Lab 9: Arc Collector

Introduction

Today, just about anything can be used as a GPS and likewise your smart phone now has the capability to do just about anything an expensive GPS unit is capable of doing. In this lab we are going to be looking at an alternative method to capturing field data. This method will involve using the basic item that almost everyone has access to, a basic smart phone. Using the mobile app Collector which is an app produced by Arc Collector for both iOS and Android Devices. The app allows you to record data points and enter feature data into your map if it is accessible on ArcGIS Online. In this lab I will go over how a simple geodatabase created in ArcCatalog can be accessed on a basic smart phone equipped with the Collector app.

Methods

Creating a geodatabase in ArcCatalog is done as simply as right clicking in your desired folder and selecting New and then selecting File Geodatabase. From there, right click on the newly created geodatabase and create a new feature class. This project will involve creating two feature classes. One will be for on-campus data collecting and the other for off campus collecting. For this methods section I will be dealing with the on-campus collection feature class and in the results and discussion section I will be working with the off campus data collection.

Once the new feature class is created, I called mine Cars, it will need domains. These domains which will soon be created by right clicking and going to properties, are the meat to the data. The domains will be all the fields for each data point that will get filled in. For my cars data I created six different domains. Domains can also have different field types such as short or long integer, float, double, text and date. And then the each domain can also have a different domain type, either coded values or range.

Domain Name	Field Type	Domain Type
Color	Text	Coded Values
Date	Date	Range
Parked	Text	Coded Values
Seats	Long Integer	Range
Vehicle Type	Text	Coded Values
Campus/non Vehicles	Text	Coded Values

A text field means the information you are going to be putting into the feature class will be a non-numerical entry. So for Color, Parked, Vehicle Type, Campus/non Vehicles all of the information entered was a text file. For example; red, parking lot, SUV, or non-campus vehicle would be a few of the entries found in the respected categories.

The other two categories used, Date and seats use different field types and domain types. The date field type just records the information from the date the data was taken and stores it that way. The long integer for the seats allows you to enter a numeric value, so number of seats. Both categories use range as the domain type. This was used to avoid entering bad data (mistakes). I knew the dates I was going to be collecting data, so I put the range of about a week in that category, and for number of car seats, I knew cars would not have less than 1 seat and no more than 10 seats, so I used the range 1-10 for that domain.

Once all the fields were correctly filled in the “map” had to be published so it could be accessed on ArcGIS Online, and furthermore Collector. Basically I am publishing a blank screen to ArcGIS since I had no data entered yet. ArcGIS has a great tutorial that goes over each step of how to do this and included great visuals of how to do it. Since this blog is not meant to be a step by step guide the link to achieving this is below.

http://doc.arcgis.com/en/collector/

Once the data has officially been published, it will be able to be access on ArcGIS Online. There you can add base layers to the map, and once you hit the save button it will be accessible on the Collector Mobile App on a cellular device.

Now that the map is accessible on a cell phone I am able to go outside and collect data. Opening the app and the desired project, for me it would be the cars map. Once I got to a car I would hit the “+” button on the top and it would bring me to a new page with each of my previously created domains. Depending on what attributes the car had would determine which choices I would select. I took data from three different parking lots (Hibbard, Schneider and HSS lots) due to the cold and my poor battery life I could only collect about 5-10 cars at a time before my phone would shut down (Figure 1). A total of 4 trips were done to collect car data and would soon be imported into ArcGIS (Figure 2).

Figure 1: A screen shot of the Collector App on an iOS device

Figure 2: The on campus display of what color cars are parked in UWEC parking lots

Results

For my off campus data collection I decided to use street lights. I only used 3 domain types all of which were text field types.

Domain Name	Field Type	Domain Type
Street Light Color	Text	Coded Values
Street Light Composition	Text	Coded Values
On/Off	Text	Coded Values

My study area was focusing on the lights around the student ghetto streets to see how spatially separated they were from each other. I did not plot the lights on Water Street because that is considered to be a part of a central business district (CBD) and therefore did not seem to fit my data area of student housing. So going from the alley ways between Water Street and Chippewa Street all the way north to Lake Street. And Started on First Street and went as far west as 5^th

.

Data collection was done once again by walking to the individual lights and recording the points and attributes on the app screen. The lights are plotted on the map below (figure 3). Each roadway and alleyway was walked through, including Randall Park, which made for lots of exercise.

Figure 3: Location of White and Amber Lights in Student Housing Area

Discussion

Collecting data for this project shows that the street lights are clustered in some areas such as 1st street and Lake Street. The reasoning behind that is because they are heavily trafficked roads. Roads like Chippewa and Broadway do not experience the kind of traffic the previously mentioned streets have. There are also plenty of street lights in between Chippewa and Water Street. This is probably because that alley way is more or less a business alleyway. It has to deal with lots of trash cans and parking for the local businesses on Water Street therefore making it an area that needs to be properly lit. Lastly, I found it a little interesting how few lights there were in the alleyways between the streets. Most houses in the study area have parking in that alleyway so you would think it would have to be adequately lit to avoid any potential accidents.

In total 105 light locations were recorded using Collector. It would be interesting to digitize all of the buildings in the study area and see which areas the light does not reach, which could make it a dangerous area to be at night.

Conclusion

Overall using Collector is a great tool to use if you have cell service and cannot afford to buy a fancy GPS that can store this data for you. With today’s technology, phone GPSs are very accurate, looking at the maps of where the lights were, they are very close to the actual location. This method just proves that technology is advancing so much, that projects done in the past with specific technology can now be done by simply touching a screen on something that most people carry around in their pockets. 

Lab 7/8 Topographic Survey

Introduction

The goal of this lab is to use basic topographic surveying equipment and methods to conduct a study on an area in the University of Wisconsin Campus Mall, between the Davies Student Center and Schofield Administrative Building. This lab was broken up into two major sections; the first section used the Hiper device to collect GPS field data. This section was done in a group of two where one person used the Tesla to record the points and the other person held the Hiper at the location of where the GPS point was being taken. The second part of this lab was done using the Topcon total Station and a reflective prism. This section was done using three people where one person looked/lined up the Topcon with the second person who was holding the reflective prism and the third person was working with the Tesla recording the points. Both of these exercises will teach us the basics of how to use Field Surveying equipment and also help us work with understanding different methods of collecting and exporting data.

Methods for Section 1

On the first day of working with the equipment, Dr. Joseph Hupy of the Geography and Anthropology Department at the University of Wisconsin Eau Claire taught the class how to use the equipment and delivered some tips which would prove effective once we took the equipment into the field. For this project we were going to have to gather 100 data points in the campus courtyard. This was done using four separate folders due to technical issues. In order to learn the proper way how to use the equipment which included using the Tesla and Hiper, Professor Hupy told us all the basics of how to set everything up and then instructed group 1 on how to set up all the equipment. In turn that group would then instruct the next group and so on until every group was familiar with how to use the Tesla and Hiper (image 1 and 2).

Image 1: Tesla unit used during our data collection

Image 2: Similar Hiper and Tesla unit we used during our field data collection used at an apparent construction site

The steps for this were;

                1.) On the main screen of the Tesla select the Magnet Field App

                2.) Create a new job, which is accessible from the Magnet Field Home Screen (select job)

                3.) Fill in all the information for the job. We used generic class and group names to make it                      easier for everyone to know which files were being used by which group.

                4.) Still in the new job information, several additional questions will pop up including                              Coordinate System questions and Configuration setup questions.

                5.) Using the Bluetooth capabilities, fueled by the Verizon Wireless Mi-Fi hot spot                                    (image 3), connect the Hiper to the Tesla unit.

                6.) Now we are ready for data collection. We used the code name ELEV for elevation and                        used a specific group of points so we do not experience any overlap from other groups                        once all the points are merged together.

                7.) Make sure data collection is on Fixed only. We used an average of 10 points per data                          point collection

                8.) Once all data is collected and saved, you may disconnect all the equipment and return to                       the Magnet Field home screen to export the data

Image 3: The Verizon Wireless Hot Spot, a similar model was used in the field while we were outside collecting data

While in the field collecting data one person worked the Tesla Unit while the other person made sure the Hiper, seated atop a 2 meter stand, was perfectly level while the data was being collected. Casually going through a section of the Campus Mall we collected a total of 100 data points. By hitting the save button on the Tesla Unit, it would take 10 points from the one spot and then average the points out and save it as one point. Once concluded, we exported the four files onto a jump drive. The txt file exported contained information on the northing and easting direction as well as the elevation from the point. This txt file can be imported into ArcMap and turned into a map (figure 4).

Image 4: The final product of the Hiper/Tesla data collection 

Discussion for Section 1

When looking at the map created by taking our data points we see as we move from the right side toward the left the elevation is decreasing. It is only about a 2 meter decrease but in our small study area it is seen as a large change in elevation based on the color scheme. Also as we move from the top right of the map to the bottom left we see the elevation continuing to decrease. Overall, it is clear that the campus mall is sloped downward toward the Little Niagara Creek. When looking at an aerial image of the area one would think the center circle of the amphitheater would be the lowest part, but in fact it is the area just to the east of the circle which is the lowest according to our data.

Conclusion for Section 1

In conclusion I would say that this was a fairly quick method, once all methods have been learned, to collect data. It is accurate and easy to learn. Since our study area was not done using a basic GPS tool it is difficult to say which was better at collecting data. As for the campus mall, although not a study question for this lab and something that came to a surprise to me, the circle at the center of the amphitheater is not the lowest point on Lower Campus mall, not including the land by the creek which can easily be seen as lower.

Methods for Section 2

This part of the project as mentioned in the introduction was done using three person groups. Again we spent the first part of class learning about how to use the proper equipment. To start off our learning experience we talked about what all was going to be done in the class room and learned some basics about how to use the surveying equipment and some basic terminology. Using the same study area as section 1 we had to gather 25 data points. These 25 points also include 1 backsight point and 1 occupation point.

                Occupation Point: It is the point of where the Topcon Total Station will be used

                Backsight Point: It is a point taken back towards a point of known elevation, it is used to                calculate the height of the surveying instrument.

The backsight and occupation points had to be collected using the Hiper and Tesla devices. Using the same methods as used in section 1 to collect points, the two points were collected and labeled correctly so we would know when processing the data which points were which. After the data was collected we could begin working with the total station. The first part we had to do was set up the tripod for the total station to be set up on. It was comprised of three legs with large stakes at the end of the legs to assure it would be able to stay firmly on the soft ground. Once the tripod was set up the total station could be screwed into the stand using a metal connector which screwed into the bottom of the piece of equipment. Once the equipment was at the desired height a laser was shot out the bottom to show the exact point it was above. Because we set our occupation point to a specific spot, we had to assure the laser was directly above that. Once at the correct height, we had to measure to make sure we knew the precise height of the total station above the ground. We used the elevation of 140 cm or 1.4 meters above the ground (figure 5).

Figure 5: The total Station set up and ready to be used for data collection

The next step was to make sure the equipment was completely level to assure the most accurate data collection. This was done by moving the legs up and down, one at a time, and using knobs beneath the total station. Once level we could begin collecting data. In order to collect data, the total station shoots out a laser toward the reflective prism, which is at an elevation of 2 meters.

Before shooting data points, all the equipment had to be connected via Bluetooth using the Verizon Wireless Mi-fi. This was done similar to the section 1 connection. This section proved to be the most difficult part of the exercise, will address issues in the evaluation of lab 7/8 in the assessments tab.

In order to collect the data points, one person would line up the total station view finder with the prism at a different location. Once lined up, the person working the Tesla Unit will capture the data by hitting the save file button on the Tesla, this would be done 23 times, in order to collect a total of 25 points. While working we experienced so many unable to capture data points and data points captured that we wouldn’t even have to talk instead we would just listen to the beeping sound the total station would make. A triple beep became our best friend in the frigid weather. Triple beep indicated a successful data capture.

Once all points were captured, we exported the file as a txt file, similar to how done in section one and would be used to create a 3D and a 2D view of the data.

Discussion for Section 2

The data once again put into a txt file was imported into ArcMap to create a visualization of the study area. When looking at the map created we see the elevation decreases as we move from the right side of the map to the left side. We also see as we move from the top of the map to the bottom of the map a decrease in elevation. The area around the Little Niagara Creek as expected is the lowest area in the study area, and the circle which looked like it was not the lowest point in section one, is confirmed not to be the lowest point on the map. The Occupation point was marked using a Green Flag to represent the starting point, and a checkered flag was used to mark the backsight point, not because it was the final point, but just to fit the theme of flags being used to mark the points (image 6).

Figure 6: The totalstation data captured in the field portrayed in a 2D map using ArcScene

A three dimensional map was also created, using the kriging method of data interpolation in ArcMap, a raster file was created and imported into ArcScene. In order to better see the change in elevation, I changed the exaggeration to 6.0. The image shows the higher elevation is away from the creek and as we get closer the elevation gets lower. There is only about a 3 meter difference between the highest point on the map and the lowest (image 7).

Figure 7: The 3D view of the total Station data captured, using ArcWorld to display data

Conclusion for Section 2

In conclusion I would say that this was a difficult method to learn and get used to, but once we learned how to do everything we became very quick at capturing data points. This method again if we could compare it to other methods in the same study area I could have an opinion on the accuracy, but since we did not do this I cannot say which is more accurate, but can assume this way is much more. Overall, this was a great method to be able to add to our collection of surveying techniques and like the other survey methods conducted, this one serves a particular purpose and is best suit for certain fields while other methods have strengths in different areas. 

Lab 6: Navigation with Map and Compass

Introduction

Continuing off of the work we did in the previous lab exercise (lab 5) the class met at The Priory, which has a University of Wisconsin- Eau Claire hall and a large forest section with several trails running through the woods. Within the woods there were several marked features which had to be located using the tools provided. These tools included a compass, a map we created with a coordinate grid overlaid on the map and a list of the point coordinates so we could located them on our maps.

Area of Interest

As mentioned earlier, the area of interest where this lab was going to be conducted was in the Priory.
Located about 3.5 miles south of the University of Wisconsin-Eau Claire's Phillips Hall (where class normally takes place) the Priory is home to the large wood section right next to Interstate 94.

Image 1: Priory study area located in the red box toward the bottom of the map

Methods

Meeting in the Priory parking lot, we were given our maps which we put together the previous week and with our group, also the same from the previous week, we were to locate the five points on the map. The points we had to find were;

1: 617708.81339, 4958257.83960

2: 617930.69249, 4957346.94679

3: 617619.79970, 4958049.24309

4: 617835.30499, 4958136.93679

5: 617695.53000, 4958123.65040

All points listed above our in a UTM projection, where they are measured as distance from the origin. With this data, and the the map, we individually located the five points on our map and then compared them with the rest of the group to assure we plotted them correctly. After locating the points we were given a compass with a rotating dial which could be offset to adjust to magnetic north. Magnetic North differs from true north, luckily for Eau Claire, Wisconsin that difference is minimal. The compass had a ruler on both sides, on in inches, the other centimeters, this was used to measure the distance between the two points. Once the distance was found, we could determine how far the two points were, along with the bearing by using the rotating dial of the compass and an eTrex GPS, just in case we got lost or our point was not marked. With the distance and bearing figured out, we could calculate how many paces away they points were. Our designated pace counter had a 100 yard pace count of 63. Using a simple equation (meters x .63) we could figure out how many paces away the points were from each other.

Image 2: The compass used to track our bearing as well as measure the distance in between the 

Image 3: Plotting and measuring the points we had to find at the Priory

Now that we accuracy plotted all the points, we were ready to go locate them. Using our designated starting location, we found our bearing we had to travel, and had our pace count which we had to go. 

Every member had a role in which we were suppose to follow. One person was in charge of the compass, and making sure we were heading in the right direction. Person two was in charge of keeping the pace count. Person three was in charge of making sure we were traveling in a straight line based on the compass person's direction.

On paper this seemed pretty simple, but once we got into the woods we quickly learned it was not going to be as easy as it sounded. Our paths were blocked by dense shrubbery, meaning we would have to find an alternate way around, once we were off of our designated path it became difficult to try and figure out which way we then had to go. The steep hills and down trees also made it difficult to keep an accurate pace count. 

After about 30 minutes we found our first point, located between two large slopes. From that point we looked at our map to find our second point, only to realize there was an impossible object to try and walk a straight line through, that would be a large building. Luckily we found a new place where we could start from on our map which was on the other side of the building, we calculated our bearings and pace count and took off looking for it. Eventually, about 30 minutes after we found our first point, we found our second point. The second point was not marked at all, and the only way we knew it was the correct point was because we had a GPS on us which confirmed our location to be correct. 

Image 4: A similar GPS we used to locate our actual position to figure if we were close or not to our point.

The last point we located, point 3, took us about 40 minutes to find, It was over 350 meters away from point 2, and again the Priory was in the way so we had to recalculate our bearing once we got to the other side of the building. Once calculated we took off looking for it, on our path there we found 3 marked trees, none of which were the correct trees (other teams points), and after using the GPS to confirm our location we found that point 3 was not marked either. 

Image 5: One of the points we had to find, the GPS confirmed the location, the tree was unmarked

Due to the shortness of daylight in Wisconsin this time of year, we ran out of usable light at about 5:15pm meaning we could not attempt to locate points 4 or 5. Those points were about 130 meters and 180 meters away from the previous points, respectfully. 

Of the three points located, 1 was marked and two were not. There could be any number of reasons why the trees were not marked, it could have been that they were never marked, the markers could have been taken or fallen off and blown away, or it could be user/technology error. Some of those things are just out of the control of the professor and students and nothing of which could be done to ensure they were still there without having to go out there and find them. 

The last part of this lab exercise was to use the GPS data which was collected by the eTrex GPS, it was doing a track log the entire time we were searching for points, and use that data to create a map showing where the 5 points were and also what the path we took was.

The map shows that we took a very wrong way to find our first point, the slope and vegetation had a large part to play in that. As for the second point, it shows that we were going in circles trying to locate the unmarked tree, thinking it was a marked tree. Lastly, the third point showed very similar traits to the second point. Points 4 and 5 were not attempted due to the the early sunset.The points are assigned by elevation, As the color scheme goes from green to red elevation gets higher.

Discussion

From what was learned in this lab we can say it is much easier to navigate with a GPS at your expense, but if one is not available, or you are in an area where it does not work or it simply broke/lost being able to navigate by compass and navigation maps is an important skill to know. The map we used for this lab did have a downside to it, it used 5 meter contour intervals, When looking at some areas of the map, they did not appear to be as steep as they were in person, personally I would decrease the range of the interval to maybe 2 or 3 meters just to avoid having to climb a steep slope that does not look as steep on paper. Other then that, the maps worked great, the interval between grid lines did not cause any major problems, the imagery used was a little out of date, but not much you can do about that and the trees not being marked, again is something that is out of your control and nothing can be done to assure everything is properly marked.

Conclusion

Overall this lab was both good and bad. It was good to learn how to use the tools provided and learn how they can come in handy, but this lab also had a down side. The Priory, although a beautiful location, may of been more harmful then good due to its large size, rolling landscape and extensive vegetation that made walking in a straight, or relatively straight, line impossible to do.

Lab 5: Development of a Field Navigation Map

Introduction

If technology once again fails you, it is important to be able to know how to get from place to place by using an "old school" map. In order to do this though we first must make this map. We will go into greater detail about making these maps later on in this blog post, but the major factor which will make this map usable is the grid we will be adding to it which will have a set amount of feet/degrees between each grid mark. This map will be used to locate points at the Priory in Eau Claire, Wisconsin. We will use a pace count to create a trail to locate the said points at the Priory.

Pace Count: A pace count is a set number of steps, starting with either your right or left leg, and then counting every time your opposite foot hits the ground. So if you start with your right leg, you count every time your left food hits the ground.

Methods

In order to understand a map we must know roughly how many paces were in 100 yards. Walking 100 yards and counting every time you go one pace I had a count of 62 paces per 100 yards. This information will be useful in the next lab as that is when we will be going out into the field.

The second part of this lab was to create two base maps, the first using feet as the unit of measurement and the second using degrees. In order to create the maps we used data from the Priory Geodatabase created by Dr. Joseph Hupy of the University of Wisconsin-Eau Claire Geography and Anthropology Department. The information I used for my maps was aerial imagery from ESRI ArcMap and then the Priory Boundary to indicate the study area. A 50 foot by 50 foot grid was over laid on the map as well.

The two maps also contained very important elements such as;
-North Arrow
-A Scale Bar in meters
-Relative Fraction Scale
-Projection and Coordinate System
-Grid, properly labeled in correct units
-Background layer (aerial imagery)
-Data Sources Used
-And lastly a Watermark (creators name)

Results


Map 1 uses the NAD 1983 UTM Zone 15 projection
Map 2 uses GCS_WGS_1984

Map 1 has 50 foot grid dividers
Map 2 has the same but instead of feet it is listed in Degrees

Lab 4: Unmanned Aerial Systems Mission Planning

Introduction

The objective of this assignment is to introduce the students to how Unmanned Aerial Systems (UAS), Unmanned Aerial Vehicles (UAV), mission planning and get a basic understanding on how software such as Mission Planner, Real Flight Simulator and Pix4D work. This assignment will be broken up into two separate parts. The first part being, Dr. Hupy will manually fly a DJI Phantom over a study area taking pictures every so often, and then the second part will be using the software we have at the University of Wisconsin-Eau Claire to process the data. Mission planning and Flight Simulators will also be used during this lab.

Before entering the field we learned about several different UAS/UAV systems, including fixed wing,  multi-rotor (quad/hex copters) and what is the difference between an Unmanned Aerial Vehicle/System and an RC (radio control) toy plane.

Fixed Wing

The very first thing we learned about a Fixed Wing (wings that do not move) UAV was that they are not RC planes. The major difference between the Fixed Wing and an RC plane is that the RC plane does not have a computer on board which can be used to collect data. The Fixed Wing UAVs at the University of Wisconsin-Eau Claire have Pixahwk, which are the brains of the system. Everything the Fixed Wing does is related back to the flight station, whether it is being remote controlled or the on board computer is controlling the pre-determined flight plan, all the data is relayed back to the station. Newer UAVs are now starting to use replaceable/rechargeable batteries to lengthen the amount of flight time you can get, instead of having one fixed battery which has to be recharged every so often. The Fixed Wing we were shown had an average flight time of about 1.5 hours and have a cruising speed of over 14 meters per second (m/s). Lastly, we were shown how to get one of these Fixed Winged vehicles into the air, they do not have wheels and are too heavy to throw, so they have to rely on a bungee cord type launch mechanism, making it very difficult to use in small spaces such as cities and dense forests.

Multi-Rotor

In the lab we were shown two different multi-rotor UAVs. One had four rotors (quad-copter) two of the propellers were silver and two were black. This was to indicate that they spun opposite directions. Although not mentioned in class, I believe they spin opposite directions to assure a smooth and stable flight. The spinning of the propellers in opposite directions also makes this device capable of going any direction with the ease of a switch.

The other type of multi-rotor UAV we saw was a 6 engine one. This one, much heavier than the previously shown UAVs required two large batteries to operate with. This UAV has a much larger payload then the others, but with the increase of payload, we will also see an increase in power usage. The six-rotor UAV thus only has a flight time of about 35 minutes, less if you are attempting to carry some heavier equipment.

With all three of the seen UAVs, an increase in speed then means that we will see an increase in turning radius. The multi-rotors are capable though of slowing down and being able to hover, thus eliminating the large turn radius, but the fixed wing UAV is not capable of hovering, otherwise it will stall, so it must use a large turn radius if it is going to fast.

Part 1: Demonstration Flight

Under the University of Wisconsin-Eau Claire Walking Bridge, Dr. Hupy flew his DJI Phantom unmanned aerial vehicle (UAV). The purpose of this demonstration flight was to demonstrate how a UAV is flown as well as the tools and applications which could be used in this kind of situation. On board the DJI Phantom was a camera which can take pictures of what is directly below. The DJI took several pictures, a switch on the controller initiated when the pictures would be taken. In larger study areas, or different model UAVs, cameras can have settings to take pictures every so often (usually one every 0.7 seconds).

The DJI took over 200 images of two separate study areas; one study area was east of the walking bridge, capturing several parts of the shore, lake and grass areas. The other study area was west of the walking bridge where a couple students used rocks to make a large scale 24 with a circle around it.

Later on these images will be used in the program Pix4D to create a Digital Surface Model (DEM).

DJI Phantom (figure 1)

Pros

-The DJI phantom come as a basic quadcopter starting at $500, but can be upgraded to have a gimbal and a camera with the price still being under $2000.

-Relatively easy to fly for someone who has never flown one before.

-Due to its small size, it is very portable

Cons

-Not a whole lot of control

-installation can be difficult

Figure 1: DJI Phantom in box awaiting assembly

Fixed Wing Vehicles (figure 2)

Pros

-Long Flight Time

-Larger Payload

-Multiple different instruments can be installed to it

-Stable in Windy conditions

Cons

-Long set up/prep time

-Needs a large takeoff area

-Requires large turn radius

Applications

-Precision Agriculture Mapping

-Large Area Mapping

-Ozone Mapping

Figure 2: Fixed Wing UAV

Multi-Rotor Vehicles (Figure 3)

Figure 3: Multi-Rotor UAV

Pros

-Small Turn Radius

-Easy for beginners to fly

-Multiple sensors can be attached to it

Cons

-Small payload capabilities

-Shorter flight time

-Not stable in windy conditions

Applications

-Flying over volcanoes

-Asset Inspection

-Live Streaming events
when I was at Whistling Straits watching the second round I got to get an up-close view of one of the multi-rotor UAVs they were using to get shots of some of the holes along Lake Michigan.

-And one day delivering small boxes from Amazon...

Part 2: Software

After the seeing the DJI fly around on the banks of the Chippewa River, we went back into the labs at the University of Wisconsin-Eau Claire to learn about some of the software we can use to process images, create flight plans and even use flight simulators. In order to process the images we collected, we are going to use a program called Pix4D. This image processing software was used with the DJI Phantom to create a 3D view of the images taken.

Pix4D (figure 4)

Figure 4: The software we used to post process our gathered images

The software we used to create a Digital Elevation Model (DEM) along with other features, with the pictures taken by the DJI Phantom Vision Drone. Of the 200+ images taken, I used a total of 19 to create a point cloud file, which using outside programs such as ArcMap; we can turn it into a DEM raster.

The first step of using this software is to select the pictures you want to use from the file of pictures taken. Again for this I used 19 pictures taken, each containing a portion of the Circled 24 on the bank of the Chippewa River (figure 5).

Figure 5: Selected images to be used for post data collection processing

The next step in this process was to make sure all of the properties for each image were correct. Since the camera used stored data about altitude and coordinates, this step only required a simple click of the mouse on the next tab (figure 5).

Figure 5: A list of images ready to go onto post processing containing multiple forms of data

Other steps before the processing will take place include selecting the type of units you wish to use and what kind of maps you want to be created. The one selected for this project includes orthomoasic maps and a digital surface map (figure 6).

Figure 6: A list of the different types of maps you can make depending on how you run your data

Once done running, a serious of outputs were given in a PDF file, as well as saved as individual files in the selected workplace project folder (figures 7 and 8).

Figure 7: The mosaic and Digital Surface Model created from processing the images collected by the DJI Phantom

Figure 8: One of the maps created showed how much overlap there was between the images

Mission Planner (figures 9-11)

Many newer UAVs have on board computers which can fly themselves, well sort of fly themselves. In order for them to fly themselves we have to use a program called Mission Planner. Mission Planner is a computer software where you can plan your flights and figure out how much time it will take to fly as well as how many pictures you will have to take.

With the risk of other commercial or private flying instruments and vehicles, mission planning is very important. Typically “Drones” have a bad reputation in the eyes of the public, so planning and getting everything all set up is even more important. On the Mission Planner planning page, there are multiple red circles,

Figure 9: Opening Screen of Mission Planner

Figure 10: Study area for UAV flight

Figure 11: Proposed flight plan for a flight to take place in the Study Area

Real Flight-Flight Simulator

The worst thing that can happen to a UAV in terms of expense is crashing it and totally destroying it because you are not familiar with how it works. Luckily, we have Real Flight-Flight Simulators in the lab. This way you can practice (crash) all the UAVs you want without having to worry about damaging $1000s worth of equipment.

Real Flight is one of the most lifelike flight simulators on the market; using a similar control to a real UAV or RC vehicle it gives you the most accurate type of flying. You are able to choose from just about any type of vehicle imaginable. Flight options include hex copter, quadcopter, helicopters, all different types of planes, a gator driving a wind boat, and even a paper airplane.

Part 3: Scenarios

"A power line company spends lots of money on a helicopter company monitoring and fixing problems on their line. One of the biggest costs is the helicopter having to fly up to these things just to see if there is a problem with the tower. Another issue is the cost of just figuring how to get to the things from the closest airport."

First let us run through what we are trying to accomplish with this scenario. We want to come up with a cost efficient way to monitor and fix power lines.

The biggest issue here is the helicopter. Helicopters are expensive to buy, fly and maintain. They need take off space, which often times has to be done at an airport, they need expert pilots who are capable of flying them in such tight spaces. Very dangerous to fly near power lines, both for the power line and the people around. Lastly, if you are only checking to see if there is damage and there is none, you just wasted all that time and money on something that was no problem.

Through this lab exercise, I can suggest the use of Unmanned Aerial Vehicle for this scenario. Most likely a mutli-rotor vehicle because they can hover in place, easily maneuverable, they do not need a whole lot of take off space and can be relatively cheap (compared to a helicopter).

This video, not in English, shows a prime example of how a UAV can be used to inspect power lines with much more ease then the use of a helicopter.

Sources

https://www.aibotix.com/en/inspection-of-power-lines.html
http://www.cbsnews.com/news/amazon-unveils-futuristic-plan-delivery-by-drone/

http://copter.ardupilot.com/

http://fctn.tv/blog/dji-phantom-review/

https://pix4d.com/

http://www.questuav.com/news/fixed-wing-versus-rotary-wing-for-uav-mapping-applications

http://www.realflight.com/index.html

Lab 3: Conducting a Distance Azimuth Survey

Introduction

What will happen if you need to conduct a survey using a fancy GPS and survey stations when the technology fails? Or maybe you are going to a different country and Customs cease your equipment for investigation.  It was not all that long ago that humanity did not have this kind of technology and they seemed to survive just fine. In order for us to combat this metaphorical issue we used a tool which could calculate the distance between you and the object as well as calculate the azimuth.

Using a sophisticated Trupulse Laser Range Finder, we could point to any object we wanted and in turn the range finder would give us multiple values, the only ones we were interested in were slope distance (SD) and azimuth.

Study Areas

For this lab we used four different study areas. The first study area used was done in class as a way of testing the equipment out. On the southeast side of Phillips Science Hall located where the access road meets the back parking lot (44.796281,-91.499395) Figure 1. The class shot a series of points to get a better grasp of how this equipment worked. My group (myself and Zachary Nemeth) shot six points from this location.

Figure 1: Study Area 1- Done during class time, Results-Failed

The second study area we used was located on the northeast side of Phillips Science Hall located by the bus stop at the circular drop off point for vehicles (44.797638,-91.498991) Figure 2 and 3. We shot a total of 20 points from this location. Our features included trash cans, black street lights, wooden street lights and silver street lights.

Figure 2: Study Area 2: Facing Campus we collected Street lights and garbage cans from this location

Figure 2: Study Area 2, reverse direction: Here we collected several different street pole types

The third study area we used was located in the campus mall. Choosing the large circle located in the large open area of the mall because of its easily identifiable properties on a map (44.79782,-91.500787) Figure 4. At this location we shot a total of 51 points (bringing out total to 71). We shot presumably every stone located in the amphitheater and recorded them as either single stone, double stones or triple stones depending on how many were grouped together.

Figure 4: Study Area 3: Campus Amphitheater, here we collected data on all of the stone seats (single, double and triple)

Our final study area was located about 20 feet away from the northeast corner of Schofield Hall (44.798761,-91.499713) Figure 5. Here we shot our remaining 29 points to bring our total number of points shot to an astonishing 100 points, taking about an hour to do so. At this location we shot a variety of objects including benches, trash cans and assorted lamp posts (and came across one person who called us weird because they thought we were spying on people). 

Figure 5: Study Area 4: Here we captured several benches and light poles

Each of the study areas chosen all served a purpose, we wanted to pick areas that were relatively open and away from buildings, that way when we put the points on a map they are all going to be spread out and not have 15 points all in a straight line looking one direction. The features we chose to shoot, we wanted something you could see on a map, that way we could see how precise our data was.

Methods

All of our data points we collected using a Trupulse Range Finder (Figure 6). The first step was to acquire this piece of equipment from the UWEC Geography Department. Once acquired, we went off to each of our three study areas listed above in the study areas section of the blog. We had a slight idea in our minds of what we wanted to shoot, but would not make up the decision until after we got to our location on what else we could shoot. 

Figure 6: Trupulse Range Finder, equipment used during data gathering

Once at the study area, we would use the Trupulse Range Finder and look through the scope. Through the view finder there was a square cross hair type of image on the lens, this is what you would line up with what you were trying to find the distance and azimuth of. Once the desired object is in the cross-hairs we hit a small button on the top side of the range finder labeled FIRE. This would cause a laser pulse to shoot out and gather data of the object. Using the arrows on the side of the range finder to scroll through the data we found the slope distance and azimuth of the desired object. One person would be using the range finder and saying the distance and azimuth (as well as the object being shot) while the other person recorded the data into a google spreadsheet (Figure 7). This would be repeated 99 more times. 

Figure 7: Document containing all the information gathered from the field

The next step was to find the exact coordinates of each of our three starting locations. This was done using Google Maps and right clicking on the exact area of study. An option would pop up saying “What is here” and clicking that would give us the X and Y coordinates to the 6^th decimal place. We put that information into our Google Spreadsheet and then copied and pasted the entire sheet into Excel. We did this so we could work with the data in ESRI ArcMap.

In a geodatabase already created in ArcMap, right click and scroll down to import and select table (single). After doing this our data was imported to ArcMap and was ready to be used. The first tool we had to use was called Bearing Distance to Line Tool (Figure 8 and 9). This tool would give a list of options to choose from including X, Y, azimuth and distance options which had to be filled out properly in order for this section to work. 

Figure 8: Bearing Distance To Line tool will give us the lines from the starting point to the feature point

Figure 9: End result from running the Bearing Distance to Line tool

The next step after this was to take those vertices and make them points. This step requires the Feature Vertices to Point Tool (Figure 10 and 11). For this tool there is only three options. Input Features, which is our feature we just created in the previous step, Output feature class, which is for saving purposes and lastly Point Type. For this category make sure to select END for the option otherwise you will end up with double the number of points, since it will take both the start and ending points.

Figure 10: Feature Vertices to Points will give the points at the end of the lines

Figure 11: The end result of using the Feature Vertices to Points tool, layered over the previously ran tool

In order to make it possible to show what each point represents we have to join the points with our Excel file. This is a simple join based on the Object ID. After running the join, I created a map showing the three study areas and what each line was pointing to represented as a different color (figure 12).

Figure 12: Final product of feature points. 

Step Recap

1. Gather Trupulse Range Finder

2. Locate Study Area

3. Use Trupulse Range Finder on desired objects

4. Record data in Spreadsheet

5. Record coordinates of starting locations

6. Import data in Excel Spreadsheet

7. Import data into ArcMap Geodatabase

8. Use Bearing Distance to Line tool in ArcMap

9. Use Feature Vertices to Point tool in ArcMap

10. Join the points feature class with the table by Object ID

11. Create visual pleasing maps

Discussion

On our map we had three different study areas, mentioned above. Two of the three study areas proved to be pretty accurate, but the campus mall Amphitheater one seemed to be off by several degrees. When we were testing the equipment earlier in the week we found that underground wires and Wi-Fi signals could be distorting the sensor. Here I believe that is the issue. All of the points, when looking at the map, seem to be off by about 15-20 degrees and about 3 feet short of the target. The other two study areas went off without a hitch.

In order to rid of any biasness we kept the same person capturing the data through the range finder and the other person kept to typing the data.

Unfortunately, the most recent imagery used shows Centennial Hall still being constructed. This lead to several points looking like they were construction equipment and not the light posts or tables that our actually there today.

Overall we spent about an hour collecting data points in the field. It was around 8am when we started so some points were hard to see, especially ones which were east of us, looking through a range finder when the sun is in direct line of your object is not fun, and incredibly dangerous if done for too long.

Frequently Asked Questions

Q: What am I looking for while scrolling through the range finder?

A: There are 2 different fields we are looking for one is labeled SD meaning Slope Distance, Slope distance takes into account the vertical and horizontal distance of the objects. The second set we are looking for is labeled AZ. This is the azimuth or how many degrees 0-360 we are away from True North. 0/360 would be north, 90 east, 180 south and 270 west.

Q:  What happens if I use the ALL point type on Feature Vertices to Points tool

A: It will double the number of points you have. Instead of just calculating the end points, which is what we wanted to do in this lab, it will calculate the start and end point resulting in 100 features become 200. This will then throw off the table join, resulting in 50 of your points not having a feature description.

Q: There is no current base map with up to data UWEC imagery?

A: In order to combat this issue, I used two images from the Geospatial folder on the UWEC Geography Departments Servers. Although it is not completely up to date, it still gave me what I needed, something the other base layer data could not give me, unless I was zoomed way out.

Results

The image above shows the final product of all the study areas. In total we tagged 6 benches (red), 19 black street lights (orange), 2 cigarette receptacles (light orange), 9 double stones (yellow), 8 Silver Street Lights (green), 34 single stones (blue), 11 trash cans (purple), 8 triple stones (pink) and 3 wooden street lights (grey).

Although we were careful with the data collection, we still had a little skewness to our data. Again this could be contributed to just about anything. It could be from Wi-Fi, or underground cable wires, or it was hazy and foggy during our time of data capturing, there is an infinite number of possibilities which could skew the laser, even human error was a possibility.

Conclusion

Overall, this is a pretty low tech form of mapping features. We were of course using a very expensive tool for this, but it could easily be done using a ruler tape and compass. That would have taken us a lot longer than the one hour we spent outside collecting data though.

This could come in handy if GPS technology is unavailable or if something happened to your equipment making it no longer usable. Although in a world with ever growing technology this tool, range/azimuth finder, has been replaced by far more sophisticated technologies which can easily take out the human error.