Collecting Data Using Arc Collector

Introduction

Technology these days allows us to find information quickly through the internet. This process is especially accessible since the boom in smart phones where the information is a few taps away. Not only can you find information quickly, some apps are now allowing you to collect data with GPS using a portable smart device. For this activity, ArcCollector was used to collect data around the University of Wisconsin-Eau Claire campus. I chose to collect data about cars in a popular parking lot on campus, possibly to analyze if the spaces are meeting the needs of professors, staff, students, and guests at UW-Eau Claire.

Study Area

• Date: 23 November 2015, 3-4pm
• Location: The Davies Student Center and Phillips Science Hall parking lot located on the south side of the UW-Eau Claire's campus. Phillips Science Hall is an academic building that shares a parking lot with the Davies Student Center. This area has many professors, staff, students, and guests coming in all day.
• Conditions: Cloudy and windy, 41°F (according to the Accuweather.com App)

Methods
Creating the Geodatabase for Data Collection

In order to use ArcCollector out in the field, first it was necessary to set up a geodatabase to hold and organize the data.

Figure 1. Database properties an example
showing how to enter domains

A new file geodatabase was made in ArcCatalog and in the properties we could edit the domains and other information regarding data collection. In the Domains tab, domain name, domain properties,  and coded values can all be specified.  When entering the domain name always be sure to enter a clear description about it. Another person collecting the data needs to know the criteria to ensure accuracy when the person goes into the field. In the geodatbase domains is where all of the data from each feature class will appear. For example, you could have more than one feature class using the same domain.

When creating a geodatabase to analyze bird sightings and tree types, the tree type domain would appear as a field in the bird sightings and trees feature class.

In the next section, domain properties, the domain and field type, maximum and minimum values for the field can be applied. For field types short integer, long integer, float, double, text, and date can be selected. Depending on what the data in that domain field will be (i.e. a color, number, decimal, etc.) determines the field type. Float is used if decimal values are being entered.

Domain type is either range or coded values. Range is simple, for that field you would enter a maximum and a minimum value the data could be. The program will only allow you to enter values within the range, eliminating error. As for coded values you would enter a code and then describe it. For example, you could enter maple, birch, ash, pine as coded values for tree type with a description such as any type of maple and so on. Again it is important to specifically describe the coded values in case multiple people will be working on the project.

Feature Class in the Geodatabase 

Figure 2. Feature class properties an example
showing how to create fields

The next step in preparing the geodatabase for ArcCollector is to add a new feature class.

In the Create New Feature Class menu, field type and data type are displayed. Enter field name without any spaces and select the data type (long integer, short integer, text, date, float, and double). All of the entries under field name will display in the feature class' attribute table. So, field name will be what  is entered under domain name. Also, the data type in the feature class properties has to match the domain type from the domains from the geodatabase properties.

For example, tree type was selected for a domain and as a feature class field name would be treetype and in both situations would be a text data type.

Within the field properties section of the feature class window you can choose to allow null values, select the domain, and length of the field. When selecting the domain this must match your field type, as stated above. Take bird species as an example, if you created a field for the attribute table called "birdtype" the domain would be set as bird type from when you entered the domains for the geodatabase.

After completing all of the fields and information necessary for the attribute table your feature class is ready to be added to the map.


Practice Creating Geodatabase and Feature Classes for data collection
 For this assignment we explored creating a geodatabase and feature classes for data collection as a class. We focused on trees and bird sightings on the UW-Eau Claire Campus. The domains included tree type, DBH (diameter at breast height), height, date, temperature, bird species, gender, and identifiable characteristics.

We created two feature classes to demonstrate that a domain can be displayed in more than one feature class. The trees feature class field names included treetype, DBH, height, date, and temperature and the birds feature class field names included birdspecies, gender, characteristics, date, and temperature. We selected these criteria to make our domains because we wanted to included different domain types within our feature classes.

Collecting UW-Eau Claire Campus Car Data
The next part of the assignment was to create our own geodatabase with the intent of collecting some sort of data on the UW-Eau Claire Campus. I choose to focus on cars in the Davies parking lot. As for domains I included car type, parking location, parking status, plate classification, date, time, and temperature.

Definitions of domain types
Car type: very basic description of the cars make (any type of Toyota, Mazda, ford, BMW, etc.)
Parking location: what parking classification was the stall in which the car was parked (S=student stall, F=faculty, G, visitor, or other)
Parking status: was the car parked correctly, it was not aligned in stall or in backwards
Plate Classification: MN, WI, MI, IA, IL or other

Next, in ArcMap I added a basemap and the cars feature class to the map. Following the directions from ESRI ( http://doc.arcgis.com/en/collector/) the cars map I created was published as a service and used ArcGIS Online to create a map to collect data. This site walks through each step from creating the geodatadase to collecting the data with ArcCollector.

Finally, I downloaded the ArcCollector App on my smartphone and loaded my cars map. In the field I stood in front of the car on the drivers side to be consistent with each entry. Forty-three points were gathered. ArcCollector prompted the whole data collection process. When adding a new point ArcCollector would automatically provide you with the options predetermined the domains of the geodatabase.

Metadata

Who: Morgan Freeburg 

What: Collecting Data using ArcCollector 

Where: UW-Eau Claire Davies Parking Lot

When: 23 November 2015

Why: To practice collecting data with a smart device and demonstrate ease of access to ArcCollector

Discussion

First I choose the Davies Student Center Parking Lot because it is accessed by different commuters all day whether it's students, professors, other staff, and even guests. The Davies parking lot has many different sections of parking as well, student parking, faculty parking, etc. This is why I selected this area to see if the parking lot is meeting the needs of all commuters and what can be improved.

There are many aspects of this lab I would retry if given the chance starting with creating the geodatabase. I would have liked to included more of a variety of domain types in order to vary my data a bit. Text domains were the dominant type within this geodatabase. Additionally, I did not realize you could not delete a domain field, so I had two temperature fields in my attribute table. 

As for type of car it was difficult to say with confidence that a certain car was a certain make (because I am not familiar with many). By default a lot of the data points fell under "any other type of car". The time field only allowed the date to be entered, and I already had a date field set up, so it was useless. Something more simple such as car color would have led to less confusion and more varied data.

Viewing the data points on the maps shows there are cars parked in the Phillips Hall yard. This obviously was not where they were located and a different base map should have been selected.

Collecting the data itself was simple and efficient, however, I was dissapointed in the absence a notes field while collecting data. When creating the geodatabase a notes domain was made for this reason. People were watching me go from car to car stopping at each one. One individual asked me if I was going to give her a ticket. It made me more cautious that people were skeptical, so I tried to work quickly. More points from different locations in the parking lot would have been more helpful than just a certain section of the parking lot.  

Figure 3. Map from ArcGIS Online, data collected using ArcCollector of the
cars of the Davies parking lot of the UW-Eau Claire Campus

Figure 4. A sample from the table displayed on ArcGIS Online, used in the field to collect data with ArcCollector

Conclusion

The use of ArcCollector is incredibly useful  in being able to access a dataset instantly, however, it was impossible to change the attribute table in the field. This could possibly give rise to frustration without anticipating certain fields essential for the study subject. If done correctly ArcCollector can streamline the process of collecting data and make it more efficient. 

Topographic Survey vs. Total Station Elevation Survey Techniques

Introduction

Gathering elevation data using GPS can be done in many ways. In this lab we explored the use of using topographic surveying with a dual-frequency GPS and surveying using a total station. The topographic survey requires a tripod, Topcon telsa field controller, topcon hiper SR positioning unit, and the MiFi. The total station survey requires the topcon telsa field controller, topcon hiper SR positioning unit, the MiFi, and the Prism pole. Both methods have advantages and disadvantages which we explored in this lab.


Study Area

• Date: 12 November 2015 and 23 November 2015
• University of Wisconsin-Eau Claire Campus Mall (area between Little Niagara that run through campus and Schofield Admissions Building)
• Conditions: Partly sunny with some wind, Average temp. 44°F (retrieved from weather underground)

Methods
Materials
Topcon Telsa Field Controller
Topcon Hiper SR Positioning Unit
Topcon Total Station
MiFi
Tripod stand (with level)
Prism Pole

Figure 1. Telsa Unit

In order to start collecting data, the Topcon Telsa unit needed to be set up. We created a new job and entered the necessary information. Note that we used the projected coordinate system UTM 15N and recorded our units in meters. Otherwise, our group used the default settings while creating a new job.

For our elevation survey we created a code "ELEV" so that the points taken were recorded under this code and given a unique number.

After the job was created, we connected the Telsa controller to the Hiper unit using a blutooth connection. With the Hiper attached to the top of the tripod unit and the Telsa attached as well we could start data collection.

During the second part of the lab the total station tripod and unit was used and connected to the telsa unit through a blutooth connection. To achieve the connection a MiFi portable wireless device was used just like in the topographic survey.

Figure 2. Total Station Unit

Data Collection 
On the Telsa unit there are two ways to collect the data (precise or quick). We chose to have the telsa take the average of 10 points using the quick option. Each time we collected a data point we needed to move and level the tripod to collect accurate points.

We took 100 points throughout the campus mall. Our strategy for taking the points was to zig-zag the mall area and avoid the large stones (as they would skew the elevation data).


The second part of the lab we used the total station to take elevation data by setting up an occupied point, and one back site. The purpose of the occupied site is that you only have to level the total station once to collect points versus with the topographic survey needing to move the whole unit 100 times.

Figure. Leveling the total station

Twenty-four points were gathered using the total station method. The back site and occupied point were first recorded using the topographic method. This is done because the total station sits atop of the occupied point and uses the back site as a reference in case you want to collect data beyond the scope of the original occupied point.

Figure 3. Troubleshooting the
blutooth of the total station

To set up the total station we first had to level the whole unit using the three round knobs on each side of the unit. Later, we used the laser to ensure we were right on top of the occupied point.

Collecting data with the total station, it is required to find the cross hairs from the prism pole exactly in the middle while looking through total station lens. From there you are able to collect the point data by selecting the save button on the telsa unit.




Analysis

Figure 4. Successful data collection!

We imported the data from the telsa unit to a notepad file on a flashdrive. From there we were able to display the X,Y data. We set our header line as follows, Lat (Y), Long (X), and Elev (Z) to avoid confusion when exporting the data as a feature class.

For both the topographic survey and total station survey the kriging, natural neighbor, spline, IDW, and TIN tools were run for all of the points. In the topographic survey data from the entire class was used to display the elevation of the UW-Eau Claire campus mall area. In the total station lab only points from our group were displayed.

Metadata

Who: Ally Hillstrom and Morgan Freeburg

What: Collecting elevation data by using dual frequency GPS

Where: UW-Eau Claire Campus Mall 

When: 12 November 2015

Why: To practice gathering elevation data with GPS  

Metadata

Who: Josie Markham, Drake Bortolameolli, and Morgan Freeburg

What: Collecting elevation data using total station equipment

Where: UW-Eau Claire Campus Mall
When: 23 November 2015

Why: To utilize another way of collecting elevation data

Discussion

The topographic survey was an easy method of collecting elevation data, however, compared to using a total station unit, the topo survey was quite slow. Below in figures 5 and 6 are the spline analysis layered over a topographic basemap of the UW-Eau Claire Mall. I chose spline to represent both datasets because after analyzing all of the tools spline displayed the topography of the study area the best. 

The data analysis is similar in accuracy between the two methods. The total station map would appear more segregated if there were more points to run the spline analysis with. Because the accuracy is comparable, the total station method seems much quicker and you could gather many more points in less time than the topographic survey.   

Getting to the end point of the maps was no walk in the park, especially with the total station lab. In both labs my groups struggled to work the telsa unit. We spent a lot of time troubleshooting. This goes to tell it is necessary to be prepared for complications in the field. Regarding the total station data collection we had to go out to the field three times in order to successfully collect the elevation data. Each time we went out we would get stuck on the next step. 

Our first challenge was the blutooth of the total station would not hold its signal. We had all the equipment set up and ready to go; the blutooth was even conected to the telsa unit. Once we tried shooting our back site the connection to the blutooth was lost. After that we spent fourty-five minutes turning the telsa and total station on and off (alternating the order) and trying to find our mistake in the settings. 

The next time we went out to the field we had all the equipment set out again, blutooth connection secure and the telsa turned off and would not respond. Finally, our third time in the field we were able to correctly arrange the equipment and the connections, but the telsa would not capture the point. Thankfully, it took less time to figure out the issue was focusing the crosshairs, and we were able to collect all of our points. Through our frustrations we became experts in setting up the total station over the occupied point in one try and leveling unit, but the telsa is the object that held us back. Unfortunately, technology fails sometimes.

Figure 5. Points collected from topographic survey and analyzed using the spline tool of ArcMap. 

Figure 6. Points collected from total station survey and analyzed using the spline tool of ArcMap.

Conclusion

Even though my group had our fair share of frustrations through out this activity it was incredibly useful to see the difference between the topo survey method and the total station survey method. Once again we learned technology is not perfect and may take a lot of troubleshooting, but in the end when it does work it makes collecting data much easier and efficient. The advantages of the topographic survey was it could be carried out with one person and does not require as much initial set up time. Again, I would favor the total station even though it requires at least two people and has greater set up parameters, but the ease of collection after is so much more efficient.

Priory Navigation Maps: Part Two

Introduction

As a continuation from last week's lab we used the maps we created to navigate a predetermined course given to us only using a compass. It is necessary to be able to calculate the the correct headings, and know your pace count before embarking on a course where you only have a compass and map to lead you.

Study Area
Date: 26 October 2015, 3-6pm
Location: Land plot surrounding Priory Hall, 1190 Priory Rd, Eau Claire, 54701

Conditions: Overcast, Average Temp. 53°F

Methods

Figure 1. Creating headings and pace
counts for the course at the Priory. 

First, we were assigned a course based on a UTM grid scale. We plotted the points on our UTM maps we made of the priory the week before and double checked with our group members to see if we plotted them correctly.

Firgue 2. Distances measured
between each point to
calculate pace counts. 

Next, we chose a starting point. Since the parking lot had been updated since the basemap was created we chose to start at the Northeast corner of the Priory's garage. Here we sat down and worked out all of the headings to each point, and roughly calculated the distance based on the pace count, we had determined earlier. To find the headings we lined up the orienting lines with our UTM grid of the map and made sure the north arrow matched north on the map (we made sure that the compass was set to north with 0 degrees). Lines were drawn to and from each point to clearly see the path and to find the degrees of each point.  After, we used the ruler on the compass to measure the distance between each point to calculate the approximate number of paces would be required to each course point.

With this information we were ready to start our course. Matt was our leap frogger, who was pointed in the right direction and moved towards the next point. Alyssa was the pace counter for Matt, and notified me when Matt got to the destination or landmark. I was the azimuth controller; I would ensure the heading was correct and point Matt in the right direction.

Figure 3. A tree marked with
neon tape to identify the
course points. 

The course points were marked with neon colored flags on trees. Once Matt found a course point, I would first make sure the north arrow was "red in the shed" and then changed to compass to the correct degree bearing. It was very important that the base plate was stable the entire time, that the direction-of-travel arrow was pointing directly ahead of the person. To minimize the error of these issues, the compass was kept at chest height and not tilted, or turned side to side. Only by rotating my full body was the compass pointing us in to the correct path.

Metadata

Who: Matt Brueske, Alyssa Krantz, and Morgan Freeburg

What: Navigation to course points using a map and compass

Where: Priory Land, Eau Claire, WI (1190 Priory Rd, Eau Claire, 54701)

When: 26 October 2015

Why: To use the traditional method of compass navigation to locate course points

Discussion

When it came down to navigating the course, we ran into a couple of problems. At the very beginning while we were trying to plot our points, we noticed our maps did not have many decimal places to go off of forcing us to somewhat estimate the course. Also, we did not have divisions between the major grid lines. Minor tick divisions would have saved us a lot of time and effort trying to calculate the values in our head. Plus, this would have made our course more accurate on our map.

Figure 4. Looking from the northeast side of the
Priory garage towards our first course point... 

Another issue we faced was our starting point. Although it did not interfere with the orienting to the first point it was more of a mental obstacle to start with. By selecting the northeast corner of the garage, we had to send the leap frogger right through a patch of shrubs and smaller trees. This could explain how we did not exactly find our first point. We saw a branch with a neon flag, but we later realized the first point we were supposed to find was a little more to the west. The distance and angle were close so that is why we figured it was our first point, when it was only marking a trail.

Figure 5. Course point two not marked with tape.

Navigating to our second point was extremely difficult. The course took us through a forest of knee height shrubs and tree branches trying to stop us. The downhill path was difficult to count paces through and to even take a normal pace because of all the debris in the way. We had thought we had made the approximate amount of paces but could not see the marker in sight. We searched for around five minutes, trying to troubleshoot and double checking the compass. We ran into the other group completing course five also looking for the second point. We looked compared notes and looked for another ten minutes together. Finally, we consulted the GPS we were given only to use if we were lost, and determined the flag must have been lost. We picked a tree that was closest to the what he GPS said was the correct point and used that to head off to point three.

Figure 6. Challenge of counting paces downhill
and through trees and shrubs.

After struggling with the first to points, the rest of the course was fairly easy to navigate. We were excited when the compass pointed us in the right direction, only varying by a few feet or when the pace count was dead on. The difficulties from the rest of the course were trying to explain a landmark to the leap frogger when the only feature around was trees. The "tree" at that point did not serve as an accurate landmark and got quite confusing when they all appeared to be the same height and species. Sometimes it was beneficial to point out a landmark farther in the distance because it was easier to identify, however, if there was a communication error the leap frogger was off course. If we selected close landmarks it was more tedious and it seemed as though more obstacles were in the way. The final issues we faced were trees or poky plants slightly altering our course of direction by trying to avoid them as well as going up and down hillsides altering the pace count. We did not account for this and should be taken into consideration in future courses.


Track Log

Below you can see the path we took through trying to find the points using our map and compass. In the area that is dense with points, we were lost trying to find course point two (which we later found out was unmarked). The rest of the course, however, went somewhat smoothly, especially navigating through the dense (dark green) wooded area.

Figure 7. The track log from the GPS unit we carried with us through navigating the course. 

Conclusion

In conclusion, navigating is reliant on many little nuances that if vary even a small amount can throw you off course. A great map, proper use of a compass, and pace count are all essential in the successful navigation of any course. This activity showed the importance of ALL these elements and forced us to trouble shoot when we became a bit lost.

Priory Naviagation Maps: Part One

Introduction

Navigation cannot be done without a well thought out, purposefully constructed map. Along with a proper map the coordinate system used to construct this essential tool must be carefully chosen to ensure the accuracy of what the map will be used for. If not careful the incorrect coordinate system may lead a navigator many meters off course and in the wrong direction.
In this activity we created maps of the Priory in Eau Claire,WI for the purpose of navigation to certain points in a later activity.

Study Site
Date: 21 October 2015, 3-6pm
Location: Map of the Priory, Eau Claire, WI (1190 Priory Rd, Eau Claire, 54701)
**Maps were made in the geography department of UW-Eau Claire


Methods

Two navigation maps of the Priory were created; one using a UTM style grid with 50 meter spacing and the other with Degrees Minutes and Seconds grid.

From data provided by Professor Joe Hupy I used the Eau Claire West SE raster to display the section of Eau Claire with the Priory in ArcMap. The Navigation Boundary feature class and 5 meter contour lines feature class was added to the map as well.

The Eau Claire West SE raster was set to 40% transparent by entering its properties, selecting the display tab, and then changing the transparency to the desired value. This allows better visibility of the features and grid displayed on the map.

The navigation maps include all essential map elements such as a north arrow, scale bar, RF (representative fraction) scale, legend of features, source, title, projection used, and a watermark. A RF scale was included in these navigation maps in order to provide more detail than the usual scale bar. The projection used for both navigation maps was NAD 1983 UTM Zone 15N Wisconsin Transverse Mercator.

Grids were constructed in order to make these maps useful in identifying the course points for the next activity.

To create a grid in ArcMap go into the Grids tabs of the data frame properties. Select New Grid. Measured grid was chosen for the UTM style grid, while the graticule grid was selected for the degrees minutes and seconds grid. Make sure to select Grid and Labels so the output is labeled in the desired units. The interval for the UTM grid was changed to every 50 meters (x and y axis). As for the decimal degree grid the interval was every two seconds for the x and y.

Metadata
Who: Morgan Freeburg
What: Navigation Maps of the Priory Land Plot (1190 Priory Rd, Eau Claire, 54701)

Where: University of Wisconsin-Eau Claire Geography Department
When: 21 October 2015
Why: To prepare for navigation relying on these maps and a compass

Discussion

Besides including the basic map elements (title, north arrow, scale bar, legend, and source) I decided to include a scale text reference to enable us to calculate the number of paces we need to reach each point. As for the grid itself I wanted to ensure there were divisions between the major grid lines in order to more accurate pin point where the course would be. Unfortunately, after creating the map I realized I did not create enough subdivisions and found it difficult to narrow down the location of the point. Having subdivisions will guide you to the correct location instead of the general area. Another aspect to consider would be how many decimals or significant figures to include in the making of the grid. Initially, I did not include enough significant figures for the UTM grid and ended up, once again, only arriving in the general area of the point.

Figure . Navigation map using the NAD 1983 UTM Zone 15N
Wisconsin Transverse Mercator coordinate system with a degrees,
minutes, seconds grid. 

Figure . Navigation map using the NAD 1983 UTM Zone 15N 
Wisconsin Transverse Mercator coordinate system with a UTM grid. 

Conclusion

Creating the grids for the UTM and degrees, minutes, seconds map was a great way to prepare for the navigation activity. Not only did the maps help us familiarize ourselves with the priory land it forced us to focus on what was needed to create a practical navigation map. Map elements must be a priority in creating a navigation map because if the elements are not considered and made correctly, you could find yourself very lost!

Activity Four: Unmanned Ariel Systems

Introduction
Unmanned Aerial Systems are the new technology with collecting images and have proved very useful across disciplines.  Different types of aircrafts exist and all have their advantages and disadvantages. The greatest difference is between the fixed-wing and multirotor systems.

Fixed-Wing Systems

Figure 1. Fixed-wing aircraft

Advantages: Stable in high winds, large field of view, flies faster than multirotor systems, battery life is longer

Disadvantages: need landing and take off space, flight checks are longer, bigger turn radius

Fixed-wing systems contain a pixhawk, which is the brain of the aircraft. The pixhawk sends information to the base station quickly. These systems also contain a GPS setting and a compass to navigate. Their lithium batteries are heavy, and should be kept in the fridge for storage. This system is cutting edge with its ability to collect ozone readings and when analyzed can create 3D models of ozone.

Figure 2. Phantom DJI

Multirotor Systems

Advantages: remote sensing, no distortion for small areas, more agile than fixed wing systems, needs less space for take off and landing

Disadvantages: slower, narrow field of view, shorter battery life

Multirotor systems come in many shapes and sizes. There are aircrafts with 4, 6, and even 8 multirotors. The propellers spin in opposite directions  controlling the speed of the aircraft. A Jems sensor that takes infrared readings can be added to the multirotor system. This would be especially useful in biological studies.


Study Area
Date: 12 October 2015, 3-6pm
Location: Sandy Bank of the Chippewa River Valley (underneath the footbridge of UW-Eau Claire's Campus)
Conditions: Clear skies, Average temp. 60°F (retrieved from Weather Underground)

Methods
Demonstration Flight and Pix4D

On Monday, October 12th our class went to the bank of the Chippewa River (underneath the UWEC footbridge) to manually run the DJI Phantom. This was used to demonstrate the possibilities of data collection with images. The DJI Phantom was fairly simple to operate as its operator had control of moving it forward, backward, and was able to turn the Phantom as well. A camera was secured to the DJI Phanom and that is how I was able to collect the aerial data over the bank of the river. The Phantom leans forward while it is moving, but our professor informed us that the camera dock stays parallel to the ground and automatically adjusts for the angle.

Figure 3. Field Methods Class on the Chippewa River Bank
experimenting with the Phantom DJI.

Dr. Hupy took many images around the bank of the river. We had two principal areas to focus in on, a "24" made in the bank with rocks, and the sandboxes from our previous assignment. We also were able to take photos of a bids nest in a tall tree about 10 meters from us. Dr. Hupy had an iPad set up to the camera, so we could see up close what was in the nest.

Pix4D was used to process the images from the flight demonstration we did as a class with the DJI Phantom. On the sand bank of the Chippewa River (underneath the UWEC footbridge) the rocks were organized in a "24" with a circle surrounding the number, which can be seen in the figures below.

To process the data we created a new project in the program and named it, "Flood Plain Data".  We had to specify the platform, site, and date (Flood Plain Phantom 10/15) and saved it within our personal student folders. We had to add at least three pages, and then we were ready to add pictures to Pix4D. The pictures were copied into our personal student folders as well. I chose to upload around 50 photos from the 24 pattern picture collection. Then we set the it to make a 3D-Map and adjust the GCP's to make it more accurate and let the program run. The data was processed in roughly two hours and exported right into my student folder. Pix4D generated a mosaic rater and a DSM. Both rasters were opened in ArcScene to display them in 3D. For the DSM raster, the hillshading effect was increased to 2.5 to show the micro-topography of the rock pattern.

Metadata

Who: Joe Hupy

What: Unmanned Aerial System 

Where: University of Wisconsin-Eau Claire Geography Department

When: 15 October 2015

Why: To familiarize ourselves with the UAS technology

Discussion

The DSM and mosaic rasters generated by the Pix4D very accurate. The technology of using a multirotor aircraft has incredible applications, whether it is scanning a river bank or peeking into a birds nest.

Figure 4. DSM from Pix4D of 24 rock pattern
of the Chippewa River Bank.Created with ArcScene, 
hillshade = 2.5. Higher areas are darker.

Figure 5. DSM from Pix4D of 24 rock pattern of the Chippewa River Bank. Created with ArcScene,
 hillshade = 2.5. Red is higher topography.

Figure 6. Mosaic from Pix4D of 24 rock pattern of the Chippewa River Bank. Created with ArcScene.

Mission Planner

We used Mission Planner to explore how UAS are managed and how the flight plans vary with different settings (aircraft, altitude, speed, etc.).

In Flight Plan we zoomed into a football field near the UW-Eau Claire Campus. We wanted to find an undisturbed area to practice setting up a UAS flight mission. Right-clicking and selecting the Survey Grid(2) will open a new window to allow you to regulate the flight settings. As shown below in Figures 7 and 8 the yellow lines represent the flight lines or the route of the aircraft.

Discussion

Increasing the speed of the flight plan decreases the flight time, so this is important to take into consideration when planning a mission. Also, some UAV's have a shorter flight time to begin with which emphasizes the flight mission needs to cater to the goals of the mission and what data you are trying to collect.

Altitude is an essential part of the flight plan as well. As the UAV increases in altitude less area  is covered in the flight plan and less images are taken. The more images taken the quality decreases.

Figure 7. A flight plan created using Mission Planner Software with a short flight time, few images, and a high altitude. 

Figure 8. A flight plan created using Mission Planner Software with a longer flight time, many images taken, and a low altitude.

Real Flight Simulator

The Real Flight Simulator enabled us to try working a UAV without and real damage done. (I may have crashed a few times). The Flight Simulator was a great tool in learning how to run different types of aircrafts (there are many to choose from) and the different scenes available helps challenge the pilot.

Multirotor Aircraft

First, I flew the Q4 Quadcopter 520. It took about 10 minutues before I successfully took off. Flying UAV's is all about patience and taking off smoothly. I chose the Sierra Nevada Cliff to start, so I did not have to worry about running into trees or other objects.

The Quadcopter was difficult to turn but very stable. If you let the controls go the aircraft would hover in one spot. In the simulation the Quadcopter seemed to be moving very slowly, this may be because of the certain aircraft. The right lever of the controller propelled the quadcopter forward and backward and the left lever rotated the aircraft 360 degrees in the same place.

Multirotor aircrafts are very stable, can hover and rotate 360 degrees which would allow many practical applications in the field collecting data.

Figure 9. H4 Quadcopter in the Sierra Nevada Cliff Simulation (Nose View).  Real Flight Launcher 7.5 

Figure 10. H4 Quadcopter in the Airport Junkyard (Chase View). Real Flight Launcher 7.5

Fixed-Wing Aircraft

Second, I selected the Piper Club a fixed-wing aircraft. This was easier to control, however, the fixed-wing aircrafts are less stable and no quick movements can be made. The right lever tilted the plane to turn left or right and also you could increase or decrease the throttle. The left lever turned the rutter left or right and helped the plane increase or decrease in altitude. The speed of the fixed-wing aircrafts seemed to be much faster than the nultirotors.

Since the fixed-wing aircrafts were easier for me to fly, I chose different landscapes and was able to navigate around obstacles.

Figure 11. Piper Club flying in the Bayou (Chase View).  Real Flight Launcher 7.5

Figure 12. Piper Club going through an obstacle (Chase View).  Real Flight Launcher 7.5

Discussion

A military testing range is having problems engaging in conducting its training exercises due to the presence of desert tortoises. They currently spend millions of dollars doing ground based surveys to find their burrows. They want to know if you, as the geographer can find a better solution with UAS.

Figure 13. This is an example of a
Jems sensor to measure infrared levels.

I would recommend using a multirotor system. Since the military is doing ground survey to locate the burrows, a multirotor system would be best because it could be stable flying only a meter or so off the ground. Another major benefit of using a multirotor aircraft would be with adding a Jems Sensor you could analyze the infrared data given from the burrows to locate them faster. Tortoises rely on warmer areas since they are exothermic (do not produce their own body heat). Finally, the cost of implementing a multirotor system should be less expensive than flying a fixed wing aircraft.


Things to take into consideration would be the flight time will be less than what it would be with a fixed wing aircraft and the field of view would be lower. Although, since the military was doing ground surveys it is probably not necessary to have a large field of view, just to quickly collect the data.  

Conclusion

Overall, this lab activity opened my eyes to what technology is available to use for data collection. The multiple interactive parts of this activity helped in the learning curve and gave me a taste of what is possible. When performing this lab I could not help thinking the biological applications UAV's can give scientists to explore habitats, etc. 

Activity Three: Distance Azimuth

Introduction

The distance azimuth survey technique is useful in trying to determine and pin-point the exact locations of certain features. This method can be applied to many fields and has numerous applications. The distance azimuth calculates the distance between two points and the angle from true north. This is helpful when you want to return to a specific location, a coordinate point may not be accurate enough.

The objective of this activity was to select a study site and feature to survey and obtain the distance and azimuth of each feature. Then it was required to import the data into ArcMap and display the study site with the distance azimuth lines and points to see how accurate we could be.

Study Area

Date: 1 October 2015, 11am-12pm
Location: Owen Park, Eau Claire, Wisconsin
Conditions: Sunny, Average temp. 65°F (retrieved from Weather Underground)

Methods

Materials
TruPlus 200 Rangefinder
GPS Tour App
Data Table

Figure 1. TruPlus 200 Rangefinder.

Figure 2. GPS Tour app. 

Figure 3. Set up for the data table used
in this activity. 

      




Collecting the Data
We selected a location in Owen Park where we could easily view one hundred trees. We chose trees as our feature because there are many in Owen Park and we knew we would be able to reach one hundred data points.

First, we took our GPS location by using the GPS Tour App. According to the app we were at E-91.500481 and N44.803508.

Figure 4. Study area Owen Park,
Eau Claire, WI. 

From there we were able to start entering data points into our handmade table. The laser distance finder was set to SD to take the distance and set to AZ to record the azimuth (angle) of the trees. Alyssa collected all of the points and I recorded all of the data to ensure there were not inconsistencies in the data. We wanted to make sure the data reader (Alyssa) was only pivoting in the same location, rather than moving over to take the points. This would create a problem with our data because those points would be from a different location than our origin.

When using the TruPlus 200, we kept all electronics and metal objects far away from the device in order to not skew where true north was.

Figure 5. Alyssa Krantz taking
points with the TruPlus 200.

Trees were chosen as the attribute information field type because we wanted to record one thing that was abundant in the Owen Park area. The 100 trees surveyed for this activity were the trees that could be seen from the point of origin.

Importing the Data into ArcMap
Create a new file geodatabase
Set this geodatabase as the default geodatabase by going into the Page and Printer Set Up from the File tab

Right-click on the geodatabase and select Import Table (single)
In the Table to Table menu:
Select the Table and sheet containing the data
Review that the Output table is set to the default geodatabase you created

Figure 6. Within the ArcToolbox Data
Managment Tools, Bearing
Distance to Line and Feature
Vertices to Points was used to
create the map. 

In the ArcToolbox select the Bearing distance to Line tool. This tool appears under Data Management  Tools and then under Features.
In the Bearing Distance to Line interface select the sheet from the imported data table
The output feature class should again be set to the default geodatabase.
The X-field refers to the longitude of the where the data was taken and the Y-field refers to the latitude. The distance field is as it implies the distance measurements of the data (taken from the laser) and the Bearing Field is the azimuth the laser calculated.

After clicking ok, ArcMap will draw lines from the point of origin (latitude and longitude) to each of the data points. To clearly see where the feature lies on the map it is necessary to use a different tool from ArcToolbox.

In ArcToolbox select Data Management Tools and Features again. This time choose the Feature Vertices to Points.
Within the Feature Vertices to Points window set the input feature to the sheet of the imported data table and the output feature class should be set to the default geodatabase.
As for point type, this should be set to END because for the distance azimuth activity it is only desired to show the end of the line, where the feature is.

Figure 7. Distance azimuth of trees in a section of Owen Park, Eau Claire, WI. Map made using ArcMap. 

Metadata

Who: Morgan Freeburg and Alyssa Krantz

What: Collecting Data using Distance Azimuth

Where: Owen Park, Eau Claire, Wisconsin

When: 1 October 2915

Why: To collect data by distance azimuth 

Discussion

The results from our survey were somewhat accurate. On the map it appears some of the trees appear to be on the sidewalk or edge of the road. Obviously, this was not the case. The error could lie in the lack of precision of the GPS device used to record our point of origin. Another source of error could have been the perception of the trees through the laser distance finder. Alyssa mentioned it was difficult to see some of the farther trees and get a steady reading from the device. This could be because a tree may have overlapped another somewhat blocking the view of another behind it or the fact that it was too far away in the first place. On the other hand maybe if we selected a different basemap with more detail, the points would lie more accurately on the map.

Some problems we encountered specifically using ArcMap was the data table was incorrect and only displayed half of our points. Originally, a handmade table was used and then the data was entered into an excel spreadsheet. If the data had been originally entered into an excel these problems could have been easily avoided and personally saved me two hours of time trying to fix the errors. There were only two values entered incorrectly, but ArcMap would not import the table for unknown reasons. Finally, with a BRAND new data table, ArcMap allowed the importation.

Another item that we would change for the future is how to obtain the point of origin. As said above we used a GPS point. Our origin would not be easily identifiable from a google earth image or bing image because of the tree cover and an arbitrary start point. It would have been better to place our origin at the corner of a street or near a landmark of some sort. The downfall for our specific experiement to that alternative would have been would couldn't have seen as many trees.

The laser distance finder was a great tool to use in order to analyze the distance and azimuth of the features, which can be applied to just about anything. It streamlines the process of using a compass and measuring tape taking hours and hours to get a handful of points. To obtain the distance azimuth data for 100 trees it took only one hour. This method of obtaining data is very efficient. On the other hand, some disadvantages we ran into was if a certain feature was blocking another feature behind it. That would prove important if an individual needed to record the location of a feature away from a certain point. The feature would be missed altogether. Also, this method heavily relies on the accuracy of how you obtain your point of origin. By using a GPS the location could be somewhat unreliable, or if a map was used to pin-point the location to the origin a specific map of the study area would be needed.

Conclusion 
In conclusion, this method of surveying a large area in a short amount of time was very effective. There were some inconsistencies of points being in the incorrect places, however this could have been due to the fact of the GPS app, human error of moving while taking the measurements, etc. Otherwise, displaying the data in ArcMap went smoothly after the data table was corrected. It seems that conducting a distance azimuth survey is very user friendly (with the equipment we used), effective, and efficient.

References
http://www.forestry-suppliers.com/product_pages/Products.asp?mi=38721
https://itunes.apple.com/eg/app/gps-tour/id492684276?mt=8
http://www.mc.edu/rotc/files/6413/1471/7292/MSL_201_L03b_Land_Navigation.pdf

Activity Two: Visualizing and Refining Terrain Survey

Introduction

Often in trying to analyze a digital elevation surface multiple methods need to be considered and utilized to come up with an accurate visualization of what exists. As a continuation of last week's grid based survey of our sandbox we revised our methods to include more selective points to better represent the valley area. After analyzing the digital elevation models from our first set of data, we realized there were not enough points to properly show the narrow valley included in our sandbox.

After the revision of the data surveying collection, different interpolation models were used to analyze the accuracy of the features within the sanbox. ArcMap and ArcScene were used to achieve this task.  

Study Area

• Date: 22 September 2015 from 3-5pm 
• Sandy bank of the Chippewea River Valley 
• Conditions: Sunny, Average temp. 65°F (retrieved from Weather Underground)

Methods

Figure 1. Original 8 x 8cm coordinate
       system, 18th Sept. 2015.

Revising the Coordinate System

Figure 1 shows the original coordinate system created 18 Sept. 2015 with the cell size of 8 x 8cm. 

The valley was too narrow to be represented by an 8 by 8cm coordinate system. 

Only x-values from Y12 to Y15 were re-recorded, because we thought the digital elevation models produced an accurate representation of our other features. 

Figure 2. Revised coordinate system 4 x 4cm from Y12 to Y15.

From Y12 to Y15 the coordinate grid was adjusted to include points every 4 by 4cm. 

Push pins were placed every four centimeters around the edge of the rectangle, and then string was wrapped around it to produce the 4 x 4cm squares. 

This area was selected because the narrow nature of the valley. The other features within the sandbox were large enough to be recognized in the original depictions of the digital elevation models. 

Figure 3. Casey Aumann recording the z-values using
the 4 x 4cm coordinate system. 

Data Collection

Again, a meter stick was used to measure the depth of the features below sea level (from the top edge of the rectangle). 

The meter stick was lowered until it was touching the sand and hovered there until a measurement could be read. The meter stick should not have gone below the surface of the sand. 







Digital Elevation Models 

We used the Natural Neighbor, Spline, IDW, and Kriging interpolation methods to analyze how well our coordinate system represented the features in the sandbox. Side by side comparisons are shown below to view the improvement of using the 4 x 4cm grid system through out the valley area. 

Natural Neighbor ( "area stealing" interpolation) 

The Natural Neighbor method places value on the cell depending on what values lie inside of it. 

Figure 4. Natural Neighbor model with
4 x 4cm coordinate system. Made in ArcScene.

Figure 5. Natural Neighbor model withoriginal 8 x 8cm 
coordinate system. Made in ArcScene.

Spline

The appearance of the spline method is smooth because it takes into account the overall surface curvature and tries to minimize it. The points go directly through the input values. 

Figure 6. Spline model from revised data
with 4 x 4cm coordinate system. Made in ArcScene.

Figure 7. Spline model from original data with
8 x 8cm coordinate system. Made in ArcScene.

IDW (Inverse Distance Weighted)

This method values the center point of the cell, making the peripheral values of the cell less important overall. 

Figure 8. IDW model from revised
data with 4 x 4cm coordinate system. Made in ArcScene.

Figure 9. IDW model original 8 x 8cm
coordinate system. Made in ArcScene.

Kriging 

Kriging places high value on the z-values of the cells. It can take many z-values and predict the surface. 

Figure 10. Kriging model 4 x 4cm coordinate
system. Made in ArcScene.

Figure 11. Kriging model 8 x 8xm coordinate
system. Made in ArcScene.

Metadata
Who: Casey Aumann, Ally Hillstrom, Morgan Freeburg
What: Grid based surveying of a sandbox with different features
When: 22 September 2015
Where: The bank of the Chippewa River underneath the UWEC footbridge
Why: To revise the technique of a grid based surveying technique


Discussion

The spline and natural neighbor interpolation methods best represented our elevation surface. The difference from the 8 x 8cm grid system to the improved 4 x 4cm grid within the valley area is clearly visible. The valley has distinct boundaries, especially with the spline. With the  Kriging method , the valley still is much improved and visible however the banks of the river are somewhat blurry and it seems the features run together. IDW does not appear as accurate for our sandbox surface either because of the pixelated nature of how it is display. The IDW is giving the surface the appearance that the features are choppy and disconnected. This is where the spline method comes in between a happy medium of the IDW and kriging. The features run smoothly together, but not so much that they are not distinct. 

The second time around as far as collecting data was simpler in the sense we knew what we needed to improve on and how to go about getting the materials and setting up. On the other hand, it did not take us any less time to collect the data.


Conclusion

We were more confident in our abilities to create and execute grid based surveying system. We did not realize we could have selectively chosen the valley area to include more points in the first trail, we thought this would inflict some bias. Looking at the surface and features that were created it only made sense to subjectively select this area for more surveying points.For this extension we challenged our critical thinking skills and were able to overcome the obstacles we had in the first activity. 

References
ESRI. (2015). Comparing interpolation methods. ArcGIS Software (Version 10.3) [Software]