Introduction
For this lab our objective was to navigate the area of
study, Priory course 1 data points, as shown in Figure 1. Each group, which
consisted of 2-3 people, were given course points to navigate with a Bad
Elf GPS, Map and Compass, shown in Figure 5. Using these different
methods would show the practicality between using a GPS unit or a Map and
Compass in the any situation that presents itself. The UTM Maps used for this
field excursion were created in the previous Field Activity #7 to help navigate
the course.
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| Figure 1. Area of Study Data Points |
Methods
The first navigation was done using a GPS unit in UTM
Coordinate System and a UTM Map. The author of this blog was assigned to course
1, as shown in Figure 1. The yellow data points were tagged trees. The UTM
points concurrent with our course were transferred onto a physical map to allow
a visual of the terrain. Figure 2. Shows the UTM units.
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| Figure 2. UTM units for course 1 |
The course was already tagged with
trees as data points giving the course and point number as shown in Figure 3.;
however, to make sure the points were accurate we went out and double checked
the UTM units and recorded them in our field notebooks. Bad Elf GPS units were
assigned to track logging to gather a continuous stream of data of where we
walked in accordance to finding each data point. Pictures of each tree
data point were gathered and geotagged to be imported into the maps in the
Results/Discussion section as shown in Figure 3.
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| Figure 3. Course 1 Data point 1. |
The second navigation was done using
a UTM map and compass. Although, we were to only use the compass and map we
were given Bad Elf's to track our logs. In order to use the map given with its
scale, we had to establish a pace. This was done by walking a distance of 100
meters and counting whenever we would step with our leading foot. The pace of
the author of this blog was 70. Due to how slow this pace was and not taking
the elevation of the terrain into consideration, it wasn't used. Next, the
compass had to be calibrated in order to find the correct bearing. Since all
compass arrows point magnetic north instead of true north, your declination needs
to be taken into account to accurately point you in the right direction. This
can be found on the map you're using or the internet. Since our declination for
our location was around -2 we didn't bother using it. But if we had we would
have subtracted 2 units from our bearing measurements. A total of 3 data
points were chosen to navigate to on the map, the first step was to gather a
bearing to know what the direction of travel was. To do this a line was
drawn connecting one point to another depending on where we started. Then
putting the compass edge, shown in Figure 4., on the course line
created from the start point to point 1 and aligning it with the north arrow
one could create a path. From here the bezel (rotating attachment) was moved to
become parallel to north and south on the map. Once that is done the Index
Line on the Compass (center of compass, above the bezel) should read off a
number that corresponds to the direction you need to travel i.e. 136. After the
compass is set up with the direction it can be held in front of you with north
facing away. From here you would want to have the magnetic arrow move into the
red arrow on the bezel or the common phrase is known as "red in the
shed". After the bearing, the distance had to be measured. Since we counted
out our pace and had a map scale we could simply decipher how many paces it
would take to get to our data points using metric units.
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| Figure 4. Navigation Compass and UTM Map |
Once all the track logs and photos
were gathered the next step was exporting them and creating maps showing our
data. The track logs were exported to an iOS device in a kml and gpx file,
then emailed out. Next, the kml file was converted into a layer in ArcMap using
the Conversion Tools-->From KML--> KML To Layer. After this
there coordinate system had to be projected. The cs chosen was:
NAD_1983_UTM_ZONE_15N because of the maps used were in that CS and also, a
feature class of the data points for the area of study was provided by
Professor Hupy in that cs. The tool used was Data Management Tools-->
Projections and Transformations--> Project. This was done to every track log
gathered that day. Next, the photos were imported into the map using the
tool Data Management-->Photos-->GeoTagged Photos To Points. Using this
all the photos could be imported and matched to the coordinate points associated
with them. For the author of this blog only 1 photo out of 4 came in accurately
to the correct data point. In order to have all photos come in properly
Geosetter was used to edit there Location coordinates, shown in Figure 4.,
based on the recorded ones when navigating the course. After the correct
changes were made the photos were saved as a kmz file, from here the kmz file
was then opened in Google Earth and saved as a kmz file. Once opened in ArcMap
the Conversion Tools-->From KML--> KML To Layer was used. This created a
Layer with the photos with their correct coordinates that could be used for
reference.
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| Figure 4. Geosetter Location |
Results/Discussion
The map in figure 5. Shows the data
points for course 1 and track log. All data points were found except
for 5, because our group ran into some "private property" issues
with a man bow hunting in the late morning. That being said the track log shows
that we were pretty close to the location of it. Data Point 1 isn't shown being
within the track log; however, it was found. This "miss" on the map
could be because of how the data point was marked when the trees were initially
marked in the woods. Whatever technology was used to mark it, it could have had
some error in its coordinate units. Course 1's track log isn't perfect with
straight shots to each data point given the terrain of the area of study.
Looking at Figure 3., one can tell the regional biome of the area was very
dense with shrubs and a mix of deciduous and coniferous trees. The multiple
canopy layers of the environment were also very hard to navigate. Also, looking
at Figure 5. and considering the contour lines, the elevation of the terrain
was very uneven. Relating these lines to the physical world, one could infer
multiple depressions for run off and soil deposition. The imported photos in
the map shows the coordinates that were taken of each marker. For the most part
they are pretty close to the actual tree marker, except for point 2. The photo,
data point, and track log are all not in correspondent to each other. This
could be due to writing down the wrong coordinates for this area. Since, these
photos weren't taken at a very high resolution it was not possible to enlarge
the thumbnail anymore; however the map file uploaded is a tiff so it should
have the highest pixel resolution that it can.
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| Figure 5. Course 1 Track Log |
Figure 6. Shows a map of all the
course points and track logs recorded that day. Starting with track log 2, all
of the data points were hit. Looking at data point 1 of track log 2 it could be
inferred that the path wasn't a straight shot to the next data point, with
trees and brush being in the way and steep topography. Data point 5 resembles
this theory with all the different routes taken; however, this track log
doesn't show the start and end path so it's hard to say if the group who
created this track log had issues going to data point 5 from 4 or vice versa.
Track log 3 appeared to reach all data points and had very little change in
elevation. Towards data point 3 there was a lot confusion in the terrain with
the track log showing multiple lines. Looking at track log 4, every data point
was hit. There were also instances where the signal was lost and retrieved
again provided by the evidence of stray track log lines. This could be due to
losing satellites or having magnets close by that are messing up the
connection. The terrain for track log 4 was quite steep given the closeness of
the contour lines, they were navigating in low and high relief constantly.
Finally, track log 5 exhibited some terrain that had to be navigated based on
the little changes in the track log. It also seemed that a lot of the data
points were overshot with the coordinates.
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| Figure 6. GPS Navigation Track Logs |
The map in Figure 7. Shows the data
gathered from the map and compass navigation. Due to some unforeseen events
while gathering the data, such as turning off the GPS unit by accident and
having a possible mix up with the kml and gpx files, no data can be presented
for track log 1 from the author of this blog. That being said another group had
the same course that can be analyzed with the others. Firstly, the bearing
distance found to navigate to each data point. Since most groups had 2-3 people
in them everyone had a designated job. The first person had to choose a
landmark for the second person to travel. Considering the terrain of the area
one couldn't walk in a straight path, so the bearing was dependent on
topography. The third person had the compass to tell the bearing to the second
person walking with their pace to the landmark. In the case of only 2 people in
the group the first person's job was omitted. Starting with the analysis of
track log 1, the data points were all hit. The path is not straight put
provided the landscape and changing the bearing distance from landmark to
landmark this makes sense. Track log 2, resembles the same patterns with change
of direction. It shows the pattern that the group chose to retrace their steps
back to the data points as they returned to the starting point of turning their
track log on. Track log 3 seemed a bit off from there points. However, there
data points could be located in a different area than was specified due to
errors. Looking back at Figure 6. Track log 3, it is hard to say where there
data point 4 and 2 would be with the track log congested around it. Moving onto
track log 4, this data point doesn't appear to have been hit but just like
track log 2 the same could be said with the errors. The paths taken to and from
the data point is quite similar and straight forward with few variations in
direction. Finally, track log 5 hit all 3 data points. Looking at the contour
lines down the hill and then turning off to data point 1 infers that the
landscape wasn't that hard to navigate through. While creating these track logs
a large possible impact on the results was not finding the exact bearing
direction because declination was omitted. Although it is only a 2 degree difference,
in a very dense forest and uneven terrain things can be easily missed while
walking and watching your step.
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| Figure 7. Compass and Map Navigation Track Logs |
Conclusion
This navigation field activity was very helpful and
applicable in real world experience, especially when you don't always have a
gps unit with you or the weather forbids it. Knowing the simple methods into
reading a compass and map to navigate can help in many situations. Overall,
having a map of the area you are traveling is the most important thing. That
being said it is also important to have the most updated one, considering
erosion, weathering, and other natural processes of the environment change the
lay of the land.
Reference
https://nhtramper.wordpress.com/2013/03/31/wilderness-compass-navigation-primer/
