Sunday, October 25, 2015

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!

Sunday, October 18, 2015

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.

Image result for Gems sensor for infrared
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. 

Sunday, October 4, 2015

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