Field Activity: Conducting a Distance Azimuth Survey

Introduction
GIS, GPS, and survey technology have progressed astoundingly far in recent years. With this technology, it is now very easy to conduct an accurate survey. However, technology is not infallible. The survey equipment may break, the GPS could be seized at the airport, or maybe the battery refuses to charge. As unlikely as it may be, statistically speaking, something will stop working eventually. To prepare for this eventuality, it is necessary have an effective backup plan. One such option, a low-tech survey technique known as azimuth surveying, was conducted for this lab. In azimuth surveying, a single GPS point is taken, known as an origin, and data points are collected around this origin. The locations of these additional data points are referenced with a corresponding distance from the origin and azimuth bearing measurement. By using one or several origin points in an azimuth survey, the exact GPS coordinates do not need to be collected.
Study Area
The study area, Putnam Park, sits between upper and lower portions of the University of Wisconsin, Eau Claire campus. To avoid redundancy, the imagery of the survey area is included within the results section of this post. It is well known for the large variety of trees within it, do to the varying elevation and soil moisture produced by the steep slope. To practice azimuth surveying, ten trees would be surveyed around each of three origin points using three separate techniques to measure distance and azimuth, for a total of thirty surveyed trees. These points would then be imported into ArcMap for analysis.
Methods
First, three origin points were taken within Putnam Park using a personal GPS Unit. From the GPS, their x,y coordinate were recorded as "91.50034,44.79544", "91.49913, 44.79547", and "91.5017, 44.79642". This, along with the distance to each tree from the origin, the azimuth bearing, and the circumference of each tree were recorded for each surveyed tree in a data table (Figure 1).

Figure 1: The electronic Excel file datatable of all thirty trees surveyed in Putnam Park. The recorded data includes the x and y GPS coordinates of the corresponding origin, the distance in meters each tree is from its corresponding origin, the azimuth bearing of each tree from its corresponding origin, and the circumference of each tree surveyed. A column for the corrected x GPS coordinates was included, as it had to become negative in value before it could be properly imported into ArcMap.

The circumference of the tree was used as a stand-in for the tree species, as the species of each tree would be difficult for non-biology students to determine at this time of year (March 2015).
From the first origin point, ten trees were surveyed by using the GPS, a compass, and two measuring tapes. This was by far the most low-tech survey method used during this field activity. By looking through the compass and aiming it at the surveyed tree, the azimuth bearing could be recorded from the compass (Figure 2).

Figure 2: A surveyor recording the azimuth bearing of a tree
from its corresponding origin point using a compass.

The distance from each tree to the origin was recorded by measuring the distance using the longer measuring tape. The second measuring tape was utilized to measure the circumference of each surveyed tree (Figure 3).

Figure 3: A surveyor recording the circumference of a tree
using a measuring tape.

This was done for ten trees circling the first chosen origin. This is the most low tech survey method that was used, the GPS being the only electronic device necessary. This makes it the cheapest method with the fewest electronic components that may fail. However, it is also the slowest survey method with the greatest possibility for error. As the measuring tape must be physically stretching out between the origin and the survey tree, measurement is often hindered by such things as branches, bushes, and downed trees.
The second survey method, for the second origin and its corresponding ten surveyed trees, was a little more advanced. The measuring tape used to record the distance between the survey trees and the origin was instead replaced with a two part electrical device. One component of the device is aimed from an individual standing on the origin at the other component (Figure 4).

Figure 4: A surveyor aiming the first component of the two-
part distance measuring device from the the origin to the
survey tree.

The other component is placed on the survey tree by a second individual (Figure 5).

Figure 5: A surveyor holding the second component of the
two part electronic distance measuring device. The device
is being held against the trunk of the survey tree and is ready
to receive a signal.

The first component sends a signal toward the second, which is received by the second and sent back to the first, along with data recording the distance between the two components. Measurements for the tree circumference and the azimuth bearing were recorded as they were for the first survey method. This way, there is no need for a physical measuring tape to be stretched between the origin and survey point. This allows for faster surveying, at the cost of needing a more expensive electronic device. However, this method still requires multiple individuals in order to measure the distance between the origin and the survey tree.
The final group of survey points around the final origin were surveyed using a special, one-piece laser device. By aiming it at the target survey tree, it sends a laser signal which bounces back and is received by the same device (Figure 6).

Figure 6: A surveyor utilizing the single piece laser to
measure both the distance from the origin to the survey tree
and the azimuth bearing.

Not only does this measure the distance between the origin and the survey tree while eliminating the need for more than one surveyor, it also records the azimuth bearing of the targeted survey tree. This is by far the fastest way to record data used in this field exercise. However, this device is tremendously expensive. Thus, few organizations will have access to such technology, and great care should be taken with bringing this technology into the field.
Once the trees had all been surveyed, the data was converted into an excel spreadsheet. An additional column was added for the x origin data, as it needed to be properly formatted as negative values before being brought into ArcMap. If it wasn't, the points would likely be displayed somewhere in Asia in ArcMap. Once this was complete, the data was imported into ArcMap as X,Y data. Then, the Data Management tool Bearing Distance to Line was utilized to convert the datatable into a geodetic line feature class. Once this was completed, the Data Management tool was utilized to convert the end vertices of the geodetic line feature (the trees) into a point shapefile. Utilizing a table join, all of the data from the original excel table was reconnected to the tree survey points. This allowed for the tree point shapefile to display the circumference of the surveyed trees. This was utilized to create a map.
Results

Figure 7: A map displaying the trunk circumference of the surveyed trees in relation to their respective origin points. The survey was completed utilizing a Distance Azimuth Survey and utilized three techniques with varying levels of technology to obtain measurements. The location of the survey is Putnam Park, located on the University of Wisconsin, Eau Claire campus, and was completed on March 15, 2017.

After analyzing the data, it can be inferred that trees of both great and small relative circumference exist in fairly close proximity to one another (Figure 7). The very largest trees (in terms of circumference) are located along the southern and eastern portions of Putnam Park, furthest away from campus. Inferences on the levels of accuracy and precision of this survey technique can also be made from this map. While the grouping of trees around each origin is fairly similar to what actually exists, meaning a high level of precision, the accuracy of the data leaves much to be desired. The survey points in the northwestern portion of the map are roughly twenty-five meters farther north than their actually positions. The central survey points and their corresponding origin, likewise, are ten meters too south of their actual position. Only the third group of survey points remain in an area close to (within two meters) of their actual position.
Conclusion
While azimuth surveying leads much to be desired in terms of accuracy, its preciseness still makes it a valuable technique to know for anyone dealing in field survey methods. Remote sensing and increasingly powerful, modern GPS systems, while having replaced Azimuth surveying, are liable to equipment failure. So when these systems fail and when accuracy is not always an issue, distance azimuth surveys will continue to exist as a backup plan. By combining it with point-quarter sampling and properly recording species, other valuable data can be gathered. this includes density measurements, frequency, determining species coverage of an area, and calculating the importance value of surveyed species. Azimuth surveying, while dated, will continue to exist.
Sources
Hupy, J. (2017). Field Activity #4: Conducting a Distance Azimuth Survey. Eau Claire, WI.

Teh, S. (2017). Biology 3A: Ecology: Point-Quarter Sampling. Saddleback University. Available online at http://www.saddleback.edu/faculty/steh/bio3afolder/PointQuarter%20Lab.pdf

Processing UAS Imagery with Pix4D

Background
Having already worked with data acquired from Pix4D, this lab served to introduce the software and walk through the processing of data using Pix4D. In the process, a mosaic would be generated of the Litchfield Mine in Eau Claire, Wisconsin. The use of UAS as a form of data collection has increased in recent years, and Pix4D is one of the leading software programs for processing this data. Pix4D works by analyzing multiple aerial photographs of an area for similarities and keypoints. Once these points have been located, the software combines the images into 3D cloud points and mosaic rasters. However, there are several critical points that must be discussed before utilizing the software.

• What is the overlap needed for Pix4D to process Imagery?
• The overlap required to process imagery is 75% frontal overlap and a minimum of 60% sidelap. High overlap is critical to getting accurate results. Thus, data acquisition must be planned to maximize overlap (Figure 1).

  

  Figure 1: An ideal flight plan for maximizing overlap.

   
• What if the user is flying over sand, snow, or uniform fields?
• When a survey area is comprised of large, invariable areas, it becomes more difficult for the software to properly match images. When flying over sand, snow, and fields, it becomes necessary to increase the overlap to compensate. In these areas, the minimum requirements are 85% frontal overlap and 70% sidelap.
•  What is Rapid Check?
• Rapid Check is a feature that allows for the quick verification to see if the flight settings and parameters were formatted correctly and will result in the creation of a raster. It does this by reducing the image resolution to 1 megapixel to decrease process time. If the Rapid Check fails, it is recommended that the flight be redone over the survey area. If the Rapid Check succeeds, full processing may commence. It is critical to return to full processing, as reducing the resolution to one megapixel results in a decrease in positional accuracy, which may negatively affect results.
• Can Pix4D process multiple flights? What does the pilot need to maintain if so?
• Pix4D can indeed process multiple flights. However, several criteria must be met.
• The flight plan must collect enough overlap for each image.
• The flight plan must collect enough overlap between the two or more images (Figure 2).
• The flights must be flown at the same altitude, in the same or similar conditions (sunlight, weather, etc.), and within a close enough time frame that the surface features have not changed.

Figure 2: A depiction of two flights with enough overlap for Pix4D (left), and a depiction of two flights without enough overlap for Pix4D analysis (right).

• Can Pix4D process oblique images? What type of data do you need if so?
• Yes it can. In order to do so, there needs to be multiple flights at multiple camera angles. One above the object or site, one at a 45 degree angle, and one at a 90 degree angle
• Are GCPs necessary for Pix4D? When are they highly recommended?
• GCPs are not necessary for Pix4d. However, having them does increase the positional accuracy and georeferencing. GCPs should be used when accuracy is of the utmost concern. This is typically done for city and street construction, corridor mapping, or other such urban construction plan where accuracy is critical
• What is the quality report?
• A quality report is a summary file that is created after the initial processing is finished. From the quality report, a variety of information can be gathered a preview of the image mosaic and a number of initial processing details.

Methods
First, a new project was created in Pix4D. It was titled "20160621_litch_krismejr_phantom3_60m" based on the date of the survey, the site, the sensor, the altitude, and the project creator. Then the images from the flight over the Litchfield Mine were added to the project. The camera model settings were edited so the Shutter Model read as Linear Rolling Shutter. All other camera settings were left as default. The output coordinate system was left as default, the processing template was set as 3D Maps, and the project setup was finished. The DSM, Orthomosaic, and Index processing options were changed to triangulation in the Raster DSM option. The initial processing was started and completed, with a quality report being generated afterwards. According to the summary all 68 of the images were used, with none of them being rejected (Figure 3).

Figure 3: The summary taken from the quality report after the initial processing. All 68 of the images take of the Litchfield were used in the initial processing, with none of them being rejected.

Also taken from the quality report, the majority of the image overlap occurs in the center, with the areas of low overlap being around the edges (Figure 4).

Figure 4:Number of overlapping images computed for each pixel of the orthomosaic. Red and yellow areas
indicate low overlap for which poor results may be generated. Green areas indicate an overlap of over 5 images
for very pixel. Good quality results will be generated as long as the number of keypoint matches is also
sufficient for these areas.

This makes sense, as the aerial sensor only rarely passes by the edge and flies over the center of the survey area multiple times. Afterward reviewing the quality report, the "Point Cloud & Mesh" and "DMS, Orthomosaic and Index" final steps were initiated. When this was completed, a triangle mesh. Using the built in functionality of Pix4D, a digital flyover of the mesh was created and exported as a video file. Finally, the raster files generated in the final step were brought into ArcMap and used to create cartographic ally pleasing maps of the Litchfield Mine, projected in the WGS 1984 UTM Zone 15N coordinate system and the Transverse Mercator projected.
Results

Figure 4: A raster imagery map depicted the Litchfield Mine in Eau Claire Wisconsin (right) with a reference map (left). 

Figure 5: A raster elevation map depicted the Litchfield Mine in Eau Claire Wisconsin (right) with a reference map (left).

Based on comparisons made between the imagery mao (Figure 4) and the elevation map (Figure 5), several inferences can be made. The highest elevation areas are mounds of soil and dirt built up over the course of the mine's operation. The flat areas are reserved for the mined areas, which have been reduced to relatively featureless areas of exposed earth. The lowest elevation areas are the forested areas which ring the mine site.

Conclusion

Pix4D has proven to be a available tool for a remote sensing analyst. Even without the use of GCPs, Pix4D was able to collect multiple photographs taken by a UAS over the Litchfield Mine to both create a 3D triangular mesh and a series of raster images which could be converted into maps. In the future, Pix4D could be utilized to construct something similar over a larger area with multiple UAS flights, or create a highly detailed and spatially accurate map with the use of GCPs in order to aide to city construction.

Sources

Hupy, J. (2017). Construction of a point cloud data set, true orthomosaic, and digital surface model using Pix4D software. Eau Claire, Wisconsin.

Using Survey 123 to Gather Survey Data

Background
The portable phone has come a long way. In a few short decades, it has gone from just being able to make calls to functioning as a personal computer, GPS unit, and field collection device in addition to serving as a primary means of communication. AS proof of this, the primary function of this lab was to introduce and demonstrate Survey123, an Esri designed application for gathering survey based field data either from a computer or a personal smartphone. To due this, a sample survey would be constructed. This sample survey would focus on collected data for the 9 "Fix-its", as determined by Challenging Risk, complete in order to better prepare a household in the event of an earthquake or other similar disaster. The surveyor would be answering a series of questions as to whether or not their home has these safety precautions in place. If this survey were to be actually be used, HOA would use it to determine the preparedness of its members in the event of a disaster.
Methods
First, a new survey was created in the "From Survey123 Web" selection of the Survey123 Website. After titling, tagging, and creating a proper summary for the survey, "Create" was selected. The first question that was added was a required Date question requesting the date of the survey completion date, with it defaulting to the submission date. A Singleline, required question asking for the participant name was then created. A Singleline question and Geopoint question were created, asking for the participant's address. A series of questions were created (Figure 1), asking for: the participant's type of residence, levels of the residence, a picture of the residence, number of people who live in the residence, and age of people living in the residence.

Some of these questions only appeared as part of a rule if other questions were answered a specific way. For example, the question asking for the levels of the residence only appeared if the previous question asking for the type of the residence was answered as Single family (house). From here, the nine safety check questions were created (Figure 2):

Safety check 1: Are televisions in the home secured?

Safety check 2: Are computers in the home secured?

Safety check 3: Are bookcases secured to the walls?

Safety check 4: Are large cabinets secured to the walls?

Safety check 5: Are any objects placed above sofas and beds?

Safety check 6: Are all exits (doorways to outside) clear of obstruction?

Safety check 7: Are functioning smoke alarms present in each room?

Safety check 8: Are there fire extinguishers in the home?

Safety check 9: Verify there are no overcharge plugs in the home

Four more rule based questions were created depending on if the surveyor answered yes to specific questions:

• 1: Yes. How are they secured?
• 2: Yes. How are they secured?
• 7: Yes. When were they last tested to be in working order
• 8: Yes. How many extinguisher units?

A few final questions were added, asking if anyone in the household was trained in First-Aid, inquiring on if certain items which could be used in an emergency were in the house, asking if the surveyor has an up to date emergency contact list, inquiry to the existence of evacuation and community disaster plans, and one final Multitext question asking for any additional comments. After previewing the survey, it was published.

Once the survey was completed, it was shared with every member of the linked organization. Afterwards, the survey was opened and completed as an initial trial run on a computer browser. While this is one way to complete the survey, the most effective way to distribute and gather data on a survey like this is through a mobile device. The Survey123 mobile app was downloaded, and the survey was completed again using a mobile device (Figure 3).

In order to vary up the survey results, a previous residence was used as the source of the survey data. A third trial of the survey was completed on a mobile devise, using the residence data of a nearby relative. In order to analyze the survey data, at least eight surveys needed to be collected. A fourth survey was completed, using a previously lived in residence hall as the survey point, then four students were asked to complete the survey, as to provide surveys that were not randomly generated. However, the main purpose of this activity was to introduce survey construction through Survey123, not to build an accurate dataset and deeply analyze it. As of such, the students were instructed to not worry if they did not know the answer to a required question on the survey.

In the "My Survey" section of the Survey123 Website, it is possible to analyze the data retrieved from the survey. A multitude of options exist for analyzing the data. It can be viewed in various graph formats (Figure 4), numerical, or as data points on a map (Figure 5).

Afterwards, the data export options were explored.

The survey data can be exported as a CSV file, a shapefile, or as a File Geodatabase. To test it, the survey data was exported as all three. Finally, the data was shared as both a map and a custom web map with members of the joined organization. A map was constructed using the provided ArcGIS map viewer. The map displayed the survey points, as well as the answers from most of the questions in survey as a pop-up window when selecting a point. A question omitted from pop-up display was the survey question asking for the participant's name. Afterwards, the map was saved, with the finished map being properly title, tagged, and summarized. The map was shared as both a map and a Basic Viewer Web App with all members of the organization (Figure 6).

Results
All of the results were taken from college students, with the majority being about their college residence. Out of all the surveys completed, six were in the Eau Claire area. The other two were taken of a home residence and from the information given about a relative's residence. From the survey data, several clear patterns are distinguishable regarding whether residences do or do not follow the nine guidelines given by Challenging Risk, in the event of a disaster. Only one of the eight residences has secured televisions, only three have secured computers, only two have secured bookcases, only three have secured cabinets, and five have objects placed above the sofas and beds. In contrast, six of the residences have clear paths to exits, six have functioning fire alarms, and five have fire extinguishers. Half the residences also have overcharged outlets. These patterns seem indicative of students who live in dorm rooms. Much of the furniture is provided. However, due to restrictions placed by the university, furniture can often not be secured to the wall. Its designed for ease of movement in mind as students rearrange their rooms often. However, the benefit of living in the dorm is that the university provides and maintains fire extinguishers and smoke alarms while keeping exists being blocked.

Figure 6: An interactive map display, HOA Emergency Preparedness Survey Results. showcases the survey data of each of the collected survey points. This map was distributed as a Basic Viewer Web App and was shared with all member of the UWEC ArcGIS organization. This map is available for viewing by all members of the organization at https://arcg.is/1G8SHb.

Conclusion
Survey123 is a valuable tool for the field survey analyst. With it they can take data collection on the go and submit it for easy and effective analysis late.An ecologist could be seen  using this to collect plant health data from various vegetation ecology zones, use it to collect and submit soil sample data from fields and forests, or even collect population samples of fish taken from different segments and tributaries of a river system. The ability to take and submit electronic survey data in the field also elliminates potential sources of error generated when digitizing the data, as the collected survey can be downloaded afterwards in a number of formats. In addition, it allows for a level of direct data analysis. Survey123 may not contain the complex statistical algarithms required to perform proper statistical anlysis on ecological and and population data, but it does allow for the viewing and distribution of base level greographical patterns. 

Sources
Krismer, J. (2017) HOA Emergency Preparedness Survey Results. Retrieved 3/8/2017, from https://arcg.is/1G8SHb

Hupy, J. (2017). Using Survey 123 to gather survey data using your smart phone. Eau Claire, WI.

Get Started with Survey123 for ArcGIS. In Learn ArcGIS. Retrieved 3/7/2017, from https://learn.arcgis.com/en/projects/get-started-with-survey123/

Field Activity: Development of a Field Navigation Map

Introduction
In order for an individual to navigate through an area, an individual requires several critical tools. First, they require a way to orient themselves, be it with a GPS, a compass, the stars, or the positioning of the sun in the sky. Secondly, that individual requires a location system, typically a navigation map. This location system is constructed utilizing and projection, usually with an accompanying coordinate system. The purpose of this lab was to develop skills in navigation map construction and increase the understanding of coordinate systems and their projections. To this end, two navigation maps would be constructed of the area surrounding the University of Wisconsin-Priory Hall in Eau Claire, Wisconsin (Figure 1), utilizing the UTM coordinate system and the GCS WGS 1984 coordinate system. These were to be used at a later date for navigating the area.

Figure 1: A reference map showing the University of Wisconsin-Priory Hall location using a Google Maps imagery (left), and a topographical map (right). The navigation map area is denoted by the red rectangle. The address of Priory Hall is 1190 Priory Rd, Eau Claire, WI 5470, and its latitude and longitude are 44.7654109° N, 91.5136289° W.

Background
In order to properly construct the navigation maps, it was critical to first understand the differences between the projection and the coordinate system of a map, and to understand the primary function of each. A coordinate system is the three dimensional representational surface of the globe which uses collected data points to link locations in the real world to points on the coordinates system, which is then used to define locations on earth. Components of a geographic coordinate system include an angular unit of measurement, a prime meridian, and a datum (based off a spheroid). A projected coordinate system utilizes a linear unit of measurement, a map projection, the parameters of the map projection, and a geographic coordinate system. A map projection is the two dimensional representation of an a three dimensional area on a globe. Depending on the area of interest in a study, the corresponding projected or geographic coordinate system must be selected . This is critical to map construction, as a projected coordinate system and a geographic coordinate system are imperfect representations of the real world. When transferred to a two dimensional surface, like a map, distortion occurs in the space outside of the focus area as the three dimensional model is stretched and altered to fit the projection. To compensate, many coordinate systems have been created on national, state, county, and local levels so that distortion can always be kept to a bare binimum, no matter the area of interest.
Methods
First, a new map file created in ArcMap, with the layer properties being set to the "NAD_1983_UTM_Zone_15N". This would ensure all feature classes brought into the map layer would be projected into the NAD 1983 UTM Zone 15N projected coordinate system, with a Transverse Mercator projection. Then, a raster dataset of the Priory and surrounding area, "grdn45w092_13", was incoperated into the map. Utilizing this raster dataset and the necessary tools, both a hillshade and a contour shapefile with a contour interval of two meters were created and implemented into the map. Next, a boundary shapefile of the navigation area was added to the map. This was used to clip the  contour file to only the navigation area. Afterwards, the raster, the hillshade, the navigation boundary shapefile, and the clipped contour were all reprojected into the  NAD 1983 UTM Zone 15N projected coordinate system in order to ensure they were in the correct coordinate system. In the layout view, a measured grid was added to the map layer, with the coordinate system being set to that of the data frame. In addition, the x and y axis intervals were set to 50 meters, and the origin was set to that of the original coordinate system. With the primary map components implemented, the remainder of the work on this map was dedicated to map design. The width and height of the map document were set to 17 and 11 inches, respectively. An RF scale was included. A scale bar was included, with the equivalent value of paces being included, as taken from an average pace count of 100 meters. The average pace count was 66 paces per 100 meters. The raster was set to a partial transparency in order for the hillshade to appear beneath it. The grid and contour lines were both redesigned to make them more easily visble. The coordinate system and projection were included on the map. Traditional map elements like a north arrow, a watermark, and the data source were included. The grid labels were altered to both fit the other map elements and prominently display the 50 m change between intervals. After all reorientation and resizing of the map elements was completed, the map was titled Priory UTM Field Navigation Map and exported.
To contrast the UTM map with a coordinate system that has been more familiar up to this point, a Decimal Degrees Field Navigation Map was created of the navigation area by altering the UTM map. First, the data frame projection was changed to the GCS WGS 1984 geographic coordinate system. Then, all of the relevant feature classes (contour lines, raster, hillshade, navigation area shapefile) were reprojected into this geographic coordinate system. Then, the UTM grid was replaced by a Graticule Grid which used Decimal Degrees and whose origin was set to that of the coordinate system. The interval and grid labels were altered several times till they displayed the changes in Decimal Degrees up to the fourth decimal and at an interval of 1". Similarly to the UTM map, a field map titled Priory Decimal Degrees Field Navigation map was constructed, using many of the map elements of the UTM map. Most elements, like the RF scale and the scale bar, needed only to be resized. The only elements completely changed were the grid, as mentioned previously, the labels for the map's coordinate system and projection, and the title. After resizing and reorientation was complete, the map document was exported.
Results
The UTM Field map was designed primarily for ease of use and to minimize clutter (Figure 2). Rather than use an image of the navigation area for a background, the raster dataset was set as partially transparent with the hillshade behind to simulate the terrain This would allow both the contour lines and the grid lines to more easily appear against the background while still showing the terrain. The contour lines were set at a two meter interval to accurately portray the terrain while also eliminated any excessive contour lines. A traditional scale bar was included. Map elements, like the scale bar, RF scale, pace count, watermark, source list, and coordinate system label, while included, were kept to a minimal size and delegated to the corners of the map to minimize the amount of visual interference associated with reading the grid labels. Speaking of which, the grid lines were set to 50 meter intervals to once again maximize the number of grid lines while still making the map easy to read. The labels were oriented so that all but the top labels were arranged horizontally so that they could easily be read by the navigator. Only the top labels were arranged vertically, as some of the bottom labels were lost when they were arranged horizontally. In addition, the labels were set so that the 50 meter intervals would always be the last or second to last digit displayed. This was again done to maximize readability. The projected coordinate system, NAD 1983 UTM Zone 15N, was created in 1983 and focuses its projection between 96°W and 90°W of North American (Spatial Reference). Since the navigation area falls within this zone, this projected coordinate system is ideal for minimizing distortion of our map. One traditional map element that was not included was a reference map. This is because the navigator will have little use for a reference map, as navigation map will only be utilized from within the navigation area, whose location is already known. It would only serve to create clutter.

Figure 2: A UTM navigation map of the area surrounding the University of Wisconsin - Priory Hall. The projected coordinate system is NAD 1983 UTM Zone 15N, and the grid lines have an x and y interval of 50 meters.

The Priory Decimal Degrees Field Navigation Map (Figure 3) shares many of the same aspects of the UTM map. All map elements of the UTM map, such as the RF scale or the watermark, are included in this map as well. It is designed with ease of readability in mind and utilizes the same raster and hillshade as a background. What has changed is the coordinate system and all map elements relating to this. The map is displayed in the geographic coordinate system GCS WGS 1984, a fairly standard coordinate system created in 1984. There exist a couple problems with this coordinate system. First, when projected, it does not focus on the navigation area. This has resulted in distortion and slight reorientation of the navigation area on the map. Second, by relying on Decimal Degrees as a unit of measurement, the grid labels mark intervals using a unit that is non-optimal for this scale. As a result, all changes between intervals are denoted up to the fourth decimal place of a degree, as this is the scale the map is operating at. Out of the two navigation maps generated, this is the less effective of the two for navigating the Priory area.

Figure 2: A Decimal Degrees navigation map of the area surrounding the University of Wisconsin - Priory Hall. The geographic coordinate system of the map is GCS WGS 1984, and the grid lines have an x and y interval of 1".

Sources
Coordinate systems, projections, and transformations. In ArcGIS Pro. Retrieved 3/8/2017, from http://pro.arcgis.com/en/pro-app/help/mapping/properties/coordinate-systems-and-projections.htm

Google Earth View. Retrieved 3/8/2017, from https://www.google.com/maps/place/University+of+Wisconsin-+Priory+Hall/@44.7654109,-91.5136289,17z/data=!4m5!3m4!1s0x87f8be07fda0a431:0xb2c38ea0334a21d!8m2!3d44.7654114!4d-91.5114395

Kupy, J. (2017) Field Activity #5: Development of a Field Navigation Map. Eau Claire, Wisconsin.

NAD83 / UTM zone 15N: EPSG Projection. In Spatial Reference. Retrieved 3/8/2017, from http://spatialreference.org/ref/epsg/nad83-utm-zone-15n/

What are map projections? In ArcGIS Desktop. Retrieved 3/8/2017, from http://desktop.arcgis.com/en/arcmap/10.3/guide-books/map-projections/what-are-map-projections.htm