10.5
2.5
205
35
-12
1
205
45
-4
9
205
55
-4
9
205
65
-7
6
205
75
-12
1
205
85
-10.5
2.5
205
95
-10
3
205
105
-11.5
1.5
215
5
-8
5
215
15
-9
4
215
25
-9
4
215
35
-10
3
215
45
-9
4
215
55
-12
1
215
65
-9.5
3.5
215
75
-9
4
215
85
-8
5
215
95
-9
4
215
105
-9
4
Discussion:
Our landscape that we created, complete with a basin, a riverbed, mesa, and plains were decided because of the dynamic environment that it represented. Most of these features do not occur together, so once the points are imported into ArcMap, the unique surroundings can be manifested in a way that creates a visually appealing setting. Our decision to make the sea level for Z coordinates at -13 centimeters is a decision that should assist in the data importation in the next lab. The collection of the points was difficult with the temperature being -10 degrees at the point of landscape creation and surveying. Several breaks had to be taken in order to warm up, which caused the collection process to take longer than expected. But thankfully, this was the only issue that we ran into, and the rest of the procedure was simple and uncomplicated.
Conclusion:
Creating the landscape to be surveyed, and establishing the coordinate system that needed to be used in order to accurately record the features that were built was a fairly straightforward process. Despite the cold weather of -10 degrees during the point of collection, accurate measurements were attained, and it should be simple to transfer these points the Excel sheet to ArcMap.
.jpeg)
.jpeg)
.jpeg)
.jpeg)
