Sunday, May 14, 2017

Navigation Mapping Exercise

Introduction and Methods
While less of a formal exercise, this activity served to introduce students to the concept of navigating via map and GPS. Utilizing a navigation map previously created in the Development of a Field Navigation Map exercise )Figure 1) groups of students were tasked with marking five trees along a predetermined course of GPS coordinates. The personally assigned group and course number was 4, and the course was based off of this list of GPS coordinates:
Course 1
1 617713, 4958075
2 617767, 4958224
3 617640, 4958159
4 617553, 4958074
5 617579, 4957938
Course 2
1 618127, 4958040
2 618325, 4958110
3 618169, 4958200
4 617978, 4958196
5 617877, 4958151

Course 3
1 617708, 4958257
2 617930, 4957946
3 617619, 4958049
4 617852, 4958136
5 617695, 4958123

Course 4
1 617591, 4958171
2 617627, 4958343
3 617640, 4958444
4 617520, 4958456
5 617537, 4958263

Course 5
1 618011, 4957883
2 618093, 4957823
3 618107, 4957942
4 618195, 4957878
5 618220, 4957840
Figure 1: A navigation map utilized be groups when moving through their assigned courses.

At each GPS coordinate on Route 4, a tree was marked with both the route number and which point it was on the route. Utilizing the GPS which was synced to report back data as groups moved, a map was generated to display how each group moved on their target route (Figure 2).
As can be seen by the course data, each group did not move in a direct line between points. This is in part due to the sometimes rapid elevation shifts in the area, where the elevation may drop or rise 15 meters. These rapid changes in elevation can be seen by observing the contour lines on the navigation map. To more easily navigate these areas, groups appeared to follow the ridges as they wound around rather than attempt to scale them, in most cases. Unfortunately, there were some errors in the data collection during the navigation exercise. Groups 1 and 5 failed to have their GPS units properly turned on and synced. Thus, the exact path each of these groups took through their routes was not recorded. If this exercise were to be repeated, ensuring these devices were properly active would be of utmost priority. In addition, this route data could be spatially analysed for the total distance each group traveled and compared to the minimum possible distance on each group route.

Monday, May 1, 2017

Topographical Survey with Survey Grade GPS and Total Station

Background
As the final major exercise for this course, this activity served to demonstrate the process of surveying an area utilizing industrial grade survey equipment. Utilizing a Dual Frequency Survey Grade GPS, soil-based thermometer, pH indicator, and a TDR probe, students collected data on a community garden located in Eau Claire, Wisconsin. In addition, a survey grade UAS drone was utilized to collect surface imaging which was later processed in conjunction with the collected soil survey data.
Study Area
The study area for this assignment was a community garden located in Eau Claire, Wisconsin (Figure 1). Coincidentally, the instructor was utilizing a plot in this garden to grow garlic. Due to the temperamental nature of some plant species, it is vital to know the soil characteristics in order for the desired crop to grow properly. Every plant has its own little preferences for soil pH, moisture content, and temperature. By knowing the soil characteristics, a plot can be altered to better fit the growing conditions of a desired crop.

Figure 1: A google map view of the community garden, located in Eau Claire, Wisconsin, which was surveyed for soil characteristics on April 26, 2017
Methods
First, a series of locations were marked throughout the site with flags. Then, utilizing a Dual Frequency Survey Grade GPS, location data was taken for each of these flagged points (Figure 2).
Figure 2: A surveyor collecting data on soil points in the community garden utilizing the Dual Frequency Survey Grade GPS.
In addition to recording X and Y GPS positioning, the Survey Grade GPS was set up to accept pH, moisture content, and temperature data entries for each point. Soil temperature was measured in degrees Celsius at each location utilizing a thermometer whose probe was inserted into the soil (Figure 3).
Figure 3: A surveyor collecting soil
temperature data utilizing a soil 
thermometer. Temperature was 
collected in degrees Celsius.
Soil pH was measured utilizing a special probe that could analysis a soil sample mixed with diluted water (Figure 4). Finally, soil
Figure 4: A surveyor utillizng a special probe to record the pH
of a soil sample by mixed it with diluted water.
moisture content was first measured with a TDR probe and then entered into the Survey Grade GPS(Figure 5). Once every point had been successfully measured for location, pH, moisture content, and temperature, the survey was completed for that day, April 26, 2017.
At a later date, My 3, 2017, the class returned to the site to conduct a surface mapping utilizing a survey grade unmanned aerial drone (Figure 6). This drone would collect surface data of the site in
Figure 7: A ground control point
used to program the flight path of
the UAS drone.
conjunction with a preprogrammed flight path that utilized a number of ground control points (Figure 7). These gound control points required their GPS coordinates to first be collected with the Survey Grade GPS befor the course could be set. Once the course was set, the drone could automatically take the required data over the flight course. Once this data was collected, it was brought back to the lab, where it along with the point data was formatted for usage in ArcGIS and used for analysis.
Figure 6: The industrial grade survey drone utilized to collect surface imagery
of the community garden siteand the surrounding area.
















Results
First, the UAS drone data was processed in Pix4D to create a a mosaic image and DSM elevation raster of the area surrounding the community garden (Figure 8, 9).
Figure 8: A mosaic image map of the 
community garden site created from the 
collected drone data and processed in Pix4D.
Figure 9: A DSM map of the community 
garden site and the surrounding area displaying
the elevation of the site. This raster image was
generated by processing the collected drone data
in Pix4D

The image mosaic provides accurate imaging for the site at the time of recording, as the community garden site changes frequently over the course of the year. The elevation in the area is also relatively static, as can be seen from the DSM. Elevation Does not generally exceed 270 meters. The minimum for the area is just under 267 meters, and the high of 280 meters are a result of the drone recording the tropes of trees as ground level data. Utilizing the mosaic image, a map was created showing the location of each recorded point in the community garden (Figure 10). Due to time constraints, much
of the garden remained unfortunately unmapped. However, this point data was utilized to generate a series of surface models mapping the soil temperature, pH, moisture content, and elevation of the recorded area using a kriging interpolation method.
Figure 10: A map showing the points where soil data was taken in the community garden 
on April 26, 2017, in Eau Claire, Wisconsin. The entire garden could not be surveyed due to 
time constraints. 
The elevation of the surveyed area does not change greatly throughout the site. Based on the interpolation, elevation rises from a minimum at 268.888 meters on the western side of the site to a high of 269.44 meters on the eastern side of the site. This is only a variation of less than one meter, and is gradual. As a result, elevation should not be a factor when it comes to planting crops in the garden. However, it may be of some significance when it comes to explaining other types of soil data.

Figure 11: An elevation map of the surveyed area of the Eau Claire community garden. The surface model was generated using a kriging spatial analyst interpolation method.
Similarly to the elevation data, soil temperature varies only mildly across the mapped site (Figure 12). It rises from a low of 12.0756 degrees Celsius to the west to a high of 12.8179 degrees Celsius in the east. This is a variation of less than 1 degrees Celsius. However, this variation is not constant as the model moves from west to east. There exist pockets of slightly higher temperature areas, likely as individual plots change. I would appear that the recently tilled plots retain temperature slightly more than the untilled areas. However, the change in temperature is so small that this could merely be conjecture and a result of acceptable deviation in the data. This is because the freshly tilled lot in the far southern portion of the surveyed area does not seem to maintain this trend.
Figure 12: A surface map showing the soil temperature of the surveyed area of the Eau Claire community garden. This model was generated utilizing the point data and the kriging interpolation method.
The pH of the surveyed area is perhaps a bit more complex. The lowest pH areas exist in the northern, northwestern, southwestern, and south-central surveyed areas. In contrast, the areas with the highest pH value are located in the western and central portions of the surveyed area (Figure 13). Based on the interpolated data, pH may vary between a value of 6.86 to 8.145. While this may seem like very little, a change in pH from 7 to 6 indicates an order of magnitude in change of acidity. Anything below a pH of 7 is consider acidic and anything above a pH of 7 is considered basic. While pH also appears to vary partially between plots as a result of fertilizer compounds being added to plots, it also follows a general trend of decreasing as elevation decreases. This may imply  that acidic compounds are carried to a lower elevation as it rains by flowing water.
Figure 13: A surface model map showing the soil pH of the surveyed area of the Eau Claire community garden. This model was generated by utilizing the point data in conjunction with the kriging interpolation method.

Finally, the moisture content of the soil was analysed (Figure 14). Based on this data, several notable observations can be made. Areas to the far north and the far west have the lowest moisture content, approaching 13.94% water, while areas in southern and central portions of the survey have the highest moisture content, approaching 20.9% water. Additionally, moisture content appears to start to decrease in the eastern portions of the surveyed area, but doesn't seem to fall below a 17% moisture content. What's interesting is that these trend seem almost independent of other soil characteristic patterns. While temperature could only halfheartedly be linked to the tilling of the soil, the soil moisture content seems to fit this trend near perfectly. The darker plots, which indicates resent tilling of the soil, have the highest moisture content and the relatively untilled plots have the lowest moisture content.


Conclusion
Based on the data, temperature and elevation have the least variation and are likely a factor of environmental conditions while pH and soil moisture content are a result of human interaction in the fields, adding compounds or tilling the plots. While this data shows general trends of the Eau Claire community garden, it does not map the entire site. However, generating data trends was not the main focus of this exercise. This survey was designed to familiarize students with survey grade GPS systems capable of plotting a point with only centimeters of error. This has been far more accurate than any other system previously utilized in this course, and would likely be used by a student after they became employed after college.