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.

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.

Arc Collector 2: Creating your Own Database, Features, and Domains for Deployment and Use in ArcCollector

Background
With the completion of the previous exercise, students were now familiar with the workings of ArcCollector. With this knowledge, each student was tasked with developing a personal project, building the necessary geodatabase, feature classes, domains, and collecting the data individually using ArcCollector. When setting up the project, designing and implementing the domains and fields of features was critical. Domains are what limit what kinds of data can be recorded in attribute fields of feature classes. Without these, the possibilities for for data entry error are numerous. In addition, if the domains and fields are not assigned the proper field type, proper spatial and statistical analysis cannot be performed.
For this project, the focus of the data collection was on the bike racks located on the University of Wisconsin, Eau Claire Campus. In the last three months, construction began on the Garfield Avenue project, resulting in this area being cleared for construction. This included removing the available bike racks located in this area. With this being the case, the survey focused on the question "at what bike racks are students parking their bikes on campus and how much available space is at each location?" This survey was designed to identify the areas with the bike rack locations with the least and greatest maximum storage and compare it to the amount of the space being used.
Study Area

The study area for this survey would encompass most of the University of Wisconsin, Eau Claire campus (Figure 1). In addition, this data would be recorded at two separate times during a school day (Monday-Thursday), once at 9:30 AM- 12:00 PM and once at 6:00 PM - 8:30 PM. This would allow for the comparison for the differences between usage of bike rack at the beginning of the school day and the end of the school day. in addition, data would be gathered recording the weather and date of recording. Ideally, this survey would be collected daily over multiple weeks to get a proper survey and rule out possible outliers in addition to recording changes due to day of the week and weather. However, with the limited time available for this exercise (April 20-25), three viable days for data collection (April 20, 24, 25), and schedule constraints on the one surveyor, it was decided that data would be recorded once for each bike rack in the morning and once in the evening. The evening data points would be collected on April 24 (and April 25 for one location that was initially missed), and the morning data points would be collected on the morning of April 25. The weather and date recordings would serve as footnotes for those who were interested in looking over the data.

Figure 1: A map displaying the survey area for the ArcCollector bike rack usage survey completed on Arpil 24 and 25, 2017. In addition, the map also shows an area currently cleared for construction which has displaced a number of the bike racks, among other surface features.

Methods
First, a project geodatabase was created in ArcCatalogue. Within the Geodatabase properties, the domains were accessed. Several domains were generated to limit and monitor the future data collection:

• Bike Number: A long integer domain for the number of bikes currently present at a given bike rack. The range was set to 0-400, showing that a bike rack could be empty up to containing 400 bikes. This range was determined as it was roughly 60% larger than the maximum possible storage for a bike rack located on campus. This would allow for data collection beyond the current limit if the University ever decided to expand the storage at a given location
• Maximum Occupancy: A long inter domain for the maximum number of bikes a given rack was designed to hold. The range was similar set to 0-400 as the bike number domain, for the same reason. The bike number domain at a location could exceed the maximum occupancy if students stored their bikes in a way the rack was not originally designed to.
• Time: A long integer domain for the time of data point recording for each bike rack, with a range of 0-2359. This would allow for the recording of military time in a way that it could be easily separated later for analysis.
• Weather Conditions: A coded value text domain for the weather conditions at the given time of recording. This was set to codes for Clear (<50 cloud="" cloudy="" cover="">50% cloud cover), Rain(>90% cloud cover and raining), Snow (>90% cloud cover and snowing), and Other (Sandstorm, hail, other unlikely forms of weather).
• Two other domains for Date and Notes were created but ultimately not implemented, as they interfered with data entry for these fields. Students were instructed to avoid utilizing a Date domain due to the complexity and difficulty in implementing such a domain.

Once the geodatabase was created, three feature classes were created in the geodatabase:

• A point shapefile for bike racks
• A polygon shapefile for the construction area on Garfield Avenue
• A polygon shapefile denoting the primary areas on campus, and the most likely locations for bike racks owned and maintained by the University to be located.

These shapefiles were projected into the WGS 1984 Web Mercator Auxiliary Sphere Projected Coordinate System, as this projection worked best with ArcGIS online and ArcCollector. In the properties of the bike rack feature class, additional fields were created for the data which would be recorded:

• Bikes: Long integer data field for the number of bikes at a bike rack. Connected to the Bike Number domain.
• Max_Occupancy: Long integer data field for the maximum occupancy of a bike rack. This was connected to the Maximum Occupancy domain.
• Time: Long integer domain for the time of data point collection, in military time. This was linked to the Time domain
• Weather. Coded text field for the weather conditions at the time of data point collection. This was linked to the Weather Conditions domain.
• Date: A text field for the date of data point collection, recorded as mm/dd/yyyy. This was not linked to a domain, but was given a maximum number of 10 characters in length.
• Notes: An additional text filed for any notes or critical observations on a given data point or bike rack. This field was generally left blank.

Once these fields were created, the feature classes were opened into an ArcMap viewer with the same coordinate system as themselves. An editing session, along with a basemap were used to generate polygons for both the construction area and the study area around campus (Figure 1). In addition, the fields of the bike rack feature class were tested for validity and to make sure they were maintained by the domains. Once verified, the basemap was removed and the data frame was uploaded to the UWEC personal enterprise account as an editable, updatable, and remotely syncable  service. This service was additionally made to be only personally accessible.
In ArcGIS online, the feature layer was saved as a remotely editable map. This allowed for the insert and removal of data to any of the feature classes online via computer or remotely via cellular device or tablet. Once this was completed, the data could be collected. As previously described, all but one of the data points for the bike racks in the evening (6:00 PM- 8:30 PM) were recorded on April 24, and the locations and corresponding fields were collected a second time on the morning of April 25. This was done remotely via ArcCollector on a mobile device. Once the data collection was complete, it was viewed in ArcGIS online. With this, an online, interactive, and public map was generated to display the data. In addition, the online map document was opened in ArcMap, and the feature classes saved offline. This data was utilized to created a series of maps which displayed the most critical results of the study.

Figure 2: An interactive map displaying the data collected during the bike rack usage ArcCollector Survey on April 
24-25, 2017, on the University of Wisconsin, Eau Claire campus. The pairs of points are a result of collecting data for each bike rack twice, once during the morning and once in the evening. Data displayed also includes the study area and area of construction on Garfield Avenue.  
Results
Looking at the results of the survey, several conclusion can be made(Figure 3). The number of bikes on racks in the western portions of campus don't really change, regardless of the time. However, racks in the northern and eastern portions of campus appear to hold more bikes during the morning hours than the evening. This was expected, as the eastern and northern portions of campus are primarily occupied by lecture halls which are more active during the morning to afternoon. What is surprising is the seemingly lack of change in the western bike racks, which primarily surround dorm halls. Only a small number of bike racks show a decrease easily noticeable decrease between 9:30 AM and 12:00 PM, a couple of racks located near center of the study area. This would imply that the increase in bike storage in the eastern and northern portions of campus, during the day, are largely a result students who live off campus biking to school in the morning. In regards to the number of bikes stored at a given located, it seems to be tied to the size and accessibility of the building next to the rack, in addition to the visibility of the rack. The more openingly visible racks next to large buildings hold the greatest number of bikes, which cannot be easily seen by looking at a map. However, these individual numbers matter less if the total possible storage is not looked into. Without knowing the maximum available storage, it cannot be determined which racks are filling up.

Figure 3: A map depicting the number of bikes stored ate bike racks on the University of Wisconsin, Eau Claire Campus. The data was collected from April 24-25, 2017, and shows each bike rack during the morning (left) and during the evening(right).

When looking at the maximum possible storage at bike racks (Figure 3) on campus, it can be seen that many offer tremendous storage. The largest bike racks can hold anywhere from 97 to 249 bikes. These largest racks are typically located by themselves in central locations. The smaller racks generally are located on the periphery of campus, and are sometimes in small groups. The mid-sized racks are scattered throughout the remaining areas. What's important with this data is that it can be used mathematically with the number of bikes at racks to calculate the available storage that was used.

Figure 4: A map depicted the maximum possible occupancy of bike racks on the University of Wisconsin, Eau Claire campus on April 24-25, 2017.

When looking at the percentage of available storage used (Figure 5), it can be seen that most of the available space is not completely used during either the morning or evening. However, there exist several locations where the available storage was either completely used or exceeded. In these cases where the available storage was exceeded, bikes could be found coupled to nearby trees, signs, and poles. When compared to the total available storage, it was found that these filled areas also have the smallest possible maximum storage of all the bike racks. This means that these locations fill up quickly. Whats strange is that all but one of these four locations are filled throughout the day, and are located next to an almost empty rack. Only one location whose storage is used completely empties at the end of the day. Its located on the far eastern border of campus. In addition, it can be more clearly seen that areas to the north and east empty at the end of the day, while most racks in the center and western portions of campus remain relatively constant from the beginning of the day to the end. In response to this data, a recommendation could be made to increase the available storage at the filled or nearly filled locations.

Figure 5: A map depicted the percentage of available storage used on bike racks on the University of Wisconsin, Eau Claire campus. This data was recorded on April 24-25, 2017, and shows bike racks during the the morning and evening. Percentage used was calculated by dividing the number of bikes present by the total available storage. An IDW interpolation was utilized to create a continuous surface to display the data.

Discussion
Proper database creation and domain field implementation allowed for the proper data collection and analysis completed within this project. Without it, the bike number and maximum occupancy data could not have been properly displayed and analyzed. This study, up to this point, shows that bike rack occupancy increases in the eastern and northern portions of campus early in the pay and empties near the end. The southern, central,  and western portions change little throughout the day. A small number of bike racks completely fill up, and these should be increased in size to fit the demand. However, these bike racks have the smallest maximum occupancy limit, and the largest bike racks which are shown to hold the greatest number of bikes do not fill completely. Unfortunately, weather data wasn't able to be properly explored, due to the limited time available for data collection. Ideally, this study could be repeated, with data collecting taking place over several weeks. That way, proper statistical analysis could be performed for weather data in conjunction with the bike rack usage data.

ArcCollector Part 1: An Introduction to Gathering Geospatial Data on a Mobile Device

Introduction
With the introduction of powerful and easily obtained smartphones, GPS units have largely been replaced when collecting location data. Additionally, smartphones have the ability to access recorded data on the go and monitor survey progress through the study area as its continually updated. In this lab exercised, one such smartphone based survey system would be introduced and utilized, ArcCollector. ArcCollector is an Esri based mobile GIS application allowing for the recording of multiple attributes of survey locations based on predetermined domains by many surveyors at once. Domains are essentially rules that limited what type of data, quantitative or qualitative, can be entered into an attribute field, and what range or descriptive data may be entered.
Study Area
The study area was the University of Wisconsin, Eau Claire campus, particularly the areas surrounding the primary area. As the area was far too large for a student to survey completely in the allotted time, the study area was divided into seven zones, labeled numerically, and each student was assigned to help survey a particular zone (Figure 1).

Figure 1: A map of the study zones of the March 29, 2017, micro-climate survey of the University of Wisconsin, Eau Claire campus. Each zone was assigned two or three students from the class to survey, and positional and micro-climate data would be recorded for each zone using the smartphone application ArcCollector.

There were two or three students assigned for each area, and each student would need to survey roughly twenty points to effectively survey the entire study area. Zone 4 was the zone personally assigned. Using ArcCollector, students gathered positional and micro-climate data from within these zones.
Methods
First, each student was tasked with downloading the smartphone application ArcCollector to their personal smartphone, available on both the Google Play and Apple app stores for free. While this was downloading, the data attributes were overviewed, so each student would know what data they were collected. This was primarily micro-climate data, with supplementary time and group data.
The attributes were:

• Group. Listed in the attribute table as GRP. This would show what group collected the data point
• Temperature. Listed in the attribute table as TP. This would be collected with a personal Kestrel pocket weather meter in degrees Fahrenheit.
• Dew point. Listed in the attribute table as DP. This would also be collected with the personal Kestrel pocket weather meter in degrees Fahrenheit.
• Wind chill. Listed in the attribute table as WC. This would be collected with the personal Kestrel pocket weather meter in degrees Fahrenheit as well.
• Wind speed. Listed in the attribute table as WS. This would be collected with the personal Kestrel pocket weather meter in miles per hour.
• Wind direction. Listed in the attribute table as WD. This would be collected by using a compass, either physical or electronic, to measure the direction the wind was coming from in degrees, with North being the designated 0.
• Time. Listed in the attribute table as Time_. This would describe the time each point was collected during the day, and would be listed in military time with the colon removed.
• Notes. A general notes tab for use later. We were instructed to keep this area blank when  collecting data

In order to normally collect data of this kind in ArcCollector, a Geodatabase with the appropriate shapefiles for both the group zones and the survey points must be created, with the appropriate attribute fields and corresponding domains, and uploaded to ArcGIS Online for group use. For the purpose of this exercise, the geodatabase and its shapefiles had already been constructed, uploaded to, and been made available on ArcGIS online for the class. Each student needed to only been given access by the instructor.
Once each student had access to the Online ArcGIS database, they were sent out to gather data points for locations within the zones. This would create point data for the micro-climate point shapefile feature class within the geodatabase. Each student had between 4:10 PM and 5:00 PM on March 29 to gather points data for the feature class. By pushing the "add point" tab within the application, it would create a new datapoint within the shapefile. It would then ask to fill in the attributes listed above. The TP, DP, and WC fields all had domains requiring the input to be a whole number in the normal range of temperatures for this time of year within Eau Claire County. The WS was limited by a domain requiring the input to either be a whole number greater than or equal to 0. The WD field was limited by a domain requiring the input to be a whole number between 0 and 359. However, do to an error created when generating the domains, the time and notes field were not properly limited. The Time field was supposed to have a domain that required the input to be a whole number between 0 and 2359, military time without the colon. However, there was no such domain present. As a result, the field ended up populated by correctly formatted entries (ex: 1636), incorrectly formatted entries (ex: 16:36, 4:36), and null values. In addition, the domain set for the Notes field would demand a form of entry if the field was accidentally accessed during the survey. As a result, this field was occasionally populated by an assortment of values (anything a student could consider a note on the location) and the proper null values. Each student completed roughly 20 survey point in their allotted area before returned to the lab.
Upon the completion of the survey, the data was viewed in ArcGIS online. Using the built in functionalities, it was possible to view the point density as a heat map, for reference. Once it was certain that all data recorded was present, the database was downloaded from ArcGIS online and turned into an offline file geodatabase. The data from the survey was brought into an ArcMap viewer so the data points could be properly viewed (Figure 2).

Figure 2: A map showing the micro-climate survey points collected during the ArcCollector Survey on March 29, 2017, between 4:10 PM and 5:00 PM on the University of Wisconsin, Eau Claire, campus. Each student surveyed roughly twenty points for micro-climate data which including group number (based on group zone), temperature (in degrees Fahrenheit), dew point (in degrees Fahrenheit), wind chill (in degrees Fahrenheit), wind speed (in miles per hour), wind direction, and the time of point collection (in military time).

Once it was ascertained that the data could be properly viewed, it was utilized to create a variety of surface and point maps displaying the collected data.
Results
Using the point data, a surface map could be created of the temperature with the IDW interpolation method and the TP (temperature) field (Figure 3). The data collected shows that the temperature in the northern half of the area is generally below 55 °F, except for scattered points close to the river, while the southwestern and southeastern portions of the map are dominated by areas where the temperature is greater that 55 °F. These areas of highest temperature are generally located directly west of buildings in exposed areas. In addition, the points to the south-east are located at the base of a steep embankment, with elevation increasing as the location moves from the north east to the south-west.This means they received the most exposed sunlight during this time of day, increasing the temperature of these areas. The one notable exception to this trend is the area in the far south, which possesses few tall buildings and is comprised largely of sports fields. This abnormality may be explained by combining a combination of the other micro-climate attributes. However, it does not show strong correlation with either high wind speed or low dew point, meaning that explanation does not likely lie with either of those two factors.

Figure 3: A temperature surface map created of the University of Wisconsin, Eau Claire campus using the data collected in the March, 2017 ArcCollector micro-climate survey. The temperature data was collected in degrees Fahrenheit between 4:10 PM and 5:00 PM. The continuous surface map was generated using the IDW interpolation method.

A wind chill map was created by similarly utilizing the point data and the IDW interpolation method, using the WC field instead of the TP field (Figure 4). When looking at this data, it is clear it exhibits the same general trends as the temperature map. Areas of higher temperature and lower temperature for wind chill line up very well with the areas of higher and lower overall temperature. The major difference is that wind chill has a lower minimum wind chill than overall temperature, 45 °F as compared to 49 °F.

Figure 4: A wind chill surface map created of the University of Wisconsin, Eau Claire, campus using the data collected in the March, 2017, ArcCollector micro-climate survey. The wind chill data was collected in degrees Fahrenheit between 4:10 PM and 5:00 PM. The continuous surface map was generated using the IDW interpolation method.

A dew point surface map was also created utilizing the IDW interpolation method and the survey points. The DP field was the field used in this instance (Figure 5). This data shows some very different trends from temperature and wind chill. The southwestern portions of the survey area, along with the far northern locations and an area in the east-central portion of the study area have the lowest dew points, generally less than 36 °F. In contrast, the portions with the highest dew point, generally greater than 48 °F, are located on either the central to southeast and to locations to the north -central portions of the study area. These areas likely have the highest dew point because they have the greatest ambient moisture in the air. They are either at the bottom of a steep embankment (central to southeast) where water naturally collects, or either ring the river to the north or were taken from a bridge crossing the river. In contrast, the areas of the lowest dew point either sit way to the north of the river, southwest and on top of the steep embankment, or in the flat, open area between the embankment and the river. These are the drier locations with the lowest ambient moisture in the air.

Figure 5: A dew point surface map created of the University of Wisconsin, Eau Claire campus using the data collected in the March, 2017 ArcCollector micro-climate survey. The dew point data was collected in degrees Fahrenheit between 4:10 PM and 5:00 PM. The continuous surface map was generated using the IDW interpolation method.

Utilizing the points dataset in conjunction with the wind speed and wind direction fields, a map could be generating showing both the magnitude and direction of the wind quantitatively and qualitatively as angled, graduated symbols. From looking at the data, it can generally be seen that the wind was blowing from the southeast to the northwest. In locations where the wind was not blowing in this direction, the southeastern wind was usually blocked by either buildings or or forested areas. In addition, the areas of greatest magnitude of wind speed are either on the river, to the far west, or to the far north. These areas are the least population by obstructions which would either block or divert the wind.

Figure 6: A wind speed and direction graduated symbols map created  of the University of Wisconsin, Eau Claire campus using the data collected in the March, 2017 ArcCollector micro-climate survey. The wind speed data was collected in miles per hour, and the wind direction was collected in degrees from its point of original and later angled to show the direction it was blowing towards. Wind speed is show as graduated arrow symbols labeled in miles per hour.

While it would have been appreciated if a map could have been generated for the Time field, it is currently not possible, due to the state of the data on this field. Because of errors in both domain and field entry by both designer and surveyors, the data within the fields is not properly formatted so that it can be quantitatively or qualitatively expressed. It is imperative that domains be properly constructed and formatted to eliminate data entry error by surveyors.
Conclusion
ArcCollector is a valuable and easily accessible tool for completing groups surveys across an area too large for a single individual to properly survey. With it, surveyors can collect and record multiple types of data from various locations without the need for complex GPS equipment. Only a smartphone application is required. In this survey, dozens of datapoints were taken of location across the University of Wisconsin, Eau Claire campus by students in under and hour and contained data describing multiple forms of the local micro-climate. Additionally, it allows for the monitoring of surveyors and the survey progress by accessing the geodatabase from ArcGIS online, either using a computer of smartphone. With ArcGIS online, the data can also be effectively shared and distributed to all analysts within an organization for breakdown and study. However, it is critical that the domains for each field be properly formatted. If it is not, data entry errors can occur when surveyors generate the data, as in the case of the time and note fields of the collected data. However, if this error can be avoided, ArcCollector can serve as a valuable tool for a field data analyst.
Sources
Hupy, J. (2017). Arc Collector: An Introduction to gathering geospatial data on a mobile device, such as a tablet or smartphone. Eau Claire, WI.

Field Activity: Conducting a Distance Azimuth Survey

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Processing UAS Imagery with Pix4D

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

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

  

  Figure 1: An ideal flight plan for maximizing overlap.

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

Sources

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

Using Survey 123 to Gather Survey Data

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

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

Safety check 1: Are televisions in the home secured?

Safety check 2: Are computers in the home secured?

Safety check 3: Are bookcases secured to the walls?

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

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

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

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

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

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

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

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

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

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

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

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

Afterwards, the data export options were explored.

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

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

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

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

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

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

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