Arc Collector Activity 2: Putnam Prairie Research Project

Introduction

In the previous lab, the class used Arc Collector to add data to an online database that was created beforehand. For this project, each student needed to create their own infrastructure to collect data about a topic of their choice. The objectives were to think of a geospatial research question to design a project around, create a database with three or more feature classes that would implement the research objective and deploy it to Arc Collector, collect point features, and then write a report to explain the results. The report, of course, had to include maps and use the data to either support or refute the research question.
As a conservation biologist, it was natural to gravitate toward a biology research topic. Two semesters ago in the fall of 2015, the Conservation Biology 328 class implemented an invasive species removal plan in the Putnam Prairie area.  The project focused primarily on the mechanical removal and chemical treatment of invasive Black Locust trees (Robinia pseudoacacia). This invasive tree is prolific in the area and provides many management challenges, and there was some trepidation as to whether the removal strategies would help or just aggravate the problem.

This seemed a perfect opportunity to answer the question once and for all: Did the black locust removal attempt succeed in removing black locust from Putnam Prairie or did the control attempts worsen the problem?  

Some smaller questions to address:

·         Has black locust returned in the areas where we removed it?

·         Was the removal treatment effective in killing the treated stems?

Materials:

·         Nexus 9 Android tablet borrowed from the Geography department. (It has a built in GPS, unlike an iPad)

·         Arc Collector

Black Locust (Robinia pseudoacacia)

Black locust is a fast growing deciduous tree which is commonly 30-80 feet tall. Native to the Appalachian Mountains throughout Pennsylvania to Alabama, it has spread throughout most of the contiguous United States as an invasive species. Movement to new areas was often facilitated naively by landscapers, who valued the species for its aesthetics and rot-resistant wood. It thrives in disturbed habitats with full sunlight, such as prairies or floodplain forests. It grows best on well-drained soil with sparse competition.
It has pinnately compound leaves and highly fragrant white flowers which give way to hanging seed pods. Trees and saplings feature prominent woody spines along the trunk and branches, which makes it easy to identify a black locust even in the winter (Fig.1).

Figure 1 Identifiable characteristics of the species Robinia pseudoacacia. The prominent woody spines made identification simple, since there were no leaves, pods, or flowers at the time of data collection. 

Table taken from "A Weed Report" from the book Weed Control in Natural Areas in the Western United States, available through the UC Weed Research and Information Center.  

The tree produces numerous suckers from the roots and thus is capable of forming dense clonal colonies that exclude native vegetation. Root suckers, which form primarily where branch roots emerge from older roots, become new saplings very quickly when the main stem of the black locust is disturbed. This is known as vegetative regeneration and in black locust it is considered a more common means of reproduction than seed. Sprouting often occurs in response to stem or root damage due to cutting, fire, wind, or disease. This creates a huge challenge for the control of black locust, since areas of abundant sunlight allow for one tree to become a dense thicket of trees when any form of removal is attempted. (For more information on vegetative regeneration, click here!) 

Typically, the root system of an established black locust tree has a radial extent of 1 to 1.5 times tree height. Root extensions of 165 feet were documented in the Appalachians. In the sandy soil of Putnam Prairie, extensive lateral root systems are to be expected. (For more information on this, click here!)

Area of Interest

Putnam Prairie is a small area of land near the main UW Eau Claire campus (Fig.2). It is considered a “postage stamp prairie”, an area that has been a prairie since before the city of Eau Claire was built around it. Unfortunately, Putnam Prairie is being inundated with multiple invasive species, including burgeoning thickets of black locust, which are excluding the native grasses.

Figure 2 The Putnam Prairie is attached to upper Putnam Park, adjacent to the UW Eau Claire campus and Sacred Heart Hospital. 

Figure 3 These two images show a comparison between the Putnam Priaire in fall of 2015 and summer of 2016. The left image is from the ESRI basemap (TerraColor satellite imagery, NAIP2015 Source: USDA FSA) and shows “leaf off” conditions. The black locusts are more difficult to spot without summer foliage, but the 2016 photo (Source: Google Earth Pro) shows the extent of the invasion. With the exception of three jack pines, all of the trees within the blue boundary are invasive black locusts.

The Fall 2015 Conservation Biology 328 class, under the instruction of UW Eau Claire Biology Professor Dr. Paula Kleintjes-Neff, completed a project early in December 2015 that centered around removal of black locust trees from Putnam Prairie. (The class report on this project can be viewed here.) Two physical control methods were used:  total removal by either by hand saw, or girdling using hand saws or serrated knives. All the removed or girdled trees were then treated using a mixture of the chemical herbicide triclopyr and mineral oil. This herbicide, advertised under names including Garlon or Crossbow, is selective in managing woody plants as well as herbaceous weeds.  When mixed with mineral oil, triclopyr is less toxic to native wildlife, and due to the selective nature of this chemical, native grasses are minimally affected by it. The class was informed that the most successful method for application is to cut the tree down and saturate the remaining base with this mixture (Fig.4).

Figure 4 An actual photo from the black locust removal performed by the students of Conservation Biology 328 in December 2015. The triclopyr herbicide was applied using spray bottles and applied liberally to the stumps or girdled area. Photo credit: Alexandra Johnson. 

The Fall 2015 Conservation Biology class was not the most recent class to implement invasive species removal in the Putnam Prairie. According to Dr. Kleintjes-Neff, the Spring 2016 Biology 328 class also cut, girdled, and treated black locust trees. According to Dr. Kleitjes-Neff, many of the trees treated December 2015 had new shoots regenerating from the stumps or and they were treated again by the Spring class, as well as many small trees that had not been treated previously. The future spring section of Biology 328 (Spring 2017) is also forecasted to participate in the removal.  

Methods

Step 1: Prepare the data in ArcGIS for Desktop

The ESRI online tutorial provided simple guidelines. 

1. A geodatabase called “Putnam_Prairie” was set up in ArcCatalogue.
2. Next, geodatabase domains were defined. This provided a list of choices the data collector can choose from while working, and cuts down on data entry error. For this project, data needs to be collected about tree height, whether the tree has been treated for removal, type of removal attempt (girdling/herbicide, hand saw and herbicide), and whether the tree is alive or dead.  Out of trees that are alive, are the roots regenerating shoots or not.
3. To define the feature class: “Black_Locust”, a point feature class, was added to the geodatabase. Coordinate system as WGS 1984 Web Mercator (auxiliary sphere).
4. Set up the fields. This is a key part of the information model. Fields provide the structure of the information collected in the field and provide rules for the types of information collected about a feature.

o   Tree_height: height of target plant (feet)

§  Range: 0-40 feet

o   Treated: Was the tree treated for removal last year?

§  Yes or No

o   Removal_type: What method of removal was attempted?

§  Girdling (A ring was cut around the trunk and then the stripped area was sprayed with herbicide) or Saw (Trunk removed by saw, stump treated with herbicide)

o   Status:

§  Alive or Dead

o   Regenerating: New shoots forming?

§  Yes (New shoots from stump or base of trunk) or No (No evidence of new shoots)

5. Theme the data: The symbology was set to green circles to keep the map simple.

6. Publish the data: The map was deployed to ArcGIS Online for mobile offline data collection.

Step 2: Data Collection 

Data was recorded over two days in mid-November by Amanda Senger using the Android tablet. The weather was just above 50 degrees F and windy. Attributes and photos were recorded for 197 black locust trees. The study area consisted of two main thickets of black locust with trees ranging from 2 to 7 feet tall (Fig.5). The three most common tree types to find were small untreated saplings, treated stumps that were sprouting new shoots, and girdled trees that were surrounded by young clones (Fig.6). 

Figure 5 This was a common sight in Putnam Prairie. The clonal colony of black locust was so thick it was difficult to walk through. Data collection was as unbiased and inclusive of all heights and treatment statuses as possible. 

Figure 6 These were the three most common cases. The sapling on the left is less than four feet tall. Girdling was common on the medium-large trees. 

Results and Discussion

Data analysis revealed that 119 out of 197 of the trees that were sampled had been treated for removal. Of those 119 stems treated, only 59 of them exhibited no signs of regeneration. This means that the Conservation Biology class had a removal success rate of less than 50% (Fig.7). According to the literature, these trees likely reproduced vegetatively by sending out root suckers after the treatment disturbed the main stem. 

Figure 7 Only 59 out of the 119 trees (meaning individual stems) that were treated for removal over the last year were dead. The others were either unphased by the treatment or showed signs of increased sprouting or vegetative reproduction. 

Embedded is an interactive map of the study area which displays the sampled trees, treatment type, and whether the treatment was successful in killing the stem (Fig.8). It was not feasible to determine which trees shared a root structure, and which trees were vegetative offshoots of nearby stems, so each stem that possessed a singular trunk was counted as its own tree. Clusters of trunks that were touching at the base also counted as one tree. 

Figure 8 To view the interactive results map, click here. 

 Trees with heights exceeding 20 feet were considered large trees for the study area. There is a feature class in the map above displaying a minimal estimation of the ground area reached by their radial root structure. Since the literature stated that root suckers (responsible for clonal colonies) form primarily where branch roots emerge from older roots, and radial root area is 1-1.5 times tree height, it can be expected that dense clonal colonies could form quickly around these areas. My observations in the park corroborated this; the largest black locust trees were centered in the densest black locust thickets. This supports the hypothesis that those dense thickets were clonal colonies that were sprouting through vegetative regeneration from the existing root structure of established black locust trees.

 A proportional symbol map of the estimated root radius of each tree demonstrates the estimated root overlap of the trees. The dense colony areas are so overlapped that it is likely that they are sharing root structures (Fig.8).
 

Figure 8 The tan circles on this map represent the most modest possible estimate of root radius relative to tree height. With the amount of overlap occurring in the main thicket areas, it is clear that the black locust has not suffered at all from the removal treatment, and has likely increased sprouting in response.

Conclusions

·         Has black locust returned in the areas where we removed it?  Yes

·         Was the removal treatment effective in killing the treated stems? No

Did the removal attempt help or make the problem worse? The literature asserts that black locust seedlings grow rapidly when planted on sandy sites with little shade and sparse competition, especially when a site has been disturbed. According to these criteria, Putnam Prairie is an ideal habitat for black locust. Even without removal attempts, germination from seedlings and root suckers was inevitable and the trees would have continued to take over the prairie whether or not human intervention accidentally increased the rate of spread.   

Since trees do not begin producing seeds for the first 6 years of life (on average), and there were only 10 trees taller than 20 feet, it is likely that there are very few trees in the study area mature enough to be producing seeds. Root suckers are the most prevalent form of natural reproduction in mature trees, and dramatically moreso in Putnam Prairie. Suckers usually appear in the fourth or fifth year when the tree has not been aggravated, but is occurring on first year saplings in Putnam Prairie. The high rate of proliferation witnessed in the study area indicates that the trees are sending out more root suckers and are undergoing increased vegetative reproduction as a response to the disturbance of removal attempts. So yes, unfortunately, it is likely the removal attempts have made the black locust problem in Putnam Prairie worse. Some possible reasons that the treatment was so ineffective include not enough herbicide being applied or the triclopyr solution potentially being too diluted. Or perhaps the soil conditions are just too good in this prairie and black locust cannot be contained.  

However, the fact remains that Putnam Prairie would have an invasive species problem regardless. At least an attempt to help restore the prairie ecosystem is being made. As the old adage states, Better to do something imperfectly than to do nothing perfectly.


Overall, this project emphasized the importance of planning ahead and keeping the research objectives in mind while setting up the geodatabase and attribute fields. There were a few hiccups in the attribute field setup that caused redundant data to be collected (such as stating that the tree is dead and then having to fill out fields to say that it exhibits no sprouting). Domains made data collection very streamlined, and greatly increased the efficiency of data collection. For future GIS projects using this study area, research could be done to determine whether the Spring 2017 Conservation Biology 328 course meets with more success in their attempts to control the black locust outbreak.

Sources

Conservation Biology 326, Fall 2015. http://conservationbiologyuwec.blogspot.com/

http://wric.ucdavis.edu/information/natural%20areas/wr_R/Robinia.pdf

Huntley, J. C. (n.d.). Robinia pseudoacacia L. Retrieved November 27, 2016, from http://www.na.fs.fed.us/Spfo/pubs/silvics_manual/volume_2/robinia/pseudoacacia.htm

Stone, Katharine R. 2009. Robinia pseudoacacia. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: http://www.fs.fed.us/database/feis/ [2016, November 28]. 

Special thanks to Dr. Paula Kleintjes-Neff for providing information regarding the Biology 328 classes’ removal work and procedures. 

Arc Collector Activity 1: Introduction

Introduction

This activity served as an introduction to Arc Collector, an extension of ArcGIS Online that allows users to collect and update data in the field, log current location, and map data in real time. With this application, maps can be created and used anywhere to ground-truth data, make observations, and respond to events. It is highly accurate and efficient and is a logical use of resources, since today’s smart phones and tablet devices have many times more computing power than most GPS units, often coupled with better internet access. Arc Collector’s practicality makes it an excellent app for burgeoning GIS professionals to be familiar with.

For more info on Arc Collector, visit the ArcGIS Online site here. 

In this activity, the class used Arc Collector to record microclimate data from around campus using smartphones and tablets. Our objectives were to collect dewpoint, temperature, wind speed and wind direction in different areas on campus and then use the data to map the microclimate of UWEC. We were to have at least one map for wind, dew point, and temperature.

Materials Used

•      Ipad
•      Kestrel 3000 portable weather station
•      Brunton compass (to collect wind direction)
•      iPhone 5s

Study Area

The study area was the main University of Wisconsin Eau Claire campus and Putnam Park, a state natural area incorporated into the campus. Campus is naturally divided into two sections by the infamous UWEC hill. Lower campus houses the academic buildings, library, and student center, and is divided by the Chippewa River. Upper campus contains the Hilltop Cafeteria, two athletic facilities, the student health center, campus police headquarters, and nine residence halls. Putnam Park includes the hill and the areas on either side of the river valley. In order to ensure somewhat even coverage of the campus, the class was split into small groups. Access was granted to an ArcGIS Online account with a shared map of the campus, subdivided into 5 zones, and each group was assigned one of the 5 zones to collect data within (Fig.1). Group 9 was Jackie and Amanda (myself) and we were one of the groups assigned to zone 5, which included the upper campus residence hall area, the hill, and a large expanse of lower Putnam Park by the Chippewa river.

Figure 1 The map of campus that was shared with the class via ArcGIS Online. Groups were assigned a zone to collect data within.

Methods

Group 9 set out with the Kestrel 3000, the compass, and Jackie’s phone (iPhone 5s). Amanda, lacking technology of her own, borrowed an iPad from the University Geography department. ArcGIS Online and Arc Collector were opened and the project was deployed. Each group was tasked with collecting 10 or more data points. Jackie and Amanda chose to take the Putnam Park trail and walk along the bottom of the hill and through the river valley area.

Data collection consisted of holding up the Kestrel 3000 until it could get a steady read on dewpoint, wind speed, and temperature (Fig.2). Wind direction was measured by facing the wind and then measuring the azimuth. These attributes were typed into the Arc Collector chart associated with each point and updated at each individual site. Each time an update was made by any of the groups walking around campus, the data point was added to the map so that it could be seen by the whole class. The iPad, in an unexpected twist, did not connect to the campus internet, and was rendered useless for data input. It was used instead for its camera during the excursion.

Figure 2 Jackie collecting the microclimate attribute data. On the left, the Kestrel 3000 collects dewpoint, wind speed, and temperature. The wind direction azimuth was calculated using the compass (right photo).

All ten points were collected with ease. In addition to Putnam Park on lower campus, we collected points on the hill, on the tennis courts, and near Towers Hall (Fig.3).

Figure 3 Action shots of Group 9 collecting data. On the left, Amanda reads dewpoint values in the Putnam Forest. Jackie poses with the Kestrel 3000 on a scenic portion of the Chippewa River and checks wind direction on upper campus.

Generating Maps

Now that all the data was compiled into ArcGIS Online, it was time to return to the lab and commence the mapping process.

ArcGIS Online (Fig.4) was accessed and opened in ArcMap desktop. I saved the data points to my newly made “MicroClimate” geodatabase as a point feature class “MicroPts_sengera”.

Figure 4 A screenshot of the ArcGIS Online dashboard.

I also saved the “Microzone” boundaries which designated the zones on campus that groups were assigned to collect data within. In order to make a boundary around the total study area, the Dissolve tool was used. This created a polygon with no divisions between zones. The editor tool was utilized to move the boundaries to include all of the points, since there were a few data points a little bit outside the boundary lines (Fig.5).

Figure 5 Results of the Dissolve and Editor tools.

The Feature to Raster tool was used to convert the temperature field in the “MicroPts_sengera” attribute table to a raster that would display temperature as a continuous surface. Unfortunately, it didn’t work. Instead of resulting in a smooth surface, it made discrete dots that were color-coded. Interpolation was necessary. Spline was the obvious interpolation choice for temperature, since it is best suited for continuous surfaces (see my Digital Elevation Surface blog post for a review of interpolation methods). The Spline Spatial Analyst tool was used on the temperature field. This resulted in a continuous surface map (Fig.6). The same Spline interpolation tool was used on the dewpoint data field.

Figure 6 A continuous surface map of the temperature data. This needed to be massaged for a better display, but the largest step was complete. 

The wind direction data proved more difficult to map. The spline interpolation tool was used on both wind speed and wind direction, then added to the map. Under Properties>Symbology on the wind direction feature, Vector Field was selected. This allows arrows to be used to indicate wind direction. Importing the wind speed data allowed for the arrows to be colored in accordance to a scale of magnitude (Fig.7).

Figure 7 Selecting Vector Field allowed the data to be displayed as Beaufort wind arrows. This demonstrates wind direction and magnitude simultaneously.

Finally, I made a more comprehensive map of the study area. This involved editing the microzone feature class; some vertices had to be moved around to eliminate gaps and overlaps of the zones.

Results and Discussion

Unfortunately, despite the division of the class in an attempt to capture information in a relatively even distribution across campus, no groups collected data in Zone 4, which left a large data gap in the center of campus. This data void was unfortunate and undoubtedly affected the overall accuracy of the microclimate assessments. Notice there is data void over the river and also in the left side of Zone 5 corresponding with the Towers Parking Lot. Data collection in the river was prevented by the lack of kayaks, but the lack of data points in the parking lot was an accidental oversight. Apart from these three areas, the data points are relatively uniformly distributed. 

Figure 8 The map of the UWEC campus with the five designated zones. The points show each data entry to the ArcGIS Online map. Notice the lack of data points in Zone 4. 

The next map compares temperature, dewpoint, and wind speed/direction data taken at each point. The data was interpolated into a continuous surface map for each attribute (Fig.9). 

Figure 9 This map compares the climate attributes that were collected. 

It is interesting to note that the river does not seem to affect the microclimate data. Since the data was recorded on a 60 degree (C) mid-November day, it would be expected that there would be at least a small low pressure system above the river. This would be expected to have obvious affects on dewpoint and temperature in the areas directly above the water, at least. Instead, temperature and dewpoint appear to vary just as much over the water as they do over the upper and lower campus areas. Just from looking at the data without the background map, the river would not be evident at all. This could be because there were no data points taken directly above the water and the interpolation tool averaged over that area. It is also interesting to note the high temperatures recorded above some buildings on lower and upper campus. Reported at 89 degrees, this could correspond to heat vents outside the buildings or even the ambient temperature inside the buildings, but it is unlikely that the temperature inside the buildings was that high, much less the area outside the buildings. Some sort of error must have occurred in the data there.

In order to best showcase the wind data, I decided to make a separate map for wind speed and direction, in addition to the one included in the main attribute map, so that I could include more detail (Fig.10).

Figure 10 This map demonstrates the wind direction and magnitude in meters/second reported across the campus. The arrows denote the direction the wind is reportedly coming from.

This map was interesting, since the amount and size of the Beaufort wind arrows appeared very differently in ArcMap before the map was exported. The arrows in the final map are small and difficult to see, which is an unfortunate situation I will work to remedy in the future, perhaps by collecting U and V data and creating a custom vector field. It is another point of contention with the Beaufort wind display that the legend would not allow me change the colors of the arrows or to truncate the legend. In the map of campus, the highest wind magnitude reported was an 14.1, but the pale yellow arrows blend in too well to be seen against the background map. I attempted to remedy the situation by making the background map transparent, but it didn't help much. 

Analyzing the wind data, there seems to be no distinctive pattern of wind speed or direction indicated on the map. This could be due to many factors; wind is blocked in many places on campus by the hill, Putnam Forest, and by buildings. It is also likely that students did not report wind direction in a standardized manner. Some may have been facing the wind, and some may have had their backs to the wind. This discrepancy may explain the chaotic air flows and vortexes reported on campus. 

Conclusions

Overall, this field activity was a successful introduction to data collection using portable weather stations, and Arc Collector and ArcGIS Online. Collecting points was a simple matter of walking to a location that seemed to be far away from any other group’s data points, taking the measurements, and then recording and updating the map. It was incredibly easy and efficient. It was very convenient to be able to see where other groups were inputting data points in real time, it allowed us to disperse more evenly and collect a wider range of data.

Field Activity #7 Part II: Map and Compass Navigation

Introduction

This is the second part of the navigation activity at the Priory. The previous blog post contains details about the Priory and the two navigation maps that I made, one in UTM (in meters) and one in WGS (decimal degrees). This week, the task was to use the maps we created to navigate to five points somewhere on the Priory property relying mostly on a navigation compass. The class was split into small groups for this activity. Group 5 consisted of Anneli, Jackie, Jeffrey, and Amanda (myself). Prompted to choose one group member’s map for use in the field, Anneli’s was selected. Her use of 5m contour lines instead of 2m contour lines increased the readability of the map noticeably (Fig1). This navigation activity took place late in the afternoon in early November, but it was a remarkable warm and pleasant 62°F. The weather was slightly overcast, but no precipitation.

Figure 1 Our navigation map for Group 5, hitherto known as Team Thundernado. Map designed by Anneli. Notice our destination points marked with black X's. 

Materials Provided:

•         Trimble Juno GPS
•         50m measuring tape
•         color printed 11x17” copies of the navigation map designed by one of our teammates
•        A Brunton navigation compass
•          A list of 5 waypoints to locate
•          Pink plastic marking ribbon

I also brought two clipboards and a Nikon CoolPix AW120 camera.

Compass navigation is a simple skill, but it does require some specialized knowledge. More information on how to navigate with a compass can be found here.  There is also a video tutorial at the end of this blog post. The compass we used was transparent so that you could see the map features underneath (Fig2).

Figure 2 The compass we used to measure the distance between points and to find the direction of travel. Notice the green arrow is pointing in the direction of our first destination point on the map.

Methods

Upon arrival at the Priory’s main parking lot, the class received their waypoint coordinates and tools. A lesson on how to use compasses and GPS units was given by Dr. Hupy. We used the 50m measuring tape that was laid out to assess our individual pace counts. Anneli and Jackie plotted the waypoints onto the map using the list of coordinates issued to us.

Our team designated roles to each member. I had worked with compasses before, so I volunteered as the compass holder. It was my job to find the bearings from the map and point us in the right direction. Jeff, in his conveniently visible yellow shirt, was designated as the “runner”, who would go ahead of the group, directed by the navigator, and serve as a landmark for the pacers. Jackie and Anneli were the pacers, who would calculate the distance between the points using paces and walk in the given direction in as straight a line as possible. Jackie held the GPS unit, which we were to consult as a backup source of information The GPS’s tracking function was enabled so that our exact path could be downloaded in the form of a track log later. But in the moment, we needed to rely on measurements on the map converted to meters, converted to Jackie and Anneli’s pace rate. 

Within the larger navigation area, our points on the map were clustered within a 250x250 meter range in a wooded area that included a series of steep ravines connecting a dry creek bed. In order to get from the starting area to point 1, we decided to use the compass for direction but rely on landmarks more than paces, since this was a 330m stretch through thick brush and landmarks were distinguishable on the map. Using a nearby shed as a landmark, we navigated to point one easily and then check our location on the GPS (Fig3). 

Figure 3 In the left picture, Jackie is standing about 3 meters from point one. Jackie and Anneli consulted the GPS to ensure we were in the right location.

Navigating from point 1 to point 2 led us through a meadow of tall burs and grasses (Fig4). Jeff ran ahead and encountered a coyote, which became our unofficial mascot for the remainder of the activity.  Challenges came when the pacers encountered a steep ravine that was overgrown with thick brush and could not walk a straight line through. We relied heavily on the GPS to locate point 2, and we eventually realized that there was no marker on the location. We used our pink tape and marked a tree that was our closest estimate.  

Figure 4 The field between points 1 and 2, where Jeff (seen in the background) encountered a coyote. 

 Locating point 3 was relatively simple since it was on the edge of a stand of red pine that were equally spaced and easy to pace through (Fig5).  Point 4 was also simple for this reason. The route between points 4 and 5, however, brought us back through the steep creek bed. Pacing through that was complicated more by the large amount of old refrigerator parts, jars, and other not-properly-recycled goods littering the ground. Jeff ran ahead in the given direction and was able to spot the marker of point 5.

Figure 5 Team Thundernado in action! Amanda (left) crouches to find bearings to the next point, simultaneously joking about asking Sasquatch for directions. Jackie and Anneli (center) enjoy a break in the brush and some easy pacing. Jeff (right) demonstrates the "red-in-the-shed" principle of compass use: the compass is held against his torso,  and the other points in the direction of the next destination when the red arrow in the compass aligns with the "shed" in the dial.  

Figure 6 In all, it was a good day. We encountered a coyote, learned some new skills, and found all five of our points. 

Track Logs

Because the GPS unit we carried with us was tracking our location, the efficiency of our actual route can be examined by mapping the “track log” as a line feature. I designed two maps, one with the track log information and waypoints for just our group (Group 5), and another with all 6 groups' track logs and waypoints. 

The map of just Group 5’s endeavors:

In Arcmap desktop 10.4.1, I added a basemap of aerial imagery and zoomed in on the navigation area. 

Our track log was made available by Dr. Hupy in the form of a point class shapefile. I opened it in Arcmap and used the Point to Line tool to connect the dots and clearly demonstrate the path that we took on our navigation journey.

Adding the waypoints themselves to the map was a puzzle, since no shapefiles were provided, only lists of coordinates in a text file. Taking the coordinates of the waypoints from the list, I made an excel sheet with all of the waypoints for Group 5 (aka my group, Team Thundernado). The table had

Figure 7 Group 5's waypoint coordinates.

a Point_ID field, an X field, and a Y field (Fig7). 

I then imported the table into Arcmap and used the Make XY Event Layer tool to convert the coordinate points to actual points on the map. I also used the Project tool to set the coordinate system to WGS 1984 from the coordinate system the points were originally in, which was UTM Zone 15. I only did this because multiple layers in my data frame were already in WGS 1984; otherwise UTM Zone 15 would have been preferable. Since event layers don’t automatically save to a geodatabase, I exported the data to my Priory geodatabase as Destination_Points. I then had a point feature class of the waypoints that could be labelled and used to check the efficiency of our path as reported by our track log.  

In order to add a dotted line to highlight the most ideal and efficient path between the points, I exported my Destination_Points feature class to my Priory geodatabase and then ran the Points to Line tool on the destination points. I also digitized the start location and added that.

The map of all 6 group track logs:

 Track log data for groups 1-6 was downloaded and imported into ArcMap as shapefiles in my Priory geodatabase. All data was projected to WGS 1984 for the sake of consistency. The track log information for groups 2-6 (the feature class showing their path on the map as recorded by the GPS that was with each group during the activity) was originally in a point feature class. I used the Point to Line tool to convey the track log data into lines so that the path each group took would be clearer (Fig.8).

Figure 8 Results of the Point to Line tool, shown here on Group 5's data. 

 The use of this tool unearthed an interesting error with the tracklog data from Group 4. The line that was mapped to connect all of the location points continued and led to a mysterious location that was far away from the location of the navigation activity. This fun little mystery was solved by zooming in to the points at the end of the mysterious line (Fig9). This problem was easily remedied by deleting the erroneous points in Arcmap and then rerunning the Point to Line tool on the data from Group 4.

Figure 9 Apparently the tracking mechanism in GPS unit 4 was activated again after the unit had returned with the class to the main UWEC campus. You can see on the inset map that the device began transmitting again in the campus mall area. Perhaps another geography class was doing a project on campus with the GPS units and the track log never got turned off.

 Adding the waypoints to the map was expedited this time by Dr. Hupy emailing a text file of all of the waypoints X and Y coordinates.  In order to create a shapefile of the destination way points to compare the tracklogs too, I imported the text file as a table to my geodatabase and then used the Make an XY Event Layer tool. This plotted the points onto the map. I exported the event layer into my geodatabase so that. The coordinates were in UTM Zone 15 though, so I used the Project tool to project them into WGS 1984.

Results

This navigation activity went smoothly overall, but we did rely on the GPS to help direct us to points 1 and 2 more than we used the pacing and compass method. Also, we neglected to send the GPS unit with Jeff when he walked across the ravine to point 5, so on our track log it looks as though we skipped point 5.  It was difficult to use the pace-count method since the terrain and dense vegetation made traveling in a straight line impossible at times. You can see that our path, shown in purple on Figure 10, was not the most efficient. Also, there was a lot of ambiguity around the location of point 2 since the marker was missing. We wandered around in the woods a bit, and that is evident. 

Figure 10 The map of our waypoint coordinate locations. The purple line represents our path.  Ideally, we would have travelled in a perfectly straight line from point to point, but that clearly was not what occurred. 

Surprisingly, our group still fared better in efficiency than most of the other groups in class. It is clear that, as a class, we all relied heavily on the GPS coordinates to navigate. If we had only compass and pacing to navigate by, then we would have all traveled in straighter lines. 

Figure 11 All 6 group track logs are mapped here. It is unclear why many points appear to have been un-accessed.

In conclusion, this activity was a stretching experience. It opened my eyes to the challenges that most of human history, who did not have pre-processed data and GPS technology to rely on, faced in navigating the world. I respect the challenges that they overcame in mapping and orienteering in order to bring us to where we are today.

For more information on navigating with a map and compass, check out this video!

Field Activity #7 Part I: Creating a Navigation Map of the Priory

Introduction

On a basic level, all that is needed for accurate navigation is a tool to aid directional orientation, such as a compass, and a location system, which is usually a map associated with a coordinate system and projection. Which coordinate system and which projection make a large difference in the accuracy of the results; large geographic scales are not reliable for small geographic areas because they do not portray each local area with integrity and distortions become more apparent the more the user “zooms in” to smaller and smaller areas.
In this field activity, each student needed to make two maps for navigation of a local forested area, one with the UTM coordinate system and one with the Geographic Coordinate System in decimal degrees.

What is a coordinate system?

Put simply, a coordinate system is a reference system used to represent the locations of geographic features, such as Global Positioning System (GPS) locations, within the context of where it is on the globe. Coordinate systems enable geographic datasets to use common locations for integration.
 Each coordinate system is defined by the following:

• Its measurement framework, which is either geographic (in which coordinates are imagined on a round globe) or planimetric, a.k.a. projected (in which the round globe of earth is mathematically projected onto a 2D map)
• The definition of the map projection for projected coordinate systems
• Units of measurement
• Other measurement system properties such as a spheroid of reference, a datum, one or more standard parallels, a central meridian, and possible shifts in the x- and y-directions

There are hundreds of geographic coordinate systems and a few thousand projected coordinate systems. Two types that are very commonly used in a geographic information systems are:

Diagram of a GCS. Image from ESRI 

Geographic coordinate systems (GCS) - A global or spherical coordinate system such as latitude-longitude. These are unprojected (not flattened onto a 2D map). The units are decimal degrees. The most popular of all of these is GCS WGS 1984, and that is the coordinate system used for my map.

Universal Transverse Mercator (UTM) - A projected coordinate system that divides the earth into long vertical zones that resemble orange slices. These zones can be further subdivided for better accuracy. The units are typically feet or meters. For my map, I used the coordinate system UTM Zone 15N with meters.

The UTM coordinate system. Images found on google and altered.

 For more information on coordinate systems and projections, check out the ESRI Help page!

Students worked individually on the maps but were placed into an assigned team of three or four for the navigation activity, which will be covered in the next blog post (so stay tuned!). Group 5, a.k.a. team Thundernado, was Anneli W., Jackie S., Jeffrey S., and Amanda (myself). For the navigation activity, a list of waypoints and their associated coordinates will be provided. These points will be manually plotted and located the old-fashioned way, using only a trusty ol’ compass and our map.

The Study Area

Figure 1 Photo by Bill Hoepner, UW-Eau Claire photographer.

The Priory, formerly the St. Bede Monastery, is located three miles south of the UW-Eau Claire main campus. The property, situated on 112 mostly wooded acres, was purchased in October 2011 by a subsidiary of the UW-Eau Claire Foundation for use by the university. It is currently being used as a children's center and has been recently renovated for use as a residence hall (Fig.1).  It is located three miles south of the UWEC campus in a forested and hilly area. Below is a view of the Priory grounds and a map of its location, courtesy of Jacob Henden (Fig.2). 

Figure 2 This is a view of the Priory grounds where the navigation activity will take place. The Priory is a convenient drive from UW Eau Claire’s main campus.   

Objectives

The objective for this week was to make navigation maps that display the navigation area as accurately and concisely as possible. A large suite of data was made available for this project (Fig.3), and it was the responsibility of the student to decide and utilize only the most expedient information in their maps.

• Create two maps: one that contains a UTM grid at 50meter spacing (or finer), and another that provides Geographic Coordinates in Decimal Degrees
• Both maps will contain the following elements:

1. North arrow
2. A scale bar (Meters) and a RF scale.
3. Info about the projection/coordinate system
4. Labelled grid
5. Basemap
6. List of data sources
7. A watermark

Figure 3 This is a sample of the data that was provided by Dr. Hupy. The red box indicated the extent of the navigation area boundary. It encircles the land around the Priory, a property owned by UW Eau Claire.

Methods

Making the maps in ArcMap was an enjoyable challenge. After testing a few options, it was clear that the topographic map and the black & white intensity image would not be very useful on a navigation map. I decided to use the aerial image of Eau Claire as a basemap, knowing the true color imagery would help Team Thundernado get our bearings in the field. I also added contour lines to show elevation changes. The boundary of the navigation area was also provided, but needed to be projected along with all the other data into the desired coordinate system.

For the UTM map:

All the data needed to be projected into the NAD_1983_UTM_Zone_15N coordinate system. Rasters, such as the Eau Claire satellite imagery and the elevation data, were converted to NAD_1983_UTM_Zone_15N coordinate system using the Project Raster tool. Feature classes, such as the navigation boundary, were converted using the Project tool. Then the Clip tool was used to clip the feature classes and rasters down to the extent of the navigation area boundary that was given. Contour data of 2 feet contours was provided by Dr. Hupy, but the lines looked too crowded for use on a navigation map. In order to create a more visually appealing and applicable contour diagram, I ran the contour tool on the clipped elevation map and created contours of 2 meters. Figure 4 compares the two.  

Figure 4 The 2 foot contour lines were provided but appeared too crowded to be useful for a navigation map. Also, the grid was to be in meters, so feet seemed irrelevant. The 2 meter contour lines provide a clearer image of the elevation.

A measure grid of 50m squares was added to aid in navigation (Fig.5). This will help during the navigation by allowing us to visualize scale and distance.

Figure 5 A close-up view of the grid labels. The grid on the UTM map shows every 50m. 

For Geographic Coordinate System map:

All data for this map needed to be projected into the GCS_WGS_1984 coordinate system. Like last time, rasters, such as the Eau Claire satellite imagery and the elevation data, were converted to GCS_WGS_1984 coordinate system using the Project Raster tool. Feature classes, such as the navigation boundary, were converted using the Project tool. Then the Clip tool was used to clip the feature classes and rasters down to the extent of the navigation area boundary that was given. Contour lines were also projected for this. A grid was made for this as well, but adjustments were made to display the decimal degree units clearly.

Results

In hindsight, I would have preferred to use 5m contour lines to alleviate some of the high relief areas from that crowded contour line effect. Overall, however, I feel these maps are very streamlined and would be very useful and accurate for the field navigation next week.

This is the Universal Transverse Mercator coordinate system map which uses meters as a unit. It features a grid of 50m and 2 meter contour lines to aid navigation. The navigation area is outlined in orchid.

This map uses the World Geographic Coordinate System (1984). The units, in decimal degrees, are labelled on the grid. 2 meter contour lines are depicted and the navigation area is outlined in red.

Sources

The Priory provides home for former Children’s Center. (2012, September 1). Retrieved November 1, 2016, from https://www.uwec.edu/news/the-view/the-priory-provides-home-for-former-children-s-center-871 

For Priory locator map:

Henden, J. (2015, May 3). Navigation with Map and Compass [Web log post]. Retrieved November 1, 2016, from http://jacobhendengeog336.blogspot.com/2015/05/navigation-with-map-and-compass.html