The document provides an overview of various field mapping exercises conducted by Luke Ferricher for a geography course. The exercises included measuring pace and distance, using tools like tape measures, lasers and GPS to measure horizontal distance, walking and mapping bearings, mapping points using azimuth and angle measurements, laying out square plots, and mapping soil profiles. The document describes the methods used and presents data collected, along with observations about the precision and accuracy of different measurement techniques.

1.
1
Luke
Ferricher
Geography
418
12-­‐05-­‐2014

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2
Overview:
1. Field Mapping……………………………………………………….....….3
2. GPS Mapping and Navigation / GPS Orienteering / Land Cover….…14
3. Soils………………………………………………………………….....….26
4. Stream Hydrology………………………………..…………………...….30
5. Water Quality / Stream Health……………………………………….…36
6. Vegetation Sampling, Forest Structure and Composition……………..38

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1. Field Mapping:
1a. Measuring Your Pace:
(a) Calculate the average for number of meters per pace:
20/((13.5+13.5+13.5+13.5+13.0)/5)= 1.49 meters/pace
(b). Calculate the average for number of feet per pace:
(3.28)(1.49)=4.88
(c). Calculate the average for estimate of the accuracy of your pace (by finding the standard deviation):
𝑠𝑞𝑟𝑡((13.5 − 13.4)^2+(13.5-13.4)^2+ ((13.5 − 13.4)^2+(13.5-13.4)^2+(13.0-13.4)^2))/(5-1)= .112 pace
1b. Measuring Horizontal Distances: Find the mean and the standard deviation for each method by pooling all estimates from
each student in your group. Make a plot (on graph paper) of the mean and standard deviation of each method. Compare the
measurements… Which method was the most precise? Which do you think was the most accurate and why?
Luke Chris Ariel Average Standard Deviation
Pacing
13.5
13 13
13.3 0.173 paces
Tape Measure 20m 20m 20m 20m 0m
Laser
20.25 19.86 20.12 20.08 0.367m
Sonic Range
Finder
19.9 19.8 19.8 19.83 0.041m

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The tape measure was the most accurate as the sonic range finder was more susceptible to atmospheric variation (i.e.
humidity, particulate matter in the air etc.). The Laser however, showed to be more precise but was difficult to keep steady
through the leaves and grass.
Pacing: 𝑠𝑞𝑟𝑡((13.5 − 13.3)^2+(13-13.3)^2+ ((13 − 13.3)^2))/(3-1)= 0.173 paces
Tape Measure: 𝑠𝑞𝑟𝑡((20.0 − 20.0)^2+(20.0-20.0)^2+ ((20.0 − 20.0)^2))/(3-1)= 0m
Laser: 𝑠𝑞𝑟𝑡((20.25 − 20.08)^2+(19.86-20.08)^2+ ((20.12 − 20.8)^2))/(3-1)= 0.367m
Sonic Range Finder: 𝑠𝑞𝑟𝑡((19.9 − 19.83)^2+(19.8-19.83)^2+ ((19.8 − 19.83)^2))/(3-1)= 0.041m

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6.
6
0
5
10
15
20
25
Me
t
e
rs
Categories
Pace
Tape
Measure
Laser
Sonic
Range
Finder

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1c. Walking a Bearing:
Here, we were to work into groups of two. However, my group consisted of
three people. Chris, Ariel and Myself. We took a tape measure. One stationary
person held the measuring wheel, while the walking person held the end of the
tape measure and the compass. We walked various 50 meter bearing(s),–see
image above. Which we then placed flags at each end of the tape and rolled the
tape measure up. Then the stationary person walked to the far end and then
switched the jobs, in which the other person now holds the compass and
walked the reversed bearing, laying out another flags at the end of each 50m
distance trying to be precise and accurate as possible. The image above shows
the difference in accuracy (error) in our readings. Chris was involved in both
groups walking a 50m.

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1d. Mapping Into a Baseline:

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1e. Mapping Using Distance and Azimuth:
Compass Tape
Measure
Azimuth Angle
A-Tree 310 17.8 18.88 330
B-Pole 102 37.5 31.95 109.5
C-Tree
branch by
camp tent
156 21.16 18.80 157.1
Since the class was rained out during the “Mapping Using Distance and Azimuth” exercise, an alternative area
was provided for my group a few days later. The alternative site used was our base camp. We set up the transit
and only took measurements of 3 objects. Tree, campsite pole, and a tree branch near the tent of the base camp.
The below image is of the tape measure and the compass to scale. In conclusion, the Azimuth was the most
accurate but this method was also the most sensitive to use.

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This is the original image drawn into my field book. You can see my rough sketch of tree A, Pole, and the other tree
branch.

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Here you can see me drawing the lines to scale using the compass and tape measure.

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Below is three images overlapped into one using Adobe Illustrator. The first image was taken from my field journal; it
shows the layout of the camp. The second, (darker black line) is the method measurement compass and tape measure and
lastly the third, (thin grey line) is the method of measurement azimuth and Angle. This drawing is to scale….. For every 20
pixels equals 1 meter. Again, below is a screenshot of my work.

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1f. Laying Out Square Plots
To the left is where my group and I (Ariel, Chris, and Myself) laid out a
20m x 20m square plot, with sides oriented N / S, - using mapping
(walking) a bearing method. There are three steps involved with walking a
bearing, 1. Orient the Map. 2. Keep the Red in the Shed (Find North). 3.
Look at bearing (ending in degrees). We had Anika double check our work
and she found two of our bearings were off. (The site we used was located
in area that was dense flora; the ground had several sudden 2-4 foot gradual
slopes that made walking a bearing, measuring, and placing the marker
flags difficult. Eventually, Professor Dubayah stepped in advised us that
we should have picked a spot that was more flat and clear of briers, trees,
etc. We were then able to establish our 20mx20m plot subdivided into 4
10mx10m subplots (as seen to the left).

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2. GPS MAPPING& NAVIGATION:

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2a. Basic Functions / Mapping lines and Polygons:
The pictures to the left show Chris and I
messing around some of the basic
functions of our GPS. We were able to
mark landmarks in terms of waypoints as
well as establish treks (routes). The GPS
used was a Garmin etrex (small screen,
short battery life but easy was very user
friendly). Chris and I established a
waypoint called Cplot and established a
70-degree bearing to Cplot. We recorded
waypoints automatically (every 1 minute)
to give us a track of our route.
Pretty Cool!

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We were able to identify several limitations of the GPS, which include a limited # of satellites
picked up by the GPS receiver: cloud cover and tree canopy cover had inhibited the GPS receiver
from picking up satellites. The number of satellites available in the area (the more satellites
available the more accurate-3 satellites gives you northing and easting while having 4 or more
satellites gives you elevation, only having access to 12 of 24. Where the other 12 are not available to
the general public. The accuracy of the GPS really depends on the number of satellite. I believe you
can pay for a subscription to some companies fro the additional access to more satellites. For Chris
and I, the tape measure and the compass worked the best in the dense forest canopy. The GPS
works well in better conditions (clear skies, limited cloud cover, less tree cover, less particulate
matter suspended within the atmosphere etc). As you can see from the above image, there was a 1.9-
meter difference between the two methods.

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2b. Marking your Position and Mapping Points:

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2. Land Cover Validation:

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In order to properly understand how to read a map, you must understand the maps key. The key identifies water,
schools houses, airports, private property, public property, roads, vegetation, elevation etc. When exploring the outdoors with
a map and a compass, you must be able to measure distances on the map and know how to convert the measurements into
distances in the field. This is where a map scale comes in, the scale helps doing the proper map to distance conversions. For
example, if we have a map in the scale of 1:28,000 and we measure that a particular distance on our map is one inch, then the
corresponding distance in the field is 2000 feet (1 inch x 28,000 = 2000 feet). Now that we understand the key and scale we
must properly orient the map. This will allow us to better read it. You can do this with a compass by offsetting the angle of
deflection and you can do this (less accurately) simply by locating a landmark on the map and orient your body so that you
and the object are facing the same way (North, South, East or West). Contour lines on the map also come in useful. They show
steepness or the gradual decline of a slope. The closer the lines are together, the steeper the slope is; the further away the lines
are away from each other the more gradual the slope tends to be. The intervals at which the contour lines are presented vary,
sometimes they are 20 feet, while others are 100 feet, all of which depends on preciseness and detail of the map you are looking
at.

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Confusion Matrix
Above is our confusion matrix that depicts two different types of data that of which was observed by the class and the
information that was predicted by the MRLC/ NLCD. There were definitely inconsistencies in the data. Only 64 class records
matched NLCD data, this is out of 266 total sites. That means the percentage of records that matched was only 28.3%. The
data set at which data was derived from is the Landsat TM 30m multispectral satellite. It is limited to taking pictures of the
earth’s surface with 30-meter pixels (our plots were 10x10 and 20x20). The swath the Landsat TM satellite is smaller as well,
hindering its capability to portray up to date information (spreading of a forest fire). Species diversity changes overtime due

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to the change in climate, amount of precipitation that fluctuates from region to region, anthropogenic effects, altitude (less
oxygen in the air at higher elevations). Since Different surfaces reflect different wavelengths differently (because of the
reflective properties of the surface), Landsat TM can distinguish between water, fields, different types of forests, urbanized
environments etc. Deciduous Forests were the most accurate when compared to the other land cover types documented. I
believe the vast difference in data directly correlates to 1. Satellite limitations: The NLCD we used is old and out of date. It
does not update quick enough to document change. Which allows for time for disturbances to take place (fires, invasive
species, major storms) and regrowth (succession) to set in. 2 The Landsat TM 30m multispectral satellite swaths is limited to
30 meters (pixelated data causing distortion of actual results) and can not keep up with data that is out of its 30m path. 3.
Individuals within the class were limited to what they could see out in the field (dense forest obstructs a specific species of trees
that could have caused the land cover to be misinterpreted. 4. The conifers in our sample were progressively smaller in size
when comparing to the deciduous forests. The NLCD data could have misidentified a land cover type as being “deciduous”
(deciduous trees like the tulip poplar are very tall with a high leaf area index (LAI)… masking the smaller cedars and pines
that flourished underneath the canopy of the hardwoods. Lastly I found that the differentiating between agriculture, pasture,
and herbaceous land was fairly difficult and left room for misidentification as they are quite similar.

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4. Soils:
There are five different factors that affect soil formation; they are parent material, topography, climate, time and
organisms. The first site was located at the base of a mountain; was colluvial in that of all of the material on the ground was
eroded parent material from the mountain. There were big boulders that were displaced around our site (which only validates
the erosion process) and soil that was mixed with different size stones from golf ball size to larger boulder size stones. The soil
layers were dark , grey and reddish in color (the deep reddish color is normally a good sign that the iron content has been
exposed to the oxygen in the water molecule H2O (oxidation of iron in soil forms rust). The next soil site was a huge cut into a
large hill (this site enabled us to pick apart the different layers more clearly), the layers mainly consisted of shale and sediment
deposits. The last site was an excavation hole located near a river, just to the side of a cornfield. The field was apart of the
river’s natural flood plain some time in the past. Where it had flooded several times. Creating mixed interpretation of the soil
results from the class. A cycle of flooding had occurred, leading to redisposition of sediment over the original O and A layers.
This site was the most interesting to me. Below are members of my team interpreting soil layers with Jared, the on site soil
scientist.

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Site 1:
HORIZO
N
TOP
DEPTH
BOTTOM
DEPTH
MOISTURE STRUCTURE COLOR CONSISTENC
Y
TEXTURE ROCKS ROOTS
O 0 4 moist granular 10yr 3/2 loose silt loam none many
OA 4 12 moist granular 10yr 4/3 loose silt loam few many
A 12 23.5 dry blocky 10yr 3/2 loose silt loam few few
E 23.5 35.5 dry blocky 10yr 5/3 friable sandy loam few few
EB 35.5 62 moist blocky 10yr 6/6 firm sandy clay
loam
none none
BE 62 100 moist blocky 10yr 7/6 firm silt clay loam none none
B 100 137 moist blocky 10yr 5/4 firm silt clay none none

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Site 2:
HORIZON TOP DEPTH BOTTO
M
DEPTH
MOISTURE COLOR STRUCTUR
E
CONSIS
TENCY
ROCK ROOTS TEXTURE
Ap
0 9.5 10yr
3/2
granular many
C 9.5 12 10yr
3/2
single
grained
many
AB1 12 19 10yr
3/2
coarse sub-
angular
AB2 19 28 7.5yr
3/2
coarse sub-
angular
B 28 35 wet 10yr
4/2
coarse sub-
angular

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5. Stream Hydrology:
There were several errors that we identified in our field books, these errors were mostly due to our lack of
technique (documenting consistent units of measurement (cm, m, feet)). At times we inadvertently moved the
pole when looking through the transect, the river bed was slippery and was uneven. Which made keeping the
water gauge steady difficult. Broken equipment made recording water depth challenging (at second velocity
area and slope area sites) as we had to convert units of measurement of all data collected.

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(Note: below is only a partial clip of my velocity area data- I thought it would be redundant to include the other data as it is
already inputted into our graphs data.)

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(Note: below is only a partial clip of my slope area data- I thought it would be redundant to include the other data as it is
already inputted into our graphs data.)

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Site 1 Velocity Area:

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Site 2 Velocity Area:

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Site 1 Slope Area:
Below, what is highlighted in yellow were of initial concern to our team, but correlated ended up directly correlating to the
same results that were portrayed on the graph.

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I went over the numbers several times, trying to figure out what had caused the horizontal curve in the above graph.
Again I believe the measurements (cm, in, m, ft) were recorded incorrectly.
6. Water Quality:
Big Bend (A, B) College Park (A,B)
Test A B A B
Calcium 80 ppm 92 40 48
Magnesium 64 ppm 24 36 27
Hardness 144 116 76 75
Free CO2 2 3 1 6
Chloride 40 30 130 98
Chlorine 0 0 0 0
Chromium 0 0 0 0
Copper 0 0 0 0
Cyanide .15 .15 0.05 0.15
Iron .5 0 0.5 <0.5
Nitrate 0 <2 1 <2
pH 7 8 7 7.5
Phosphorus 1<x<5pp
m closer
to 5
1.0ppm 1.0pp
m
1.0ppm

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Salinity 4.0ppt 2.8ppt 6.0ppt 3.6ppt
Sulfides 0.1 <.2 0.1 <0.2
Dissolved Solids 420 880 420 430
Am. Nitrogen <1 <1 <1 <1
Alkalinity 112 120 72 56
Stream Health
Factors that may affect stream quality are, pollution, water runoff from treated lawns, erosion, seasonal
variation, dissolved oxygen, salinity (road salt) , sedimentation, eutrophication from agricultural runoff as well as
other non point sources. Point source sources like sewage treatment facilities, streets covered with car oil,
sedimentation caused by the expansion of our population (through means of construction), deforestation,
urbanization etc. The increase of impervious surfaces inhibits waters ability to infiltrate through the ground,
increasing the velocity at which it travels. This increases erosion, sedimentation, and within some instances inhibit
the suns rays to pass through healthy waters prevent the necessary growth of flora that is needed for the process of
trophic food to flourish…. Creating a less diversified environment. The best assessment for stream health is one that
would be implemented on a local, regional, national, and international scale (depending on the scale of the
assessment.) A rapid assessment only gives you accurate readings for a smaller local area other wise the results
would be invalid because the broader the scale is, the greater the impact would be (as ecosystems are more diverse

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on larger scales) you and assessment of issues that are of direct concern, for example (the increased dissolved
oxygen in the Chesapeake Bay could be contributed to eutrophication of excess nitrogen for agriculture fields to
grow crop… causing the potential of algal blooms…increased of DO levels… leading to mass fish kills in the area.
Paint Branch had progressively more chloride and Nitrate than that of Big Bend. I contribute the Chloride to the
road salt (Sodium Chloride) being used on the busy roads in the winter. There were traces of chloride in Big Bend,
however these amounts are normal for the area. The Nitrate that was found is directly associated with the poor
agricultural practices up stream of Paint Branch (Delaware Maryland, Virginia, New York) as the are heavy hitters
when it comes to nonpoint sources of nutrient runoff (in forms of overland flow… eutrophication…algal blooms…
increase levels of DO (dissolved oxygen), which could lead to mass fish kills as well as other problems (endocrine
disruption- from hormones pissed out from cows and people that make it’s way into our bay waters). These
differences show the impact of industry, urbanization, deforestation and pollution on our ecosystems. Mans remedy
to solve pollution is through dilution. It’s sad, but true.

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Average Height of Trees vs. slope and aspect
Variable Radius Plots Data
Plot Tall tree height
(m)
Slope (°) Aspect (°)
1v 12.6 10 273
2v 12.2 16 74
3v 10.2 32 128
4v 42.3 11 296
5v 16.5 3 137
Plots of Height vs. Slope
0
5
10
15
20
25
30
35
0
10
20
30
40
50
Slope
(°)
Tree
Height
(m)
Tree
Height
vs
Slope

40.
40
Height vs. Aspect
0
50
100
150
200
250
300
350
0
10
20
30
40
50
Aspect
(°)
Tree
Height
(m)
Tree
Height
vs
Aspect

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As the slope increases, I would expect two things to occure,1. normal increase of elevation: loss of oxygen that is needed
to maximize tree growth (photosynthesis). And 2. The steeper the slope the less time precipitation has to be absorbed into
the ground, washing away the nutrients that is needed for the trees to grow. The more gradual the slope the slower the
water flow from porous ground surfaces, more nutrients tend to pool and store in water / ground reservoirs that washes
are located near gradual sloped areas that are near the water table. Steeper slopes don’t allow the water to infiltrate into
the ground, causing fewer nutrients to be absorbed into the ground, resulting in vegetation that is small and stunted.

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1. Class Data: Variable Radius Plots- Tree Heights vs. Slope, Tree Heights vs. Aspect
Plot Tree Height (m) Slope (°) Aspect (°)
Group 1: 1V 27.5 10 273
2V 27 15 90
3V 40 12 115
3VB 22.4 21 101
4V 46 10 296
5V 22.5 10 40
Group 2: 1V 14.9 10 273
2V 17.7 34 60
3V 12.61 22 106
3VB 14.2 12 133
4V 46.91 12 300
5V 34.2 21 4
Group 3: 1V 12.6 10 273
2V 12.2 16 74
3V 10.2 32 128
4V 42.3 11 296
5V 16.5 3 137
Group 4: 1V 14.5 20 40
2V 10.8 20 40
3V 37 20 116
4V 44.5 18 270
5V 15.7 12 20

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Class Tree Height vs. Slope (Variable Radius Plots)
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
45
50
Slope
in
Degrees
Tree
Height
(m)
Tree
Height
vs
Slope

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Class Tree Height vs. Aspect (Variable Plots)
0
50
100
150
200
250
300
350
0
5
10
15
20
25
30
35
40
45
50
Aspect
in
Degrees
Tree
Height
(m)
Tree
Height
vs
Aspect

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Correlation Statistics for Class Variable Radius Plots:
This is the same as what was in my groups data. As stated above, the lack of steepness is directly related to tree height in that
the nutrients can absorb more efficiently into the ground. Thus resulting in a more nutrient enriched soilà bigger tree. The
aspect is positively correlated with a correlation value of 0.27474. The slope has a negative correlation value to tree height
while the aspect numbers are positively correlated. The results for both the class and my group reinforce this data’s
relationship.

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Basal Area of Variable radius Plots vs. slope and aspect:
Plot Basal Area (sq ft/acre) Slope (°) Aspect(°)
Group 1: 1V 80 10 273
2V 80 15 90
3V 110 12 115
3VB 70 21 101
4V 110 10 296
5V 130 10 40
Group 2: 1V 140 10 273
2V 70 34 60
3V 80 22 106
3VB 90 12 133
4V 220 12 300
5V 90 21 4
Group 3: 1V 150 10 273
2V 100 16 74
3V 140 32 128
4V 120 11 296
5V 160 3 137
Group 4: 1V 220 20 40
2V 180 20 40
3V 130 20 116
4V 140 18 270
5V 120 12 20

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Basal Area vs. Slope: Group 3 Alone
Basal Area vs. Aspect: Group 3 Alone
R²
=
0.06542
0
10
20
30
40
0
50
100
150
200
Slope
in
Degrees
Basal
Area
(sq
ft/acre)
Basal
Area
vs
Slope-­‐Group
3
R²
=
0.04726
0
200
400
0
50
100
150
200
Aspect
in
Degrees
Basal
Area
(sq
ft/acre)
Basal
Area
vs
Aspect-­‐Group
3
I
cannot
see
any
relationship
between
Basal
Area
and
slope
or
Basal
area
and
aspect.
R²
=0.06542;
is
not
a
high
enough
value
to
portray
a
true
link
between
the
Basal
Area
and
Slope
for
our
group.
I
would
have
guessed
this
to
be
the
case
as
shown
in
the
previous
results.

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Basal Area vs. Slope: Class Variable Radius Plots
Basal Area vs Aspect: Class Variable Radius Plots
R²
=
0.03577
0
10
20
30
40
0
50
100
150
200
250
Slope
in
Degrees
Basal
Area
(sq
ft/acre)
Basal
Area
vs
Slope
R²
=
0.02731
0
50
100
150
200
250
300
350
0
50
100
150
200
250
Aspect
in
Degrees
Basal
Area
(sq
ft/acre)
Basal
Area
vs
Aspect
As
shown
on
the
graphs
on
the
left,
the
R²
value
for
Basal
Area
vs.
Aspect
s
0.02731
and
Basal
Area
vs.
Slope
is
0.03577.
Again,
no
correlation
is
shown
between
the
two.
From
the
previous
results
on
proceeding
pages
I
did
not
I
did
not
anticipate
to
seeing
a
big
link
here.

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As
expected,
the
Diameter
Breast
Height
(DBH)
has
a
strong
relationship.
By
increasing
the
trees
diameter
of
the
tree
biomass
also
increases.
0
1
2
3
4
5
6
7
8
0
20
40
60
80
100
Tree
Biomass
DBH
DBH
vs.
Tree
Biomass
Tree
Biomass

50.
50
0
1
2
3
4
5
6
7
8
0
5
10
15
20
25
30
35
40
45
Tree
Biomass
Tree
Height
(m)
Average
Plot
Height
vs
Tree
Biomass
Tree
Biomass

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0
1
2
3
4
5
6
7
8
0
5
10
15
20
25
30
35
40
45
Tree
Biomass
Tree
Height
(m)
Average
Plot
Height
vs
Tree
Biomass
Tree
Biomass

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Softwood vs. Hardwood Biomass
0
50
100
150
200
250
300
350
400
Hardwood
Softwood
Stemwood
Biomass
Total
Biomass

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Variable Radius vs. Fixed Plots
Group 1 Group 2
Plot 1
Fixed
1
Variable
3
Fixed
3
Variable
1
Fixed
1
Variable
3
Fixed
3 Variable
Tree Height
(m)
24.25 27.5 34.55 40 14.15 14.9 12.785 12.61
Basal Area
(sq
ft/hectacre)
375 160 167.14 110 300 140 217 80
Biomass
(Mg/ha)
261.5 27.96 178.5 13.3
Group 3 Group 4
Plot 1 Fixed 1
Variable
3 Fixed 3
Variable
1 Fixed 1
Variable
3 Fixed 3
Variable
Tree
Height
(m)
15.075 12.6 14.4 10.2 15.175 14.5 38.125 37
Basal
Area (sq
ft/acre)
329.25 150 276.25 140 388.175 220 354.9 130
Biomass
(Mg/ha)
218.0 19.95 28.25 26.4

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You can also take the biomass from smaller trees, by simply measuring the diameter of a stick using it as a model;
marking the stick with the diameter measured. Like below. During the field exercises, all groups took down their plots to
quickly for us to try to measure the biomass of all the tree saplings and tree branches in the area. This would give us a
more precise number for the total biomass of the sampled plots.

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