Activity 5: Development of a Field Navigation Map

Introduction

In next week's exercise, we will be navigating through the Priory in Eau Claire. In order to prepare, just as in any other navigational task, we needed to create a tool to help us navigate. Sometimes this tool can be a GPS, maps, or even the sun and stars as seafarers often and primarily used as late as the late 1700s. For our navigation through the Priory, we made a map from which we can find locations by estimating foot steps as an approximate, defined distance (pace count method) and both a compass and GPS.

Methods and Results

We were placed randomly into groups of three. Each group member created two maps of their own using the UTM coordinate system for our area and the WGCS in decimal degrees. We then handed in our best map for printing to use in next week's lab.

We used data from a Priory Geodatabase created by Dr. J. Hupy. Our first step was to decide which features to include on our maps. From this data, I decided to use Priory aerial imagery for a basemap and 12-foot interval contour lines as any interval less than that cluttered the map and any more would not give us an accurate reading of elevation changes. I also used the Priory boundary to depict our study area and clip contour lines to the area of interest. I left out other irrelevant feature classes in the geodatabase such as the no shooting zone. 

I added all the necessary map information including:

• north arrow
• scale bar (meters)
• RF scale
• projection name
• coordinate system name
• labeled grid
• basemap
• list of sources
• my name

The first map utilized WGS84 (World Geodetic System 1984) (Fig. 1). WGS is a coordinate system in which the coordinates are generated by using the Earth's ellipsoid shape as a spatial reference (2). The positions units are given in decimal degrees. Maps utilizing a WGS are typically used for operations in large study areas.

Fig. 1: Priory Map utilizing GCS_WGS_1984.

For the second map, I created gridlines representing coordinates in the NAD 1983 UTM Zone 15N. Universal Transverse Mercator grids (UTM) split the world into 60 zones, each only 6 degrees in longitudinal width. This lessens the distortion in the area due to projection from the spheroid shape of the Earth to a flat map surface (1). Because the grid lines cover a more localized region and the associated minimal distortion, UTMs are typically used for smaller study areas such as states and counties. Metadata for both maps was then created in Map Document Properties (Fig. 3).

Fig. 2: Priory map utilizing NAD 1983 UTM Zone 15N.

Fig. 3: Metadata for the GCS map (right) and the UTM map (left).

Discussion

For this project, we had to think critically about the amount and type of information we included in our maps in order to navigate effectively and map points within a relatively accurate margin in a later exercise. I decided to include 12-foot contour lines, a boundary of the study area and aerial imagery. Having too much information on the map would crowd the map and make navigating more difficult. It is for this reason, I included 12-foot contour lines as opposed to 2-foot contour lines to show elevation data useful in pace-counting as your step changes with any slope. I added aerial imagery as a base map as this may help visually find features and a boundary of our study area for good measure of the area we should remain inside during our navigation exercise. 

Conclusion

This week's lab was meant to solidify knowledge on designing maps. It is critical to understand the coordinate systems, projections, and grids in order to create a map that will be most useful in a particular scenario. It is also important to understand the reason for making the map and what it will be used for as this will effect the amount and type of information that must be represented in the map. The purpose of each map will dictate somewhat how the map should be designed and it is important to understand this purpose in order to create a map that will be most efficient in completing the goals of its purpose.

References

1) http://pubs.usgs.gov/fs/2001/0077/report.pdf

2) http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf

Activity 4: Unmanned Aerial Systems Mission Planning

Introduction

Unmanned aerial systems are useful tools in collection of a variety of different data types. They can be used to collect atmospheric attribute measurements such as temperatures, ozone content, and humidity values. They can also be used to create aerial maps of specific features of interest such as mines, college campuses, construction sites, and logging locations. Though valuable in the field as a relatively quick method of acquiring quality data, unmanned aerial systems (UAS) require extensive planning before use. In this lab, our objective was to become familiar enough with different types of UAS's and mission planning through demonstration flights and computer software to be able to apply our knowledge to a hypothetical scenario in which a client seeks advice on UAS types to complete their own objectives in data collection.

Methods and Results

Demonstration Flights

At the start of the activity, our professor, Dr. Hupy, familiarized us with the two current types of UAS's: Fixed wing systems (Fig.1) and rotary wing systems (Fig.2). 

Fixed Wing Systems

Fixed wing systems are called so for their immovable wings. They usually have one wing over the top of the body of the vehicle and are moved through air via propellers located at the nose of the vehicle--much resembling commercial aircraft. A major advantage of these UAS's are their ability to fly long distances at higher speeds and for longer durations than multi-rotors as these UASs can fly up to 1.5 hours and at no less than 14 meters/second. This allows them to consequently cover larger areas over a shorter amount of time. In addition, they are generally able to carry heavier equipment, though this depends on the particular UAS.
Among all these advantageous qualities, fixed wing systems also have some disadvantages one must take into account before choosing them as the UAS to complete the job. One thing to consider before choosing a fixed wing system is the space it requires to launch, land, and change direction. To launch, a fixed wing must have enough space to gain lift from first gaining speed on land. Similarly, it will require a certain amount of space to slow to a halt after returning to the ground and changing direction requires a minimum of 160 feet.

Fig. 1: Fixed-wing unmanned aerial system. Picture taken from http://www.buildadrone.co.uk/what-do-i-need.html.

Multi-Rotor Systems

Multi-rotor systems more closely resemble helicopters. They typically have between 1 and 6 rotary blades that they rely on to generate lift. A major advantage of the multi-rotor system is the fact that their blades allow them to move easily in any direction. Unlike the fixed wing, systems, multi-rotor systems are able to change directions without any required space to do so. Similarly, they do not require any space for launch and landing as they can move straight upward and downward. These characteristics in addition to their automatic adjustment for wind make multi-rotor systems very easy to fly. It's ability to hover, adjust for wind, and change direction easily allow the multi-rotor system to capture detailed data and focus on areas with complex features.
Some disadvantages of the multi-rotor systems, however, include its slow speed (do not fly over 12 meters/second), their short flight plan (generally around 30 minutes),  and their cost. Because of their complexity in design, multi-rotors are typically more expensive than their fixed wing counterparts.

Fig. 2: Multi-rotor unmanned aerial system. Picture taken from http://www.dohenydrones.com/the-drone-for-you-fixed-wing-versus-rotary-wing.

Flying the Quadcopter

After learning of the types of unmanned aerial systems and their qualities, the class took the university's DJI Phantom quadcopter (4-blade multi-rotor system) to map features along a portion of the Chippewa River Floodplain in Eau Claire, Wisconsin below the campus footbridge. The DJI Phantom was flown manually as we captured aerial photos of several features in the floodplain such as a straight stretch of the floodplain running parallel with the river, our class sculpted landscapes from Activity 1, and a "24" symbol made of rocks (Fig. 3). This UAS type was appropriate for our data collection because we were focusing on obtaining high resolution and accuracy on a few small features within a small area. Speed and long flight plans were not required for this task. We needed a UAS that was easily maneuverable over varying altitudes and could hover over small features to obtain many photographs of the same feature. A total of 322 images were taken between these three features with emphasis on these three features and little attention to spatial connectivity. Since the features we were collecting aerial imagery of were relatively small, we were able to collect photos with enough overlap to avoid gaps in the spatial data. Images were later uploaded and processed into maps using Pix4D.

Fig. 3: Flying the DJI Phantom quadcopter to collect aerial photography on  a stretch of the Chippewa River floodplain running parallel with the river.

Using Software

Three different computer software programs were used to complete this activity. Pix4D was used to create DSM and orthomosaic maps from the DJI Phantom aerial photographs, MissionPlanner was used to investigate parameters of planning or programming automatic flight plans, and RealFlight Flight Simulator was used for practice in flying UAS's and observing flight behaviors of the two different types of UAS's. 

Pix4D

After the aerial imagery was taken with the DJI Phantom quadcopter, we chose a single feature in the dataset from which to create a DSM and orthomosaic. I decided to map the "24" symbol. A total of 91 images were collected specifically of the "24" symbol, but only 20-30 images were necessary to create an accurate DSM (digital surface model) and orthomosaic. Any more than 30 images will result in diminishing returns on time spent processing to attain higher accuracy. Processing aerial photographs in Pix4D will take a lot of time and requires a substantial amount of core processing ability from the computer you use to run the program, so it is suggested to only use as many photographs as necessary to create an accurate product, but no more as it will only take up time. For this reason, I uploaded 28 photos that were taken consecutively by the quadcopter as to avoid missing portions of the feature and varied little in coloration as at the moment the photos were captured, clouds were moving in front of and away from the sun.

Fig. 4: Finished product in Pix4D of the orthomosaic map. This shows both the overlay of the photos and the mosaic itself.

Fig. 5: The DSM finished product of the "24" symbol feature. This displays elevation where dark color indicates lower elevation and lighter color indicates higher elevation.

Fig. 6: Orthomosaic. This contains information on the elevation and depicts real-life map of the feature of interest--the "24" symbol. 

The resulting DSM and orthomosaic shows little error. In the DSM, the elevation data symbolized by a gradient color scheme from black to white (dark colors indicating low elevation and light colors indicating high elevation) accurately depicts the elevation changes in the feature (Fig. 5). The Orthomosaic seamlessly blends the borders of each individual aerial photograph. The difference in coloration of the land is due to the differences in coloration of the different photos involved in the creation of the orthomosaic (Fig. 6). This coloration difference could have been avoided by selecting only photos of similar coloration--photos taken when the sun was either shining or blocked by the clouds, but not both. Metadata for both the DSM (Fig. 6) and the othomosaic (Fig.7)are pictured below.

Fig. 6: Metadata for the finished DSM.

Fig. 6: Metadata for the finished orthomosaic.

Mission Planner

We used mission planner to investigate the parameters involved in pre-planning a UAS flight. This pre-planning can be used before programming automatic flight plans and manual flight plans as it is always important to consider parameters and plan before using a UAS. In Mission Planner, we adjusted camera angles, altitudes, and speeds of the UAS to see how this would effect approximated flight time and number of flight paths.
Upon adjusting these parameters, I concluded a few notable things to take into consideration when planning a UAS flight. The higher the altitude of the UAS, the less flight paths are required to fully cover the study area. This is because as the UAS increases in altitude, the camera's field of view increases (Fig. 8).

Fig. 8: A camera's field of view (FOV) increases as distance from the object increases. Photo found at https://www.melown.com/maps/docs/acquisition-guide.html.

Because the camera's increased field of view leads to less required flight paths during a UAS flight, this also means the approximated flight time will decrease because the UAS does not need to fly longer distances. However, if you are using the UAS to map digital surface models, altitude should be taken into consideration as the higher the altitude, the lesser the resolution will be on your DSM.


RealFlight Flight Simulator

The last software program we used was RealFlight Flight Simulator. This software allows you to practice flying different types of  UAS's and observe their flight behaviors such as ease of operation, speed, flight time, and stability. We were required to fly two different UAS platforms and observe these behaviors. I chose to fly the "Cap 232," a fixed-wing system and The "Classic," a quadcopter multi-rotor system (Fig. 9).

Fig. 9: The two different types of UAS's flown on RealFlight Flight Simulator. On the right is a fixed-wing system, "Cap 232," and on the left is a multi-rotor system, "Classic."

The "Classic" quadcopter was a very easy craft to maneuver as it could hold a stable position and altitude even with your hands off the controller or against wind. It was also incredibly easy to change direction as it had a zero turn-radius and "pivoted" rather than "turned." However, when turning, it is difficult to keep track of which part of the quadcopter is the front, making it hard to gauge how far to move the controls to turn towards your destination of interest. The maximum speed this UAS could fly was only 14 MPH and this was achieved only when moving in one direction: up, down, or side to side, but diagonal directions slowed the UAS slightly. It's minimum speed was 8 MPH. This particular UAS ran on battery life with a 2500mAh total. After 30 minutes, only 636mAh remained, indicating this system can only have a maximum flight time of just over 30 minutes.

The "Cap 232" fixed-wing system was less easy to maneuver. It must constantly fly and does not have the capability of hovering like the quadcopter. It took longer and more space for this system to gain altitude, however, it was still able to maintain a specific altitude with ease. This craft's top speed was 82 MPH, so if many flight paths are required in a flight plan, it would take some skill to manually fly. It also had a large turning radius and required some space to change directions. This particular system ran on fuel with a total of 15.8 oz in the tank. I was unable to fly this craft for long as expected. After only approximately 20 minutes of constant movement, it had run out of fuel. If flying a fixed wing system for a project, battery power as opposed to fuel would be more effective in obtaining longer flights.

Scenarios

Each UAS has a set of advantages and disadvantages as you have seen in the previous sections of this blog. These qualities as advantages and disadvantages are relative to the project you are working on. For instance, someone working on gathering aerial photographs for a digital surface model of a small area will find the speed of a fixed-wing system to be a disadvantage, while someone wishing to collect data on atmospheric particle content over multiple miles will find the speed of the fixed wing system advantageous. For this reason, after all the information on unmanned aerial systems was collected, we were instructed to apply the information to one of seven real-life scenarios Dr. Hupy has experienced as a consultant for projects using unmanned aerial systems to demonstrate our ability to recommend certain UAS's according to their qualities and the demands of the project. The scenario is as follows:

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

I would suggest a fixed-wing system for this project. Because the client wants to collect data over a large study area, a fixed-wing system is a great way to cover the large distance required for the data collection process and it has the ability to stay in flight about three times as long as a rotary-wing system.


I used Mission Planner to assess the distance a fixed-wing system could fly in less than it's estimated battery life of one hour and thirty minutes to make sure a significant amount of mileage could be covered in less than this time in order to gather the appropriate amount of data without losing the UAS over Lake Michigan (Fig. 10). Even with multiple flight paths for resolution of data (not completely necessary for obtaining ozone data), I was able to hypothetically fly a UAS fixed wing system over 30.32 miles in approximately 51 minutes and account for the space required for its turning radius.

Fig. 10: A potential flight plan to collect ozone over Lake Michigan.

If the client were to take a straight path flight, she would be able to fly a longer distance with each flight to collect even more data. I would suggest keeping the flight time to approximately one hour instead of running the UAS for the full one hour and a half to account for extra power used by the battery when faced with correcting against wind.

Several online sources suggest different altitudes for ozone data collection and I suggest either flying multiple flights each at one steady altitude, or shortening the distance covered by the UAS to fly at several different altitudes in one continuous flight. It is important to keep in mind that with a fixed-wing system, changing altitude is relatively easy, but it takes a certain amount of distance to reach different altitudes and depletes battery life. For altitude, multi-rotor systems are easier to manage, however, they are not able to cover long distances and cannot fly for long periods of time--both of which are a requirement in this project.

Additionally, since the client needs to carry an ozone monitor along with other meterological instruments, the fixed-wing system will be best for this job as they are able to carry heavier weight loads than multi-rotors.

Discussion

The two different types of unmanned aerial systems, fixed-wing systems and rotary-wing systems provide a means of data collection that is taking off fast in the geospatial community. Their advantages and disadvantages are only relative to the type of project you are doing as both have qualities that allow them to accomplish different tasks. The fixed-wing systems are a good fit for any project requiring data collection over large study areas and do not necessarily need a degree of high resolution for any particular land features. This can include projects like the one in the above scenario, collecting ozone, or in the process of data collection for general maps like those used for transportation or land use mapping. The rotary-wing systems are great for small study areas in which high resolution is required for all or some features. This can include projects like creating digital surface models for a single or a few features, or mapping a nesting site of endangered birds. Both systems have qualities that can be of great help when collecting data--their benefit is only dependent upon the type of project you wish to apply them to.

Conclusion

Unmanned aerial systems are useful in gathering many types of data for a variety of different fields and it is only limited by our imaginations. The system itself can be thought of as merely the vehicle and the data it can collect depends on what instruments you wish it to carry. Cameras can create visual maps for applications such as transportation or agriculture as well as elevation models and meteorological instruments can measure ozone, air quality, and humidity among other things. These systems are becoming of great use in not only the geographic community, but to biologists, city planners, and geologists among others. As technology advances, we can only expect to see improvements in these systems and a widening scope of their capabilities. It is important to become familiar with these systems to make using them in projects easier and more efficient.

Sources

http://onlinelibrary.wiley.com/doi/10.1002/asl2.496/full

http://uas.noaa.gov/news/skywisp.html

https://www.melown.com/maps/docs/acquisition-guide.html

http://www.dohenydrones.com/the-drone-for-you-fixed-wing-versus-rotary-wing

Activity 3: Distance Azimuth Survey

Introduction

This week's lecture focused on problems that can occur in the field while collecting data. As technology often fails--for instance, a GPS may not work well in a densely forested region that interferes with satellite connection--we must be prepared with methods to counter any unexpected failing of technology. It is for this reason that this week's lab focused on Distance/Azimuth surveys--a survey method that does not rely heavily on technology. It is possible to create a map of a local study area from having little or no GPS signal. A researcher could potentially view satellite imagery of their study area after collecting points with a distance/azimuth device to find their origin GPS point if their origin is an easily recognizable feature. All objects within the study area will be accurate to that origin point if the researcher is able to find the distance of another feature from their origin point and its azimuth. To gain experience in this method of data collection, my partner and I went out to the campus common area to take these distance and azimuth measurements and create a data table and upload it to ArcMap where we ran several tools on the data to create a map with our feature locations.

Methods

Choosing the Study Area:

We were assigned to obtain 100 data points from a local area of our choice and create a map of our data including distance and azimuth data and attribute data of the features we were to map. My Partner, Scott Nesbit, and I chose the University of Wisconsin-Eau Claire campus commons as our study site because it was a large, open area that could easily be seen on satellite imagery which we could use as a basemap (Fig. 1). It also contained a large number of easily identifiable objects we could use to map our 100 points. Within our study area, we chose an origin point from which to gather distance and azimuth data in a standing position (Fig. 2). This origin point was a site that had an unobstructed view of most of the landscape and was hindered little by elevation differences within the site.

Fig. 1: The Study area (University of Wisconsin-Eau Claire Commons Area).

Fig. 2: Origin Point from which all our data points were collected, It was an easily identifiable and open area.

Collecting Data:

On September 3, 2015 at 3pm, we went out to our study area to collect our data points using a TruPulse 360, a laser range finder equipped with both the ability to calculate distance and azimuth direction, and a GPS unit to find the latitude and longitude location of our origin point. We used the SD mode on the TruPulse 360 for distance and AZ for azimuth calculation.

Fig. 3: Equipment used to take the Distance, Azimuth, and Origin Point data. On the left is the GPS unit, on the right is the TruPulse 360 laser range finder.

We then organized a data table in excel to include the XY coordinate of the origin point, Distance (meters), Azimuth (degrees), and a feature ID describing the type of feature being mapped (Fig.4). We entered all data directly into the laptop to be uploaded to ArcMap after collection. The objects we mapped included the large rock benches, large trees, signs, and all lamp posts within view. Some lamp posts were not recorded as the laser was obstructed by various objects in front of them. Because the lamp posts were also a narrow target to hit with the laser when collecting the distance and azimuth data, we aimed for the top region of the lamp post where it was the widest. Data was not entered for the lamp posts until several re-shots with the TruPulse 360 were taken to be sure the correct distance and azimuth data was being taken. We were able to gather a total of 107 data points.

Fig. 4: Data table in Excel showing XY, Object ID information, Distance, and Azimuth columns. 

Once out of the field, we each created our own map on ArcMap by first importing our data table from excel to ArcMap using the import (single) table tool. I then ran the Bearing Distance to Line tool found under Data Management tools in ArcMap (Fig. 5) to create the distance lines using our data table (Fig. 6).

Fig. 5: Bearing Distance To Line Tool showing selections for all parameters. 

Fig. 6: Bearing Distance To Line Tool output showing lines generated from the Distance and Azimuth data.

After obtaining our line features, I used the Feature Vertices to Points tool under Data Management tools to convert the endpoints of our distance lines into feature class points (Fig. 7).

Fig. 7: Feature Vertices to Points tool output displaying the endpoints of the previously generated lines as a new feature class containing the locations of all objects.

These feature class points are symbolized as Row 1-5 Benches, Lamp posts, trees, and ground signs. Because the Bearing Distance to Line tool does not retain the attribute data, I had to acquire this information by using a table join in which the Feature Vertices to Points feature class was the destination table and the original data table was the source table. I joined the tables using their ID, listed as "ThingID" in our data table to avoid nonrecognition by ArcMap, as their common attribute. I was then able to successfully symbolize the different objects withing this feature class. A basemap was then imported into ArcMap from the University's geospatial data file (Band 1 image from the 2709_29 folder under City_EauClaire_3in in County Data)  to show the accuracy of our data. As the last step, metadata was created for the data (Fig. 9).

Fig. 8: Symbolizing the Feature Vertices to Point feature class.

Fig. 9: Metadata of Feature Vertices to Point output.

Analyzing the Data:

The features in our map did not seem to match well with the imagery basemap (Fig. 10). To fix this problem, I thought perhaps the coordinate systems were not similar. I used the define projection tool on the distance lines as well as the feature vertices to point features. This did not change the outcome by any significant measure. The data seems skewed in such a manner that if the data were to pivot westward on its axis, it may line up better with the features in the imagery map. This inaccuracy could be due to the natural magnetic declination. However, data collected further from the origin point also seems too skewed for any compensation in magnetic declination to correct the error.

Fig. 10: Final Map showing all features within the Campus Commons. You can see the westward shift tendencies of the data points and the increasing skewness of the data points as distance from the origin increases.

Discussion:

Our study area proved to be a good place to collect our data points as it was relatively flattened and open with little obstruction in our view of our objects. There was some problem with obstruction when collecting distant lamp posts, but we tried to compensate by firing the laser to collect multiple distance and azimuth points for each object until we could gauge the most accurate reading. Even in doing so, however, there seemed to be much inaccuracy with the more distant features of our map. This could be due to the equipment. I would suggest having multiple origin points from which to collect the distance and azimuth data to lessen the effect of distance on the accuracy of the TruPulse 360 readings. Another problem with the data seemed to be the effect of the magnetic declination. All data points were shifted westward and were not corrected after defining a projection for data to math the GCS of the basemap. 

Conclusion

This lab allowed me to gain experience with the distance azimuth survey method and to be aware of any potential problems that may arise in the field when collecting data and the usefulness of alternative surveying methods to counteract problems as well as save time. This survey method allowed my partner and I to collect data points in about half the time it did for my group in previous labs. This method could be used in situations where many data points must be collected in a small area. To save time from needing to collect GPS points of every feature, a distance azimuth survey method could be used to instead calculate the locations of each feature from only one or a few GPS points.