Field Activity #8: Collection GPS data with the ArcCollector Mobile App

Introduction
During the field activity, we took advantage of the high functioning of the gps unit and wifi or data connection within a smart phone to create a data collection platform. Surprisingly, many smartphone devices have higher computing power and accuracy than a variety of standard GPS units. Within this lab, we experimented with the ArcCollector app on our personal smartphone devices to create a data collection project. The methods I used to set up my data collection project are detailed below.

Methods
I began this lab by designing a data collection theme. I first began by designating my study area as the UW-Eau Claire campus. In class we first made a practice geodatabase. Originally I was going to do bird sightings and collect data based on this, however after entering domains in such as the species, behavior, and tree type, and observing the bird activity on campus I realized this would be difficult to complete a full data set. After this attempt, I decided I would rather sample stationary object on campus. I chose to survey how many garage disposal bin were paired with a recycle bin, to determine if more recycle bins could be placed around campus. I began by creating a geodatabase in ArcCatalog, along with creating domains, their properties, and optional values. I began by creating my domains of Bin Type, Date, Label Status, Paired, Status, Temp, Time, and Weather (seen in figure 1). I gave a description to each of these to define the domain's definition. While creating each domain, I was required to designate what field type the domain should be classified as, such as text, float, date, double, and short or longer integer. For the text fields, such as Bin Type, I was able to select a coded value domain type. This allowed me to set up selectable options within in domain, for example within Bin Type, I was able to created Recycle or Garage as two separate selected options. Once these domains were created for the geodatabase, I created a feature class called disposal bins. For this feature class, I was able to add domains to the feature class that I had previously created for the geodatabase (seen in figure 2). Once the set up of the geodatabase was complete, I was able to upload to ArcGIS online.

Following the tutorials on the esri website, beginning at Publish your data, I set up my project to be published on ArcGIS online. Once I completed this step, I followed the tutorials on the esri website to Create and share a map for data collection. The steps allowed me to create the map online, such as add a basemap, and organize which order I wanted my domains to appear in my ArcCollector app on my phone. Once I shared my map, I was able to sign into the app on my phone and open the map. The following week I went out a collected a total of 38 data points, and displayed them using ArcMap (figure 3)  In figure 3, the data points are symbolized based on if the garage bin was or was not paired in figure 3.

Figure 1: Database properties window that shows the domain type I created within my geodatabase, along with their description. Within this window, I can specify the domain's field type, domain type, and coded values.

Figure 1: Feature class properties window that shows the domain type from my geodatabase that I added to the feature class. Their data field type is also specified.

Figure 3: Map of garage disposal bins paired or not paired with recycling bins.

Metadata:

Who	Ally Hillstrom
What	ArcCollector GPS Survey
When	Collected on Tuesday, November 17th
Where	The UW-Eau Claire’s Campus Mall.. Eau Claire, Wisconsin
How	GPS Points collected using ArcCollector app on personal Samsung Galaxy 4

Discussion
After completing these data collection methods, it appears that using a smartphone to collect geospatial data is a sound alternative to using a standard GPS unit. Although as a beginner the process of publishing and creating the map on ArcGIS online was somewhat tedious, the process would be much faster and easier the second time around. Once the domains and online map is created, the process of collecting data with your smartphone is user friendly.  Surprisingly, my phone altered me if my accuracy was high, before I took a point, which was a helpful feature to have. However, referring to figure 3, it is clear a few points are inaccurate, however it is likely my data connect was not strong in these locations. It is apparent the points that are inaccurate are close to buildings. If I were to do this survey again, I would be conscious of this and wait at these locations for a longer period of time in an attempt to increase the accuracy of these points. Also, if I were to do another survey like this again, I would make sure to observe what I was collecting data of before I created the domains. As I was surveying, I noticed a few details that I would like to have added to my domain list, such as the orientation of the label on the bin. I would not have know to include this before observing the bins in person. Overall, I would recommend this method of surveying GPS points if you are in an area where your smartphone has either strong data or wifi connection. Thankfully, the wifi connection is strong across campus, aside from the outside of the buildings, which allowed me to take relatively accurate GPS points. If the accuracy of the survey was more critical however, I would like to compare accuracies amongst other survey units before making the final decision on my survey technique.

Conclusion
During this activity, I practiced how to use a smartphone for Geospatial data collection. The process was user-friendly and required minimal data collection time. Knowing smartphones are becoming increasing popular, the lab suggests that this technique may be a more practical, actuate, and less time consuming way to collect geospatial data.

Sources
http://doc.arcgis.com/en/collector/android/create-maps/create-and-share-a-collector-map.htm
http://doc.arcgis.com/en/collector/android/create-maps/prepare-data-desktop.htm

Field Activity #7: Topography Survey

Introduction:
During this activity I practiced using survey grade equipment to gather topography data. of my study area of the University of Wisconsin-Eau Claire Campus Mall. Our goal was to collect 100 GPS points that include a coordinate grid location and elevation data, and process them in ArcMap to display the elevation through the survey site. The method used to collect this data are detailed below.

Methods:

Dual Frequency Survey: 
For this lab we began by choosing a survey area of about 25m x 25m. My partner Morgan and I choose to survey the UW-Eau Claire campus mall. I began our survey process by establishing our own wifi hotspot by using a Version 4g MiFi unit. Next, we began configuring the Telsa. During this, we used the Magnet application however because of technical difficulties we were forced to use the demo mode. Although the functionally of the GPS survey was unaltered, we were only allowed to store 25 points per "job". This required us to make 4 different jobs. Within each of these, we specified the configuration for the GPS survey, such as the projection (UTM Zone 15N), datum (NAD 83 [2011]), and grid as the coordinate type . After this, we clicked on the Connection tab. Within here we were allowed to Bluetooth to the Topcon Hiper SR RTK. After this connected, we clicked on the Survey tab. From here we selected Topography, which brought us to a screen that allowed us to take GPS points.

From here we were able to set up the tripod. The Topcon Hiper was located on top of the pole, and the Tesla was locked into a extended arm in the middle of the pole. Looking at our area, we sectioned it in roughly 4 equal parts and took 25 points in each. At each location we decided to take a point, we leveled out the tripod using the attached level. Once the tripod was balanced we saved the point. We repeated this process 100 times to end with 100 GPS points.

After the survey was complete we disconnected the Tesla from the Hiper and went inside to export the data. To export the data we saved it on a jumpdrive. The file format was saved as a text file, which we were able to view on the computer in the Notepad application. In order for our data to be organized correctly in Arcmap, we were required to edit the header of the Notepad document. I chose to label the header with: Name, Long(X), Lat(Y), Elev(Z). I also chose to copy all the data into one document. Both of these change made it easier when importing the data into ArcMap. To display the data I selected display XY data, followed by Export Data. The GPS points can be seen display in figure 1. Next, to display the elevation of this study area, I ran the Natural Neighbor tool. The output is displayed within figure 2.

Figure 1: The 100 GPS points take in the University of Wisconsin-Eau Claire Campus Mall using the Topcon Telsa and Topcon Hiper.

Figure 2: This figure displays the 100 GPS points take in the University of Wisconsin-Eau Claire Campus Mall using the Topcon Telsa and Topcon Hiper, and the natural neighbor output ran on the point feature class. This output displays the area's elevation data in meters.

 
Total Station Survey:
For this lab we went to the same location of the UW-Eau Claire Campus mall, used within the Dual Frequency Survey. I began our survey process by establishing our own wifi hotspot by using a Version 4g MiFi unit.  Next, we began configuring the Telsa. During this, we used the Magnet application again and located to survey to begin a topography survey, while setting up a job the with the same settings as the Dual Frequency survey above. Using the Telsa and Topcon Hiper, we took three separate gps back sight points which will be used to set the north bearing for the total station. Next we set up the Total Station at our point of origin. This required a lot of small adjustments to balance the equipment. Once this was step up, we powered down the Telsa to Bluetooth it to the Total Station, however the Telsa would not turn back on. This forced us to come out a different day to survey the remain 22 survey points. The following week we resumed our survey using the Total Station. To take each point, my job was to focus the lens to the reflector that my team member Grant was holding. He staggered himself in 22 different locations with our survey area. After our job was full of 25 points, including the Occupy Point (Origin), and the three back sights, we headed indoors to export the data on a jump drive. This allowed us to download the data on a flashdrive and save it as a text file. Once again, I edited the textfile heading, and imported the points into ArcMap. The displayed Total Station GPS point can be seen in figure 3. My next step was too run the Natural Neighbor tool in ArcMap to generate a DEM for this survey. The results can be seen in figure 4.

Figure 3: The 25 GPS points taken in the University of Wisconsin-Eau Claire Campus Mall using the Topcon Total Station.

Figure 4: This figure displays the 25 GPS points take in the University of Wisconsin-Eau Claire Campus Mall using the Topcon Total Station, and the natural neighbor output ran on the point feature class. This output displays the area's elevation data in meters.

Metadata (Dual Frequency):

Who	Ally Hillstrom, Morgan Freeburg
What	Survey Grade GPS Survey (Dual Frequency Survey)
When	Collected on Tuesday, November 10th
Where	The UW-Eau Claire’s Campus Mall, Eau Claire, Wisconsin
How	GPS Points collected using Topcon Hiper and Topcon Telsa, along with Verison MiFi unit to create a hotspot.

Metadata (TotalStation Survey):

Who	Ally Hillstrom, Grant Muehlhauser, and Matt Brueske
What	Survey Grade GPS Survey (Total Station Survey)
When	Collected on Tuesday, November 16th
Where	The UW-Eau Claire’s Campus Mall, Eau Claire, Wisconsin
How	GPS Points collected using Topcon Hiper and Topcon Telsa, TotalStation, along with Verison MiFi unit to create a hotspot.

Discussion:
This assignment gave insight into two different ways of completing a topography survey. The dual frequency survey was a much faster way of surveying, at least for a beginner, because the set up required less time than the Total Station. The Total Station requires much more practice and time to set up, because you must have the equipment perfectly balanced. The environment also would influence the type of survey technique you would want to use, because it would be very difficult to balance the Total Station survey if you are surveying on top of sand, which is likely to move under the equipment.

When comparing Figure 2 and Figure 4, the Dual Frequency output appears to have displaying the DEM more accurately however we took 100 points for this survey, compare to only 25 points in the Total Station survey. If I were going to redo these surveys,  I would like to survey with the same amount of points with each technique, in the same size area. Figure 3 and 4 make it clear that there are gaps in the Total Station survey where additional survey points could have been taken. Knowing there were a few open areas in the data, I chose natural neighbor knowing it uses the closest input samples and applies weights to them based on their proportion of area. This interpolation also fit to boarder of my data points, unlike others such as spline that extended the interpolation to other areas without data points. Running these interpolations made it clear that it is necessary to have points equally displaced through out the survey area. Although my data isn't in depth enough to comment of the accuracy of each method, according to the College of Engineering at the University of Saskatchewan, the total station method is less accurate than the dual frequency methods. It is also important to keep in mind that the Total Station requires at least two people to complete the survey, where the dual frequency survey could be completed independently.

Conclusion:
During this lab, we practiced completing a dual frequency and Total Station topography survey. This gave an experience with setting up the configurations of the equipment before surveying, surveying with each techinique, and manipulating and interpreting the data in ArcMap, and comparing the outputs of the two survey techniques.

Sources:
page 256: http://www.engr.usask.ca/classes/CE/316/notes/CE%20316%20Ch%207%209-3-12.pdf
http://resources.esri.com/help/9.3/arcgisengine/java/gp_toolref/spatial_analyst_tools/how_natural_neighbor_works.htm

Field Activity #6: Navigating with a Map and Compass

Introduction
This week I practiced using a compass and map as a navigation technique. This lab is a follow up of the Field Activity 5. During this lab, our class was divided into group of 3. Each group chose a group member's map from field activity 5 to use to navigate with during this activity. Our professor began by giving us a set of points in which we had to locate. The methods and results for this are detailed below.

Methods
Once we arrived at the Priory, we all collected our individual maps. We were then instructed to plot 5 different latitude and longitude coordinates on our map. To plot these, I looked at the grid on my map. I used this latitude and longitude values measure where this approximately are located. The map we used contained a 50 meter UTM grid, which included 25 meter tick marks to help plot the points.

Next, we connected these points with a straight line using a ruler, as seen in figure 1. In order to navigate to each sequential point, we were required to measure the angle from due north in which we were to travel from the previous point. We did this by pointing the north arrow of the compass north as indicated by the map compass, and twisting the compass so that the angle degree was equal in line with straight line path, as seen in figure 2. This gave us 5 different angles.

Figure 1: This is a picture of me connecting the navigation points together, to create an intended navigation path to follow. 

Figure 2: This is a picture of Peter determining the angle we should orient from due north at each navigation point.

Next, using the maps scale bar, we had to measure of the distance between each point. This allowed us to determine how many step would need to be taken, by solving for X using the conversion ratio of how many steps our pacer takes over 100 meters.

We began navigating by locating our first point. This was along the trail, and was marked by orange tape, making it readily visible. Holding the compass up to our chest, we rotated it in the angle previously determined and marked on our map. The person holding the compass oriented the pacer in the direction indicated by the compass. The pacer then walked the amount of steps previously determined to get to the next point. Although we did not use a GPS to navigate, we had a GPS on hand gathering a track log of our navigation.

Figure 3: Track log data collected in the UW- Eau Claire Priory while navigating with a compass and map.

Metadata:

Who	Ally Hillstrom, Peter Sawell, David Leifer
What	Track Log
When	Collected on Monday, October 26th
Where	The UW-Eau Claire’s Priory. Eau Claire, Wisconsin
How	Track log data collected using juno GPS unit from the UW-Eau Claire Geography Department,  while using a compass and map technique to navigate.

Discussion
During this activity, we were forced to rely on maps to determine accurate measures with our compasses. We used Peter's map, which was helpful in many aspects. The map was useful in that it allowed us to first plot the latitude and longitude points mostly accurate because it contained a 50 UTM grid. This small grid interval made it easy to estimate where the point should be, however, this could have made more accurate if the grid contained tick marks in between the 50 meter intervals. Additionally, he decided to include a LiDAR Basement, which provided helpful elevation information. He also add a trail feature class, which helped us to locate the first point on our map because it was located on the trail. Navigating to each location was difficult because they were unmarked and required the pacer to walk straight through whatever terrain was in the path. Our pacer, Peter, was determined to stay on this track as much as possible however at times it proved this ideology is next to impossible. Along the track there were piles of disposed metals in the wood, as well as trees that required us to navigate around, which could count for the reason we were slightly off at each navigation site. We determined we readily found 4 of the 5 navigation points without the use of the GPS. The one were did not find as easily was unlabeled. This made it clear that if you are navigating, it is much easier to navigate there knowing what your target looks like. Without it, you must rely on the maps spatial cues. On our map, the I relied on the LiDAR elevation to know if the point would be in a high or low elevation within the area.

Conclusion
This lab highlighted what components of a map are helpful for navigating with solely a compass and a map. It became clear that this method of map and compass navigation has variable success, which is mostly dependent on the accuracy and quality of your map. The lab helped to identify which map components should be included when navigating with this method.

Field Activity #5: Development of a Field Navigation Map and Learning distance/bearing Navigation

Introduction
Many techniques have been developed by humans for navigation purposes. These techniques vary in complexity, ranging from requiring simple tools to advanced technology. For example, humans have used the stars, moon, and sun to navigate for hundreds of years. Now, most individuals rely on GPS technology to navigate to their point of interest, however in our previous lab, we discussed the unreliability of technology. Due to this, this weeks lab is designed to teach us how to navigate using a pace count, which does not require the use of advanced technology. During this activity we constructed two navigation maps that will be used during our next weeks exercise at The Priory, in Eau Claire, Wisconsin.

Methods
This week's lab consisted of two activities. For the first activity, our class went outside to determine our personal pace count for a 100 meter distance. I determined I walked 68 steps within the 100 meter distance. This information will be used in our next week's lab when we begin navigating at the Priory.

For the second activity, we were instructed to make two navigation; one with a 50 meter UTM grid, and another with geographic coordinates in decimal degrees. Aside from these requirements, we were encouraged to design the maps how we personally deemed best fit for pace count navigation purposes.

To design these maps, I began by making the UTM grid map. I beganby adding the background aerial imagery of the city of Eau Claire created by my professor Dr. Joseph Hupy. I then located the Priory in the image and fit this to the data frame. From here, I changed the coordinate system to NAD_1983_UTM_Zone_15N and then navigated to the properties of the data frame to add a UTM Grid measured at 50 meters. A grid is added to the data frame by entering the data frame properties window, followed by selecting the Grid tab (Figure 1). From here, I select New Grid. A Grid Wizard window appears, and walks you through the parameters of setting up the grid. I selected the Measured Grid grid type, which will divide your map into a grid of map units. Most default parameters were selected within this Wizard window, however I changed the Interval setting to 50 for both the X and Y axis. Because this map was given a UTM projection, the map units are in meters. Therefore, setting an interval of 50 created a grid with grid squares measure 50 meters for both their X and Y lengths.

I repeated this process to create the Geographic Coordinate System map, however I made a few adjustments. This time I added the same aerial imagery of the Priority however set the coordinate system to GCS_WGS_1984. From here, I entered the data frame properties and made another measured grid for this map. Because, this map is in an unprotected coordinate system, the grid is measured in decimal degree intervals of about 0.0006.

Figure 1: Displays the Grid tab of the Data Frame Properties window. The highlighted option button, "New Grid", is where to begin creating a grid for the data frame.

Along with the grid, I added contour lines to the map and labeled their elevation on both maps. Additionally, I included the final map requirements of a scale bar (in meters), a representative fraction ratio (in meters), compass, legend, title, and data source. The final products of these maps can be viewed in figures 2 and 3.




Figure 2: Aerial image of the Priory with a UTM 50 Meter grid overlaid.

Figure 3: Aerial image of the Priory with a geographic coordinate system grid measured in decimals degrees overlaid.

Discussion
While designing my maps, it was important to keep in mind what my maps will ultimately be used for. For our next week's navigation project, we will be divided into groups of three and given a designated path that is maps out by various navigation points. One team member will stand at a marked point, and another will start at this location and will keep their pace count until they reach the next marked point. Because this will be the first time myself and my teammates have walked this path, it is important to design a map that will inform us of the terrain is like within our path ahead. However, we discussed in class that often times it is a common misconception to add a lot of data to maps used for large scale field work. After learning this, I decided to keep my maps simple yet informative by adding only grid measurement and contour lines with elevation data. I added the contour lines in hopes that it will help inform the map reader of what the terrain elevation is like in places they are unable to physically see.

Furthermore, it is evident that the difference in coordinate system would effect one's ability to navigate. Knowing be measured our pace out in paces/meters, it seems to me it would be very difficult to convert this into paces/decimal degree. My assumption is that the UTM coordinate system is the best option to use for our navigating map due to this fact it has a map unit of meters. During next week's activity, we will be combining the use of these grid navigation maps with the pace count and compass navigation technique. In my field activity #6 report I will document the methods and analysis how well our methods worked while navigating at the Priory. Additionally I will review which features are more helpful to include in the navigation map.

Conclusion
This weeks activity was designed to introduce our class to the navigation techniques that requires the use of grid maps and pace counts. This lab is a two week activity, that began this week with the creation of our grid maps. In order to create functional maps, I was required to understand how UTM and geographic coordinate system grids are used for navigating. This gave me practice in designing a map for a specific field methods use.

 

Field Activity #4: Unmanned Aerial System Mission Planning

Introduction
This week I was exposed to the functioning of Unmanned Aerial Systems (UAS), and was introduced to the different UAV platforms. In the lab, I observed several models of UAV's owned by the UWEC Geography Department. Later, our class went to the field to practice handling the UAC. While on campus, we captured aerial images using the DJI Phantom. After the imagery was collected, I was introduced to three different types of software that can be used for UAS applications. My experiences and results from the lab are detailed below.

Methods

Flight Demonstration

On October 5th, my class went to the floodplain of the Chippewa River, located on the campus of University of Wisconsin - Eau Claire (UWEC). Here, we flew the DJI Phantom along the shore to collect georeferenced aerial imagery. The Phantom is set up to take images rapidly enough that each image will be 70% overlapped with the consecutive image. These qualities will allow us to create a DEM overlaid by a 3D image generated in Pix4D.

Software

1) Pix4D
As described above, Pix4D processes images to create a DEM and 3D high resolution image of the terrain. To generate this image, I was required to select a group of images from a common location. After choosing about 100 images, I imported these into Pix4D. This took about two hours for the program to process the images. Although Pix4D generated several outputs, for my purposes only two images were required. First, I located the Mosaic tif file within the DSM_Ortho folder. Once this was brought into my map document, I adjusted the base height to float on the DSM tif, giving the image elevation values. I also added a shading effect that gave all features a shadow relative to the scene's light position.   

Figure 1a: View 1 of the Pix4D orthomosaic output laid over the DSM output.

Figure 1b: View 2 of the Pix4D orthomosaic output laid over the DSM output.

Figure 1c: View 3 of the Pix4D orthomosaic output laid over the DSM output.

2) Mission Planner
Within the Mission Planner program, you are able to plan out your own automated flight missions at any location of interest. This allows you to alter a variety of parameters such UAV type, camera sensor, and altitude to compare how these would affect the data quality and the flight. For example these parameters may alter the following variables: the number of images take, the resolution of the images, the flight time, and the flight path. Comparing how the statistics are altered will help when making educated decision about which parameters are best to use for your study area.

While using Mission Planner, I concluded a few basic assumptions for UAV photography listed below:

1) The higher the altitude, the fewer flight paths. This is related to the camera's Field of View, or in other words the distance a camera captures widthwise within an image. The Field of View increases as the distance from the image's subject increases.
2) The higher the altitude, the shorter the flight time and fewer images required.
3) The higher the altitude, the higher the image resolution (meaning lower image quality).
4) Changing the drone type did not alter the output's variables.
5) The camera does not always have a significant effect on the output's variables.

The following images show how the flight plan and image variables differ from low altitude and high altitude flights.

Figure 2: This images displays an automatic flight plan of a 3DR_ Aero. The low altitude requires more flight paths, more images, and a longer flight time however provided a high image quality (lower resolution).

Figure 3: This images displays an automatic flight plan of a 3DR_ Aero. The high altitude requires less flight paths, less images, and a shorter flight time however provided a lower image quality (higher resolution).

3) Real Flight Simulator
While using the real flight simulator I flew several types of fixed wing and multirotor aircraft. This program allows you to select from a variety of different UAVs and environments. After testing out many of the options, I selected two UAVs to fly for at least a half an hour each. For the fixed wing aircraft, I chose a model called Pipercub, and for the multirotor I chose the X8 Quadcopter 1260. Below are my observations.

Multirotor: X8 Quadcopter 1260
I first flew the multirotor, which I soon learned flies extremely slow. Although it did not travel to a different location rapidly, the device was extremely easy to maneuver. Additionally, the multirotor allowed to be stall in place while in the air making the device stable.  On the other hand, I was able to orient myself in a new direction almost instantaneously. The flight for the X8 is about 15 minutes.

 

Fixed Wing: Piper Cub
Next I flew the fixed wing plane. This aircraft was difficult to take off in certain environments, as well as navigate once I was in the air. Although the plane flew much faster and therefore covered a much larger distance in a shorter amount of time, the device was difficult to maneuver. Additionally, there was no option to hoover in place, unlike the Quadcopter. As I continued to navigate the plane, it became easier to turn sharper but overall the fixed wing plane requires a large space to make a 90 degree turn unlike the multirotor. Lastly, the flight time for the Piper cub is about 2 hours.

After flying both platforms, it is clear they should each be used for different data collection applications. The fixed wings would be preferred for larger areas due to its longer flight time, fast speed, and requirement of large turn around area, whereas the multirotors would be best for smaller areas due to its shorter flight time, slow speed, and ability to orient rapidly.

Metadata

What	This is the metadata for the imagery taken with the UWEC Geography Department's DJI Phantom. These images were used to generate figures 1a-c from the program Pix4D.
Who	UWEC Fall 2015 Geography 336 course.
When	Images were taken on October 5th, 2015.
Where	The images used in figures 1a-c were taken of the floodplain of the Chippewa River, on the campus of the University of Wisconsin – Eau Claire.
How	These images were collected by flying the DJI Phantom manually, followed by processing them using the Pix4D program.

UAS Scenario
While working as a UAS consultant, many different scenarios will arise from interested clients from a variety of backgrounds. Listed below is a real life scenario that my professor Dr. Joseph Hupy encountered working as a consultant.

"An atmospheric chemist is looking to place an ozone monitor, and other meteorological instruments onboard a UAS. She wants to put this over Lake Michigan, and would like to have this platform up as long as possible, and out several miles if she can." I believe the most effective way to approach this situation is as followed:

Two key variables to analyze are the time and the distance the UAV is required to travel. The client requests that the monitors be exposed to the study area for a maximum amount of time, as well as travel for several miles. As previously stated, fixed wing aircraft express several characteristics that allow it them to perform most effectively in wide spread areas. According to an article by QuestUAV, fixed wings aircraft structures, in comparison to multirotor structure, are inherently more simple and aerodynamic ensuring longer times at higher speeds.

Another key variable to analyze is what equipment the UAV will be carrying. Here, the chemist states that the UAV must carry an ozone monitor and other meteorological instruments. This suggests that the UAV will carry a high weight load. Again, according the QuestUAV, fixed winged aircraft are able to carry a greater weight while using less power than multirotor. Although the weight of the instruments is unknown, it would be an important factor to investigate further if the weight exceeds the limits of a multirotor.

Ultimately, due to the long distance and maximum time required a fixed wing would be the most effective platform to survey the study area.

Discussion
In conclusion, UASes have an extremely useful role in mapping applications. The use of UASes in combination with aerial photography and GPS systems allows users to create extremely accurate terrain maps. This advancement in mapping technology saves geographers and potential clients both time and money. As noted during my methods, images can be collected in the field of a small plot of land in a matter of a few minutes, or of a larger several square miles of land within a few hours.  Additionally, there is a variety of software such as Pix4D that allows users to process these images rapidly, which can be used to investigate a variety of problems or questions about the land of interest.

The two platforms of UASes, fixed wing and multirotor, allow UAV specialists to use this equipment for a variety of applications. For example, in the UAS Senario in the methods section, we see UASes can be used for meteorological data collection. In addition to this, within my professors UAS consulting business he has come across other potential scenarios such as drainage modeling, identifying crop health, surveying wildlife habitats, bridge inspections, and identifying oil pipe line leakages. It is clear that UAS are a valuable advancement to mapping applications.

Conclusions
Although a UAS specialists must collect and process UAS data, geographers are not the only individuals to benefit from their usage. The combination of collecting aerial photography that is georeferenced makes UAS a vary useful field. The vast usages of UASes can assist a variety of individuals such as geographers, farmers, scientists, land owners, city planners, building inspectors, and military and governmental officials.

Sources

http://www.questuav.com/news/fixed-wing-versus-rotary-wing-for-uav-mapping-applications
http://www.directionsmag.com/entry/top-five-things-you-need-to-know-about-drones-and-gis/414810
http://www.esri.com/esri-news/arcuser/spring-2014/uav-and-gis-an-emerging-dynamic-duo

Field Activity #3: Conducting a Distance Azimuth Survey

Introduction

This week we learned how to conduct a survey without the use of expensive and often times unreliable survey equipment. Whatever the case may be, advanced technology is known to fail at times, causing you to resort to classic surveying methods. To prepare ourselves for these unfortunate situations, this week we learned how to survey using the distance azimuth method. Although less technologically advanced, when collecting implicit data, this can be used as a reliable alternative survey method.  During this lab we collected distance and azimuth values, imported the data table into ArcMap, and ran a series of tools to generate a final output map of our data. The methods and results of the project are listed below.

Methods

1) Choosing a Study Area:
After debating between a few locations, we chose the survey features within a portion of the University of Wisconsin-Eau Claire campus. We ultimately decided to survey here because the land is flat and the line of sight is wide open, making all objects readily visible. Once we headed to our field site, we located a tree south of Schofield Hall to be our point of origin. From this perspective, the most abundant and overt objects were trees and the concrete blocks on campus, so we chose to survey these features.

2) Data Collection Methods:
At the field site we had a TruPulse Laser, GPS, camera, and a computer. We began by using the GPS to collect the XY coordinate of this location, because the distance azimuth data we collect will be based off of this exact location. After scanning our view, we determined that sitting down would be the best way to eliminate errors from accidently moving while pivoting around our origin. We thought sitting may also help with measuring the concrete boxes because they are somewhat low to the ground.

Next Peter and I decided we would collect points on the trees and concrete blocks within our line of sight. Peter began sampling the concrete blocks from the east, slowly to the south, and eventually ended facing northwest. When sampling for trees, Peter began at the northeast most tree and continued sampling south until we reached 100 data points. At each object being sampled, Peter collected the azimuth and distance data. The azimuth represents the objects position in relation to the  distance measured in degree from a reference point, which this case is due north (Esri). The distance represents the distance between the object (end of our laser line) and our location (the origin) measured in meters. As Peter collected the measurements, he said the type of feature and its azimuth and distance values out loud and while I entered them into an excel file. After collecting 100 points, we decided to add an addition point to see how far the laser would be able to measure. Peter pointed the laser south at the tree farthest in the distance, which was located within a forested area within our campus call Putnam Park.




Figure 1: Peter stilling at the point of origin, whiling collecting the distance and azimuth data of a nearby tree using the TruPluse Laser.




Figure 2: View of UWEC campus to the east of our point of origin.




Figure 3: View of UWEC campus to the south of our point of origin.

 

Figure 4: Concrete blocks organized throughout campus. The blocks are laid out in an amphitheater design, which are meant to used as benches.

 

3) Organizing the Data into Excel:
Before importing the excel table into ArcMap, I made sure the table included the following fields; object ID (OID), distance, azimuth, feature type (type), X coordinate of origin, and Y coordinate of origin. Additionally I made sure that column with numbers were set to "number" field, with the appropriate number of decimals, and that the feature type field was set to "text" because it included words.

Figure 5: A portion of our excel data table.

4) Processing the data in ArcMap:
After the table was properly organized in Excel, I imported the excel table into ArcMap. The next step was to run the Bearing Distance to Line tool on the table. This was found in ArcToolbox, under the Data Management tab within the Features toolset. In order for the tool to run the distance azimuth function properly, I needed to specify which field in my table corresponded to each drop box field in the tool window. Additionally, I selected the spatial reference to be set as the WSG 1984 coordinate system. All of the settings I chose for the Bearing Distance To Line tool are show below in figure 6.

Figure 6: Setting chosen for the Bearing Distance to Line tool.

Once I ran the tool, it projected an output image (figure 7). This image has lines leading to each object we sampled. The lines could be considered an images of our laser shots to each object.

Figure 7: Output of Bearings Distance to Line tool.

Next, in order to add a point feature class to represent our survey locations, I ran another tool called Feature Vertices to Points. Once again, this was found in ArcToolbox, under the Data Management tab within the Features toolset. Once the tool is open I was required to select where feature's vertices should be transferred to along the feature. Knowing that our features are located at the end of each Bearings line, I transfer the points to the "end" of each line, or in other words at the features last vertices. The points are displayed below in figure 8.

Figure 8: Output of Feature Vertices to Points tool with the Bearings Distance
 to Line output. The points are a separate feature class than the Bearings lines.

My final steps to symbolize the data were then taken. Due to the fact the Bearings Distance to Line Output does not retain the "type" field, I was unable to symbolize the data unless I retrieved this data attribute. I did so by running at table join, using my Feature Vertices to Points feature class as the destination table, and my original table as the source table. I joined these together by using the OID fields as the common attribute. This allowed me to designate different symbols for the trees and concrete blocks. The last element I found necessary to include was our point our origin. I was able to import this point by displaying the XY data from our original table. The final symbolized maps are displayed below. Figure 9 displays the all the data, and Figure 10 is focused on the majority of our data points, excluding the southern outlier.
 

Figure 9: Final output of our distance azimuth data, displaying all data points. 

Figure 10: Final output of out distance azimuth data, zoomed in to the majority of our data in greater detail, while excluding the southern outliner data point that is visible in figure 9.   

Metadata

Who	Ally Hillstrom and Peter Sawell
What	Collection and presentation of distance azimuth data.
When	Data was collected on September 30th, 2015.
Where	Data was collected on the campus of University of Wisconsin-Eau Claire, in the campus mall directly south of Schneider Hall.
How	Data was collected using a TruPoint Laser. This collected the distance in meters the object was located for the area we were standing. This equipment also collected the azimuth degree of the object being surveyed.

 Discussion

Before beginning our independent data collect for our lab, our class practiced using the distance azimuth survey tools. Each lab group collected a handful of distance azimuth readings, and imported them into ArcMap. From here we ran the Bearings Distance to Line tool, and Feature Vertices to Point tool to generate a point feature class. Every group in the class projected points, however none of our points were in the correct location. Our points were about another 30 degree north of where they should have been. After checking settings such as the declination setting of the tools, our professor determined that the electro magnetic force within the area we were standing was significantly altering the accuracy of the point locations. Now that I have experienced the issue first hand I would be able to recognize the issue in the further.

When review the final data output (figure 9) it is clear the point locations are not entirely accurate. For example, there are trees symbolized on the roof of a build (Davie's Center) and in the parking lot. Additionally, looking closing at the concrete blocks points, in comparison to the blocks within the picture, you can see how most are shifted to the west of their real life location. One reason this could be off is that Peter was sitting down while taking points. Perhaps standing would have allowed for a larger target, and higher accuracy data values. This discrepancy could be due to the electro magnetic field within campus, or the improper declination setting. Seeing that all points are off by a similar value, I suspect there is a problem with the declination setting of true north. We did not bring a compass out with us, which would have potentially made our points more accurate. According to the WI State Cartographer's Office, Eau Claire has a declination of 1 degree, therefore this number should have been subtracted from the north reading on our compass. Another fault in our data could have been the accuracy of our origin coordinate. Unfortunately the GPS we used was highly inaccurate, forcing us to find our location in ArcMap. Although this most likely does account of all inaccuracy of our points, it very likely may play a role.

Although the points' locations are not precisely accurate, each point is within the general area of the object they are symbolizing in the image, and the distance between each symbolized point seems to accurately portray the real life distance between objects. The accuracy of this data is somewhat relative to what it would be used for. In our class, we are solely interested in collecting implicit data, and therefore are not concern with the precise XY coordinates of a point, but rather the general location of an object. For this reason, our output shows the distance azimuth survey method should not be used to collect explicit data, but implicit data instead.

Another issue that may be attributing to the accuracy was the troubleshooting we experienced obtaining our origin coordinate. Initially, we used a Juno to collect this XY coordinate, but once we entered it into ArcMap we discovered it was farther southwest then where we were standing. Using ArcMap we found the XY location of the tree we used as our point of origin. Using this new coordinate increased the accuracy of our final projected points, but as previously stated the accuracy is still not the best.

Lastly, to confirm, the southern most outlier point (seen in figure 9) had a distance of 346 meters. Our intent was to the tree furthest away in our line of sight. The accuracy of these point's distance is somewhat unknown but looking at it's location on figure 9, the point is indeed on the farthest edge of Putnam Park.

Conclusion

Through this activity I learned reliable geographic surveys can be completed without having to rely on advanced technology. I now understand that the distance azimuth method is a great back up to have in case our original method fails, as it uses simple distance azimuth values to locate features. Although my partner and I experienced issues associated with precise accuracy, the method appears to be useful when in need of relative locations of features. This lab demonstrates the importance of learning both new and older geographic data collection techniques.

Sources

http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=feature_vertices_to_points_(data_management)
http://support.esri.com/en/knowledgebase/GISDictionary/term/azimuth
http://www.sco.wisc.edu/mapping-topics/magnetic-declination.html