Exercise 4-Azimuth

Introduction

For this exercise we practiced another form of surveying. While the original Cartesian coordinate system, and measuring methods we created may have been good for the area we first surveyed (the sandbox), it does not lend itself so easily to larger areas. For this project our group was given a few sets of tools and told to survey a larger, quarter-hectare plot, area where we should measure at least fifty points. As a group, myself and Zach Robison, we decided to team up with another pair, Zac Womeldorf and Hannah Bristol, to collect similar points, but to use two different methods of data collection.

Methods

The first step was to become acquainted with our options for survey tools. The first option was a laser device called a TruPulse 360 (Figure-1). The TruPulse emits an invisible infrared laser which measures the distance and azimuth to a point from where you are standing. The second option was to use a compass accompanied with a ComboPro distance finder (Figures 2,3). This way we could measure the azimuth with the compass, and the distance with the ComboPro.

Figure 1-TruPulse360 and example of reading for distance purposes.

Figure 2-ComboPro Distance finder.

Figure 3-Compass used in conjunction with ComboPro to determine azimuth.

Figure 4a-Photo of data collection.

Figure 4b-Data collection

Zach Robison and myself decided to use the compass and ComboPro to take a few points outside Phillips hall. This provided us with a small sample to ensure we were able to understand and fully grasp the uses of each device.

Once we mastered the technique, we teamed up with Zac Womeldorf and Hannah Bristol to survey points on Upper campus on the north side of Horan dormitory (Figure-4). We chose this location because it seemed to give a plethora of points to survey, as well as was about the right amount of area to fit our requirements. It also seemed to be fairly out in the open, so if need be, we could check out the area from aerial photographs or in satellite images. That way, we could also compare our final data with images of the area so see how accurate or inaccurate the methods of survey were.

FIGURE 4

We then used the distance finder and compass to measure out 32 individual points within this area. Unfortunately we were unable to take 50 points because of the interference of the first 32 points we took. Trees were often blocking other objects (trees, fence posts, signs etc.) so it made reading them with the distance finder impossible. For a full list of the points surveyed, see below (Figure-5).

Figure 5-Final Data collection in excel format, Ready for import into GIS

Upon returning we were able to convert the written points and survey data into an excel table (Figure-5). We then imported this into a created geodatabase, put the table into ArcMap, and ran a spatial tool called the Bearing Distance to Line tool to map the locations of our points. This took takes the origin and uses the azimuth and distance field to calculate what direction and distance the point is from the origin. (Figures 6-10)

One error that we ran into was our spatial reference was skewed when we brought the data in. Our original Latitude and Longitude readings were not precise enough, so our origin wasn’t exactly on our true spot of measuring. We corrected this in ArcMap by using the Identify tool on the basemap provided by Bing!© maps to determine our exact latitude and longitude. This corrected our origin issue, and thus displayed the points more accurately.

Our two groups cooperated together by sharing the data we collected. Since we collected largely the same points, we thought it would be interesting to see the difference in measurements between the two measuring systems. (Zac W. and Hannah B. used the TruPulse to measure their points). We shared our data with them, and they shared theirs with us (Figures 9a, 9b). We went through the same data import process as the first time, and then were able to compare what the differences were (Figure 8). As you can see, the data did differ quite a bit. In the discussion portion we will explore this fact more closely.

Figure 6-Adding the Table in excel to the geodatabase.

Figure 7a-Using the Bearing Distance to Line Tool.

Figure 7b- Using the Bearing Distance to Line Tool.

Figure 8-Data Collection points with no spatial reference. Displayed is both collection methods and the variances between them.

Figure 9a-Data Collected by Compass and ComboPro

Figure 9b- Data collected

Figure 10-Data Points from Both Collection methods overlaid on Bing! maps basemap.

Figure11-Final Compass survey

Figure 12-Final Laser Survey

Figure 13- Compass and Laser survey data

Discussion

As you can see from the provided map, both data collection methods came up with different data as the end result, even though they were mapping the same exactly points.  You can see the error in the fact that the lines do not overlap in all places, and they both terminate in different places. We attribute this to the fact that neither data collection method is going to be perfect. For instance, we were only mapping on a 2D plane, how would the TruPulse or ComboPro react to an elevation change? And would that elevation change even show up in ArcMap?

User error may also be another explanation for point variances. It was cold, snowy and windy, nobody operates perfectly in any conditions and these adverse conditions could easily lead to a misread azimuth or distance measurement.

One source that may create small amounts of error in our survey could be the declination between magnetic north and true north. However, this is unlikely because here in Eau Claire our declination adjustment is nullified by the fact that we sit almost exactly south of both magnetic north and true north. It did, undoubtedly play a very small role in error in our data collection, though very small.

Although neither method proved to be perfect, both had good advantages. Using the ComboPro and compass was a very simple and easy method. Not a lot of equipment could have broken or been malfunctioning while in the field. The compass always will work because it doesn’t need batteries. This is perfect for situations where you can’t always replace certain items in the field, short of losing it, a compass will always work for you.

However, in more recent times the compass has been replaced with more technologically advanced survey methods, just like the TruPulse. It also is easier to go into a GIS like Google Earth and measure out points with the Distance and Declination tools. A survey like that doesn’t even require you to leave your seat.

Conclusion

In the end we are left with a Map looking something like what you see in Figure 10. The tools and skills we gained from this exercise definitely helped us create a new skill-set for surveying and displaying a plot of land. I found the exercise to be a great learning experience and know it will prove to be helpful in the future. Plus learning two different methods for survey was helpful to have a backup plan if one system were to fail while working in the field.

Balloon Rig Step 1

Introduction

This exercise is simply step one of a much larger project for this Geospatial Field Methods class. For this class we have the exciting opportunity to work with aerial photo data collection via use of balloon based ‘aircraft’. As of now, the plan is to create two balloon rigs with cameras attached so we can collect aerial photographic information; one rig is to be attached to a rope, so we walk around campus to give ultrahigh resolution photos of campus area, the second rig is for launching higher up in the earth’s atmosphere to provide an aerial photos from a wider study area. This second rig is called a High Altitude Balloon Launch (HABL).

This week we spent some time working with different materials and brainstorming best ways to create these two rigs. We know that each rig is going to be different and have different requirements when it comes to factors like insulation and/or camera requirements. Our job was to brainstorm and come up with the best rigs we could, and document each step along the way to provide a detailed instruction list for others who may wish to follow our study/experiment. There are many different steps along the way to creating these rigs, and each person was able to choose what part of the project they wanted to work on, which is explained further in the methods.

Methods

As stated earlier, there are many steps in the process of creating a successful balloon rig so the tasks were split up into seven basic categories:

1.      Construction of mapping rig

2.      Construction of HABL rig

3.      Parachute testing,

4.      Payload weights of both HABL and mapping rig

5.      Design of implementing continuous shot on cameras

6.      Implementation and testing of tracking device,

7.      Filling of balloon and securing it to the rig.

This proved to be a difficult task to have all seven aspects going at the same time. A lot of materials overlapped and were needed for both rigs so we focused most energy on construction of the camera housing. Also proving to be difficult was to calculate the payloads of each rig. I worked most closely with the group devoted to the payload weighing so that’s where this report will focus.

It is obviously extremely important to know the total payload for each rig, as this factor has everything to do with everything. As a group we got a scale from the geography department and began to weigh the materials we could get our hands on. One issue is that our scale was so sensitive we had to move to an area where people wouldn’t be constantly bumping the table and throwing off the weight read-outs.

We moved to a more isolated place and began to weigh everything we possibly could. There is an exhaustive list associated with the final weight measurements, but in general here is what was measured:

Balloons	Carabineers	Cameras	Bottles	Handwarmers	Memory Cards
Parachutes	Rubber bands	Rope	Zip-Ties	Camera Housing	Styrofoam

We of course, had many types of each of the above items but those are the general categories in which each item fell.

Along with weight measurements, we took pictures of each item and gave each item detailed description so that we were sure not to mix anything up in the end. (Figure 1 and 2)



Figure 1:Measurement process of all items to be included or used on balloon rigs

 



Figure 2:Measurement process of all items to be included or used on balloon rigs

I think it was valuable to weigh all the things from the beginning even though some items may get altered as the rigs are built. This way we can look at weights and estimate if things are going to be too heavy in the end. It would be a waste of time to create a rig that wound up being too heavy in the end because we were unable to tell how much each contributing item weighed. This way we can at least get a ballpark figure as to the end weight before we create a rig.

 

Discussion

The fact that we have a small group of students in the class made this activity run fairly smoothly. Each person was able to tackle a different, necessary piece of the project without bumping elbows with other groups too frequently.

We will have to be careful as we move along to ensure that our weight measurements maintain the same level of accuracy as we used in the first part. This can be achieved by using the same scale as we did the first time, and updating measurements as items are altered to best fit our rig (ex. Cutting apart a 2-liter soda bottle).

I also think it will prove to be valuable in the end that we are so thoroughly documenting all of our processes along the way. I have a feeling that this will pay off after the project is finished. We can examine what we did right or wrong and know how to best fix it.

Conclusion

I think this was a valuable exercise, which will hopefully be the first step towards a larger and more fruitful experiment when we are able to launch both our rigs. I look forward to seeing how this first piece plays into the larger puzzle, and to hopefully make use of our thorough documentation along the way.

This was a fun project as well. Just as discussed in class, we are using a pile of what appear to be workroom scraps that will prove to be an exciting experiment that will hopefully provide some useful data that we can use to further analyze our study areas in a spatial and GIS setting.

Figures

Figure 3: Final weight record table.

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

Figures 3-27

Figure 3: Blue Carabiner with Key ring

Figure 4: Silver Carabineer with loop

Figure 5: Cut Coke Bottle top #2

Figure 6: Cut Coke Bottle top #2

Figure 7: Styrofoam Insulation post-cut out

Figure 8: Coke Bottle, empty w/ top

Figure 9: Peanut buttle jar, empty w/ NO lid

Figure 10: Empty Rain-ex Bottle. NO Lid.

Figure 11: Handwarmers pack of 2 in package.

Figure 12: Zip Ties. Above-Small. Below-Large

Figure 13: Large, Long, Thin Blue Rubber Bands

Figure 14: Large, Long, Thin Blue Rubber Bands.

Figure 15: Large, Long, Thin, Tan, Rubber Bands

Figure 16: Large Long, Thin, Tan Rubber Bands

Figure 17: Large Long, Thin, Black Rubber Bands

Figure 17: Large Long, Thin, Orange Rubber Bands

Figure 18: Large Long, Thin, White Rubber Bands

Figure 19: Large, Long, Thick, White Rubber Bands

Figure 20: Medium Lengh, Black, Thin Rubber Bands

Figure 21: Parachute

Figure 22: One Meter of pink synthetic string.

Fiure 23: Nylon Rope

Figure 24: Rope Label.

Figure 25: Short, Thick, White rubber bands

Figure 26: Short, Thin, Orange Rubber Band

FIgure 27: Yellow Cord with buckle.

Figure 3-

Improved Sandbox Activity

Introduction:

For this revision of our first lab we expanded on our findings from exercise one. The point of this exercise was to try to see what we did well and what we did wrong, then fix any errors that we hay have made in the first exercise. As stated in the first lab write up, fieldwork is where we can collect this data to then observe and analyze later. This lab was focused specifically on the collection and surveying process that is part of any land analysis, and the addition of this data into a GIS for further analysis.

Methodology:

For the first part of the project we were tasked with creating a landscape within our sandbox (for a detailed outline of this process see Lab write-up #1). Then we collected the data and recorded it to put into the GIS when returning (again, detailed outline available in Lab write-up #1).

Since we recorded our data in a graph format of X,Y data it was necessary to convert this by hand into a table format X,Y data so that we could import it as X,Y data in ArcMap. See Figures 1 and 2.

Figure 1:  Raw data collected from first sandbox modeling attempt. Data is in 'graph' format, unusable when importing into ArcMap

Figure 2: Fixed data, now in X,Y format and will be useable for importing into ArcMap. Notice first measurements are listed under Z category, and second measurements are listed under the Nameless category (later renamed Z2).

Next we exported the data as a shapefile and then put that shapefile into our map project. We used this shapefile to create a raster image thourgh the Spatial Analyst tools of Interpolation. Through trial and error I determined that the Kriging method would be the best method for creating the raster file. See Figures 3, 4 and 5.

Figure 3: Data points overlaid on a created raster image.  Dots represent each individual point of data collection. Raster created through trial and error to determine Kriging method as best interpolation

Figure 4: Original measurements Raster file. Created through Kriging method. Notice high values in yellow/red and lower values in green and dark green. 

Figure 5: Second modified measurements Raster file. Created through Kriging method. Notice high values in yellow/red and lower values in green and dark green.

After creating the raster file for the first and second measurements we exported these rasters into ArcScene so that we could create a three dimensional image of the surface of our sandbox. By floating the layers on the Z data surface we were able to create a three dimensional model of the surfaces. See Figures 6, 7 and 8.

Figure 6: Original ArcScene three dimensional model.  Notice high values in yellow/red and lower values in green and dark green. 

Figure 7: Second modified ArcScene three dimensional model.  Notice high values in yellow/red and lower values in green and dark green. 

Figure 8: Side by Side comparison of topographical differences between orignal model and second model. Notice the differences in elevations and between the two. 

With these three dimensional models we have accurately mapped the surface we were working with, and now that this surface is in the GIS we can further analyze it for whatever purposes we deem necessary.

Discussion:

It was beneficial to go back and improve our original data as you can see in Figure 8 it clearly made a difference in our models. Working with an incomplete or inaccurate data could lead to huge mistakes, depending on what type of analysis you are taking part in. Our revised model was far more topographically diverse and I feel it best reflected the surface in real life.

As stated in the previous lab write-up, I think it was important that we used only the necessary materials to prevent any excess baggage in the field, and that we had a backup ‘dumb’ method in mind if needed.

Also similar to the first time, I think we would have benefitted from measuring at a higher accuracy, however the time and weather did not permit us to do so.

Conclusion:

This revision was valuable because it taught us a couple things. First it taught us the process of further developing our surface within a GIS. Knowing how to create a raster, and an ArcScene model will be very helpful in the future in this class, and is a vital part of learning field methods.

This exercise also taught us the importance of careful measuring and how this might change our end results. You can see in our final models (Figure 8) how they vary quite a bit.

Overall this exercise was a good expansion upon the first exercise and should prove to be a valuable learning base for future exercises.