Processing UAS Data Using Pix4D

Introduction

In this week's activity, the class was introduced to the Pix4D software program, which currently is the premiere software for creating point clouds. Using UAS imagery and association with Pix4D, the class would be constructing a georeferenced mosaic. Prior to using the software, students were to answer the following question, which can be found below along with the answers:

• Look at Step 1 (before starting a project). What is the overlap needed for Pix4D to process imagery?
• A high overlap between images is required in order to get high accuracy results automatically.
• What if the user is flying over sand/snow, or uniform fields?
• Use a high overlap with at least 85% frontal overlap and at least 70% side overlap
• Set exposure settings accordingly to get as much contrast as possible in each image
• What is Rapid Check?
• Rapid check is used in the field to get a quick preview of the outputs
• Can Pix4D process multiple flights? What does the pilot need to maintain if so?
• Yes, Pix4d can process multiple flights, and to do so, the flight height should not vary much between the flights.
• Can Pix4D process oblique images? What type of data do you need if so?
• Yes, Pix4D can process oblique images, and to do so, it is strongly recommended to use GCPs or Manual Tie Points.
• Are GCPs necessary for Pix4D? When are they highly recommended?
• Adding GCPs are optional, but are highly recommended when processing images without image geolocation
• What is the quality report?
• The quality report displays how well the imagery was processed

Methods

To begin a new project, start Pix4D and on the menu bar, click Project > New Project

Step 1: The New Project wizard opens where the user must choose a project name and a directory to save it in (figure 1). Click next.

Figure 1: New Project wizard

Step 2: Import all of the image that are to be used by clicking the Add Images button and selecting the images to be used (figure 2). Multiple images can be selected at once. Click next.

Figure 2: Select Images setup

Step 3: Configure the image properties. This include the image geolocation, selecting the camera model, and images table (figure 3). All of these were automatically uploaded for this assignment.

Figure 3: Image Properties window

 Step 4: Select the output coordinate system (figure 4). In this case, it was auto detected where the coordinate system being used was the UTM zone 15N.

Step 5: Choose the desired template (figure 5). For this assignment, the Ag RGB template was used. Check the 'Start Processing Now' box and click Finish.

Figure 5: Choose template

The initial processing begins and takes several minutes to run. Once the initial processing is complete, and quality report is generated (figure 6). 68 out of 68 images were used and covered an area of 0.0416 km^2 (10.2754 acres). 

Figure 6: Quality report

Scrolling down through the quality report, the user eventually reaches the 'Overlap' section which shows how well images overlapped in which places (figure 7). Images overlapped very well in the center of the image, while some areas on the edges (particularly the left side), did not overlap as well. This may be because images on the outside of the UAS flight path do not have as many images to overlap with as do the images towards the center.

Figure 7: 'Overlap' section of quality report

Next, students were instructed to complete four tasks:

1. Calculate the area of a surface within the Ray Cloud editor. Export the feature for use in a map.
2. Measure the length of a linear feature in the Ray Cloud. Export the feature for use in a map.
3. Calculate the volume of a 3D object. Export the feature for use in a map.
4. Create an animation that 'flys' through the project. 

Using the Ray Cloud editor, the user plots a number of vertices on the image, then right-clicks on the mouse to close the area, and then the surface area is calculated (figure 8). The enclosed 3D area in figure 8 is 5020.53 square meters. This feature was then exported to be used in ArcMap.

Figure 8: Calculating surface area using Ray Cloud

Using the Ray Cloud, the user plots a number of vertices using the 'Polyline' option along a feature length, then right-clicks on the mouse to finish the measurement (figure 9). The terrain 3D length of the road (starting from the lower green dot and finishing at the upper green dot) came to be 189.77 meters. The feature was then exported to be used in ArcMap.

Figure 9: Length feature measurment

 Using the 'Volume' tool, a sand dune was traced along the edges until completely enclosed, and then a volume of 1252.27 + or - 17.83 meters cubed was calculated for the dune (figure 10). The feature was the exported to be used in ArcMap.

Figure 10: Calculating the volume of a sand dune

Finally, another feature of Pix4D is to create an animation of the image and render a flyby, where the user chooses camera angles and speed of the animation, among many other things (Video 1).

Video 1: Flyby video of image

After all of the tasks in Pix4D were complete, the next step was to create a geodatabase (PixProject.gdb) in the designated project folder (Pix4Dproject2) and import the features into it that were created in Pix4D. In ArcCatalog, the features that were exported from Pix4D (surface area, feature length, and feature volume) could then be imported into the geodatabase and allow the user to enter in metadata for each. Once completed, using ArcMap, a map could then be made from the data that was generated in Pix4D.


Results/Discussion

The map produced in ArcMap used the orthomosaic generated in Pix4D as a base image, and the features generated in Pix4D were also included on the map (figure 11). Referring to the legend, the large orange polygon represents the calculated surface area, the small green polygon represents the calculated volume of the sand dune, and the long pink line represents the calculated length of the road.

Figure 11: Sand Mine Analysis Map Using Pix4D

The features did not line up as well as they should have with the orthomosaic, where they appear to be shifted slightly to the south and east. All features and the orthomosaic were projected with the same coordinate system, so it seems as though perhaps an error occurred when transferring the data from Pix4D to ArcMap.

Conclusion

Pix4D is a fairly easy program to use for processing and analyzing UAS imagery. The program offers a thorough tutorial with screenshots and other information for assisting users in navigating the program itself. There were some bumps along the way such as not being able to complete some taks one minute (such as exporting features) and then suddenly being able to the next. This however, could possibly just have been caused by the computer system and not the program itself. The only other "problem" would be that the features do not line up the best with orthomosaic in ArcMap. It's not clear what may be the cause for this, but perhaps future projects or looking further into how to use Pix4D will provide an answer. Overall, Pix4D seems to be a good tool for processing and analyzing UAS imagery.

GPS Topographic Survey

Introduction

For this activity, as a class, students went outside on campus and conducted a topographic survey using a survey grade GPS unit. With the collected data, students were to then import the data into ArcMap and run interpolations and create continuous maps showing the elevation changes. 

The objectives included:

• Surveying the study area using a survey grade GPS unit
• Importing the collected data into ArcMap and displaying the X, Y, and Z data
• Run interpolations using IDW, Natural Neighbor, Kriging, Spline, and TIN interpolation methods and creating continuous maps

Study Area/Methods

The study area was a grassy knoll that featured a small hill on the lower campus of UWEC located between Centennial Hall and Schofield Hall. Due to inclement weather, the survey was brief and only 20 points were collected. The sampling method used was a random stratified method where random points were selected on the grassy knoll and the elevation changes of the hill were attempted to be captured well enough for interpolations that would represent the hill adequately. This was unlike the method used for the sandbox survey conducted previously in the semester where a systematic point sampling method was used where measurements were taken on a grid that was set up with 5 cm increments. 

The first step was to conduct the survey on the grassy knoll using the survey grade GPS unit which can be accurate to sub-centimeter accuracy. To collect points, the GPS receiver was set onto the ground, the two legs were extended to provide stability, then the legs were adjusted until it was as level as possible, which would provide the best accuracy (by lining up the bubble in the level). Once the receiver was lined up properly, students used the handheld device to collect the point. These steps were then repeated until 20 points were collected.

The next step then was to upload the data into a text file format that students could then import into a geodatabase in ArcMap. First a folder was created (GrassyKnoll), then a file geodatabase which the data would be uploaded into and subsequent interpolations would saved in was made (GrassyKnoll.gdb), the text file was copied from the TEMP folder and pasted into the GrassyKnoll folder, and finally the text file was imported into the geodatabase by right clicking on the file in ArcMap and selecting 'Import' and 'Into Geodatabase'.


Results/Discussion

Looking at the results of the interpolations, it's clear to see that the grassy knoll was not represent very well. The grassy knoll featured one, elongated hill the sloped down on each side until it met the edges of the sidewalk (a picture of the knoll may be posted at a later time to show what it actually looks like). An attempt was made to find a basemap image in ArcMap that would show the extent of the knoll where the extent could have been traced and then used as an extent when running the interpolations. However, no basemap image was found to be up to date on what the knoll looks like present day (since it was just recently constructed) and therefore a default, rectangular extent was used when running the interpolations except for the Natural Neighbor interpolation which captures the extent of the knoll best by only extending the interpolation out to the edges of the most outside data points. 

The maps represent elevation where dark green is the lowest elevation and light pink is the highest elevation. The legends were placed in the lower left hand corner of each map so as to not cover up any data points (represented by the dark blue circular dots). The resulting interpolation maps are as follows:

1. IDW

The IDW map appears to be one of the least accurate interpolations where isolated, circular elevations are captured, and elevation is highest towards the left side and lowest towards the right side (figure 1). 

Figure 1: IDW interpolation map

2. Kriging

The Kriging map appears to be one of the most accurate maps where a circular, light pink shape which represents the highest elevation is towards the bottom left side and strands of lowering elevation form around it (figure 2).

Figure 2: Kriging interpolation map

3. Natural Neighbor

The Natural Neighbor map may be the most accurate of the maps elevation-wise and extent-wise where the knoll and its elevation changes are best captured. Elevation is highest towards the lower left hand corner where circular-like shapes of lowering elevation surround it (figure 3).

Figure 3: Natural Neighbor interpolation map

4. Spline

The Spline map appears to be one of the least accurate maps where the highest elevation juts upward from the lower left hand side and uneven, lowering elevation changes surround it (figure 4).

Figure 4: Spline interpolation map

The fact that the results do not very accurately represent the grassy knoll could be due to not enough data points being collected, the knoll not being sampled well enough, equipment error, or a combination of some or all of these reasons.


Conclusions

This activity goes to show how important it is to properly sample a chosen study area. The grassy knoll was not represented well enough as is shown in the interpolations made using the collected data. In future surveys, it will be important to more carefully sample a study area well enough for it to be represented more accurately. 

Arc Collector - Part 2

Introduction 

For this assignment, students used Arc Collector to gather data on a topic of their choosing. The research question that was posed was "What is the level of difficulty of each hole at Mt. Simon Disc Golf Course based on how the holes are set up relative to the landscape?" Mt. Simon is a park located in Eau Claire, WI and features a 9-hole disc golf course. To answer this question, or any research question for that matter, it's important to develop a proper project design that is going to allow the researcher to gather the necessary and correct data for adequately answering the question at hand.

The objectives for this activity include:

• Develop a database in ArcMap to be used with Arc Collector
• Use Arc Collector to gather data
• Use data collected by Arc Collector and make a map that provides an answer to the research question

The instructor provided students with the following link of a tutorial for showing students how to set up and deploy a database in ArcMap for Arc Collector: http://doc.arcgis.com/en/collector/

Methods

The study area for this activity took place at the disc golf course at Mt. Simon (figure 1). To answer the research question, each hole was assessed based on the following criteria:

• Distance (in feet, disc golf courses in the U.S. are measured in feet)
• Elevation change
• Number of obstructions between the tee and the basket

Figure 1: Google map image of Eau Claire with pin over Mt. Simon park

The criteria for this activity included that a point feature class be created with at least three fields for attribute data where:

• One field is a text field for notes
• One field is a floating point or an integer field
• One field has a category of some type for the user to choose from

The first step was to set up a geodatabase where domains, feature classes, and subsequent project work could be saved in. A geodatabase was created titled 'MtSimon.gdb' from which the 'Distance' and 'ElevationChange' domains were then created (figures 2 and 3). The domain type for both domains were 'Coded Values' where the 'Code' and subsequent 'Description' for the 'Distance' domain included:

• 0-200 (0-200 feet)
• 201-300 (210-300 feet)
• >300 (over 300 feet)

and for the 'ElevationChange' it was:

• Flat (no elevation change)
• Slight elevation change
• Moderate elevation change

Figure 2: Distance domain with coded values

Figure 3: ElevationChange domain with coded values

After the geodatabase domains were set up, a point feature class was created titled 'Holes' and would serve to mark the tees and baskets on each hole (2 points per hole for a total of 18 points). In the 'Feature Class Properties' under the 'Fields' tab, the fields were then set up where the 'Field Name' and subsequent 'Data Type' were as follows (see figure 4):

• Hole_number; Short Integer
• Distance; Text (this field utilized the 'Distance' domain)
• Number_obstruct; Long Integer (number of obstructions)
• Elevation_change; Text (this field utilized the 'ElevationChange' domain)
• Notes; Text

Figure 4: Fields for the 'Holes' feature class

After the domains were set up and the feature class was created with the necessary fields, the next step was to upload the map to Arc Collector which would then allow the user to enter data into this map via mobile device. The data was collected in the early afternoon on a weekday where disc golf course activity would be minimized. Each tee and basket and subsequent fields were plotted and recorded where the notes, if any were taken, were only entered after the first point on each hole since entering that section twice was unnecessary. Data collection went smoothly without any issues and the attribute table that went along with the feature class turned out as good as expected (figure 5).

Figure 5: Attribute table of the 'Holes' feature class

Results/Discussion

In order to determine the difficulty of each hole, each hole was "scored" based on the criteria discussed at the beginning of the methods section. Keep in mind, the difficulty ratings given to the holes in this exercise are only relative to each other on this course as opposed to being compared to holes of different courses. The scoring system was broken down as follows:

1. Distance

• (0-200 feet) = 1
• (201-300 feet) = 2
• (>300 feet) = 3

      2. Obstructions

• (0-15) = 1
• (16-20) = 2
• (21-30) = 3

      3. Elevation Changes

• Flat = 1
• Slight elevation change = 2
• Moderate elevation change = 3

Holes with the most points would be deemed the most difficult holes while holes with the least points would be deemed the easiest holes. The holes received the following scores:

1. 6
2. 6
3. 3
4. 4
5. 7
6. 5
7. 5
8. 3
9. 5

Since there were five different integer values for the final scores (3, 4, 5, 6, 7), the holes were given a value ranging from 1 through 5 where 1 was the easiest and 5 was the most difficult. These difficulty values were then added to the map and placed directly between the tee and the basket for the hole that is represents (figure 6).

Figure 6: Final map of Mt. Simon Disc Golf Course hole difficulty rating

Conclusion

Proper project design is essential for adequately answering a proposed research question. For this activity, setting up the geodatabase with its domains and the feature class with the necessary fields allowed Arc Collector to be used properly to collect the data and provided an adequate answer to what the difficulty levels of each hole at Mt. Simon Disc Golf Course are. This sort of project could be used on other disc golf courses, but could be much more elaborate. The criteria for this activity was kept very simple and the layout of the DGC at Mt. Simon is a very simple one as well that allowed the results to match up well with the given criteria. The criteria would have to be more elaborately developed and perhaps more tools would need to be utilized for more technically designed courses. 

Arc Collector - Part 1

Introduction

Arc Collector allows for collection of data in the field using a mobile device (phone, tablet).

Methods

Results/Discussion

Conclusion

Sources

Navigation with a Map and a Compass - Week 2

Introduction

In this activity, the navigation maps that were created in the previous week's activity, will now be utilized along with a compass and GPS unit to navigate the selected study area. Primary components used in land navigation include the following:

• Compass
• Topographical map
• Pace count
• Following a bearing
• Adjusting for declination

The link below provides more detail on land navigation:

http://www.backcountryattitude.com/land_navigation.html

Methods

For this exercise, students utilized the following:

• Navigation map in UTM (figure 1)
• Navigation map in decimal degrees (figure 2)
• Trimble Juno GPS (figure 3)
• Compass (which can be seen in figure 4)
• Pink ribbon (for marking trees at designated GPS coordinates)
• Pen
• Ruler

Figure 1: Navigation map (UTM Zone 15N)

Figure 2: Navigation map (decimal degrees)

Figure 3: Trimble Juno Series GPS unit

Figure 4: Measuring and marking bearings on the UTM navigation map

Each group was given a set of GPS coordinate points that were to be navigated to using the maps and compass. The coordinates were in UTM format and were therefore located on the UTM map and marked for the purpose of then finding and marking the bearings from point to point. The points used in this exercise were as follows:

1. 617708, 4958257
2. 617930, 4957946
3. 617619, 4958049
4. 617852, 4958136
5. 617695, 4958123

Each group was to choose a starting point (ours was on the north side of the building) and proceed to each coordinate point in order from 1 to 5. In order to calculate the correct bearings from point to point, a compass was used by lining up the left or right side straight edge of the compass with the starting point and the destination point. Then the compass dial was turned until "red in the shed" was acheived - that is, until the compass north arrow fell within the compass dial outlined arrow (figure 4). The bearing then was the azimuth on the compass dial that lined up with the fixed directional arrow on the compass (the black arrow located on the top center of the compass). 

After the points and bearings were marked on the map, each group member figured out his or her own pace count. The pace count was determined by calculating how many steps one takes within a given distance (100 meters for this exercise). A measuring tape was pulled out to 50 m in the parking lot where students walked alongside the tape while counting their steps. I walked the stretch of tape once and multiplied my result to get a pace count of 116.

After pace counts were determined, Dr. Hupy set up the GPS units to display the coordinates of the current location and to track students' paths through their respective courses. Finally, once the GPS unit was ready, it was time to navigate the course. 

The terrain that students navigated mainly consisted of fairly thick woods with both older, larger trees, and very young, smaller trees (figure 5). The relief was inconsistent where some spots were smooth and flat and others were quite drastic (figure 6). One group member handled the compass and navigation direction while the other two each handled a map and the GPS unit. Using the compass, a landmark was picked in the azimuth, the group walked to it with the compass bearer leading the way, and after the landmark was reached, another landmark was picked. This continued until the point was reached where the GPS unit was used to verify the correct coordinate location.

Figure 5: Section of woods on navigation course

Figure 6: Reaching GPS point 1 in a deep valley

This process continued until the course was complete. Pink ribbon was given to each group to mark a tree at a coordinate location if no tree had already been marked (figure 7). This occurred at point 2, where the GPS confirmed that we were in the correct coordinate location, but we were unable to find any marked trees and so we marked one ourselves. Also, as a way of checking the we were on the correct navigation path, we would stop about halfway between each point and gather our bearings by using the GPS unit to mark our location on the navigation map, then calculating the bearing and continuing onward (figure 8).

Figure 7: Marking a tree at GPS point 2

Figure 8: Using the GPS and map to gather our bearings

Results/Discussion

The result of our GPS tracks can be seen below, where the tracks are the red dots that form a serrated line and the green triangles are the GPS point labelled 1 through 5 (figure 9).

Figure 9: GPS points and tracks of navigation exercise

Looking at the resulting map, it's clear to see that the tracks do not pass directly through each point. In fact, it only passed directly through point 2, came near point 3, and was further away from points 4, 5, and 1, and the tracks show that our path from point to point was not have extremely efficient routes. There was some deviation off of a straight-line path from most points, especially from the starting point to point 1 and point 2 to point 3 after we navigated around the school.

We found that, for most points, our compass bearings were not leading us directly to the points, therefore forcing us to gather our bearings and recalculate the azimuth to the point. This may have been due to the map we were using (UTM), not taking declination into account, or not following the azimuth correctly. By the time the exercise was finished, we found that we did not even utilize our pace counts and thought that it would not have been that useful anyways (even though it may have) due to the hilly and wooded terrain that forced us off a straight line from point to point and to take steps that were not consistently separated as they had been in the parking lot. Since the coordinate points had been given in UTM coordinates, we did not even utilize the map with decimal degrees. Perhaps having used this map to navigate would have led to better results for our tracks. Using the GPS unit was helpful in gathering our bearings and recalculating the azimuth. Without it, we may have not found the points we were looking for. The contour lines featured on the maps was also not something we utilized very well. Perhaps having contour lines in conjunction with a slope feature class of the terrain would have helped us visualize the terrain we were navigating a little better.


Conclusion

While this activity was not a complete success, it was also not a complete failure. While our group had a good plan in place going into the exercise, we found that by the end we could have employed better navigation methods, and it is because of this that we learned a great deal of what it takes to navigate in a remote terrain (or at least got an idea).

Sources

GPS image: http://mountliming.co.uk/precision-farming/gps-hardware-and-software/ 

Development of a Field Navigation Map - Week 1

Introduction

For this exercise, students will be navigating a local area utilizing two different maps: one utilizing the UTM coordinate system with a grid and the other using the traditional world Geographic Coordinate system using decimal degrees. Along with the two maps, students will use a compass and pace count for navigating. Pace count is knowing how many steps a person takes within a given distance where the common standard is 100 meters.

Methods

Students were provided with access to a geodatabase to work with in the creating of the maps which featured numerous data sets. This material was located in:

• Q:\StudentCoursework\JHupy\Coursedata\336_Geospatialfieldmethods\Geog336_Data

Two maps were to be constructed for this exercise: one containing a UTM grid of at least 50 meter spacing and another with Geographic Coordinates in decimal degrees. The maps need to be 11x17 in landscape format and saved in PDF format and should contain the following elements: 

• North arrow
• Scale bar (meters)
• What the projection is
• What the coordinate system is
• A properly labeled grid
• Background of student's choice
• List of data sources
• Watermark of map creator's name
• Pace count

Grid Map

Layers included in final map:

1. Eau_Claire_West_SE (raster)
2. Slope_grdn452 (slope feature class created from raster grdn45w092_13)
3. Navigationboundary

Steps taken to create map:

1. Project the data frame in NAD 1983 (2011) UTM Zone 15N (Meters) in 'Layers' properties (figure 1)
2. Connect to folder containing Priory geodatabase and my_priory geodatabase (this is where output of tools will be saved)
3. Add Eau_Claire_West_SE, Navigationboundary, and grdn45w092_13
4. Clip grdn45w092_13 to Navigationboundary (figure 2):
• Go to ArcToolbox > Data Management Tools > Raster > Raster Processing > Clip 
• Input Raster: grdn45w092_13
• Output Extent: Navigationboundary
• Output Raster Dataset: grdn45w092_13_Clip
5. Then analyze slope of the output (figure 3):
• Go to ArcToolbox > Spatial Analyst Tools > Surface > Slope
• Input Raster: grdn45w092_13_Clip
• Output Raster: slope_grdnClip
• Output Measurement: Degree
6. Set transparency of slope_grdnClip to 60% (figure 4)
7. Add a grid to the map under 'Layer' properties in the 'Grids' tab (figure 5)
• New Grid
• Measured Grid, hit next
• Enter 40 meters in the X Axis and Y Axis sections
• Finish grid and adjust accordingly

Figure 1: Projection tab example

Figure 2: Clip tool example

Figure 3: Slope tool example

Figure 4: Transparency setting example

Figure 5: Grid tab example

Geographic Coordinate Map

Layers included in final map:

1. Navigationboundary
2. Contour_grdnClip2
3. F1_merged

Steps taken to create map: 

1. Project the data frame in NAD 1983 (2011) State Plane Wisconsin Central (Meters) in 'Layers properties
2. Add Navigationboundary, grdn45w092_13_Clip, F1 and F1_1 to map
3. Create contours of from grdn45w092_13_Clip (figure 6):
• Go to ArcToolbox > Spatial Analyst Tools > Surface > Contour 
• Input Raster: grdn45w092_13_Clip
• Output Polyline Features: Contour_grdnClip2
• Contour Interval: 2 (for 2 meters)
4. Merge the F1 and F1_1 rasters to create one raster (figure 7):
• Go to ArcToolbox > Data Management Tools > Raster > Raster Dataset > Mosaic
• Input Rasters: F1, F1_1
• Target Raster: F1_merged
5. Display labels for Contour_grdnClip2 by going to layer's properties > Labels and selecting 'Label features in this layer' box and adjust labels accordingly

Figure 6: Contour tool example

Figure 7: Mosaic tool example

Results/Discussion

The UTM map with 40 meter spacing can be seen below (figure 8). The coordinate system is NAD 1983 (2011) UTM Zone 15N (Meters), (the zone where the field navigation will take place).

Figure 8: UTM map containing grid with 40 meter spacing

The Geographic Coordinate map with decimal degrees can be found below (figure 9). The coordinate system is NAD 1983 (2011) State Plane Wisconsin Central (Meters), (again, the navigation area falls within this zone). The projection is Lambert Conic Conformal.

Figure 9: Geographic Coordinate system map with 2 meter contour lines, labeled

Data used from Priory geodatabase:

• Eau_Claire_West_SE: this provided a good quality basemap for the grid map
• F1 and F1_1: when merged, a good quality, grey scale basemap for the contour map where contour lines could easily be seen
• grdn45w092_13: a good quality DEM that allowed for numerous surface analyses, but first had to be clipped to the study area to speed up computational processes
• Navigationboundary: shows map reader the study area and was the extent used to clip the grdn45w092_13 DEM

Data not included from Priory geodatabase:

• drg_s_wi035: topographic map that appeared too gritty
• labels: not visible at a large scale
• LidarNE and LidarNW: couldn't get these layers to show when zoomed in on study area
• priory_2ftcontours: created 2 meter contours from grdn45w092_13 DEM

Conclusion

Overall, the map creation was successful, but how well the maps will work in the field navigation part of this exercise remains to be seen. It is necessary to include enough elements within a map in order for it to be useful for navigation, but it is also possible to clutter to the map with too many elements and impede its usefulness. Finding the right balance of elements is key to a useful navigation map. 

Distance Azimuth Survey - Week 1

Introduction

For this exercise, students are to conduct a survey of trees in a park on campus at UWEC using a basic surveying technique: distance and azimuth. After obtaining three different GPS coordinate points, students will then physically measure the distance and azimuth of different trees nearby and record the measurements which will then be transferred into an Excel spreadsheet to be used in ArcMap.


Methods

The materials used in this exercise include the following:

• Laser distance finder (for measuring the distance from the GPS coordinate points to the trees)
• High quality compass
• GPS device (for obtaining GPS coordinates)
• Measuring tape (specially designed for measuring tree diameters, in centimeters)
• Field notebook

The study area was located in Putnam Park on the UWEC campus which features a variety of deciduous and coniferous trees (figure 1). The following attributes were recorded for the survey:

• Distance (from GPS coordinate points to the trees)
• Azimuth (from GPS point to the tree being measured)
• Tree type (ash, oak, pine, etc.)
• Tree diameter (in centimeters)

The distance and azimuth are crucial for being able to make a map of the tree locations in ArcMap, and the tree type and diameter will serve as attribute data for each tree measured. 

Figure 1: Putnam Park trees

The steps that were taken for collecting the data are as follows:

1.  Pick three points from which to gather tree data from, use a GPS device to record the coordinates of these points, and pick a number of trees (10 trees were measured from each point) to record measurements on
2. Standing on the exact GPS located spot, one person would walk to a tree of choice with the laser distance finder receiver while another, standing on the point with the laser distance finder, would calculate the distance from the point to the tree, keeping each unit of the distance finder as level as possible
3. Using the compass, the person standing on the point would line up the compass with the center of the tree and record the azimuth
4. Using the tree measuring tape, the person next to the tree would wrap the tape around the tree at chest height and record the tree diameter
5. The tree type was then recorded where the species was determined based on the type of leaves, bark, and any other features that would indicate the type of tree it was

After the survey was conducted, the next step was to combine all of the recorded data and enter it into an Excel spreadsheet where the data was entered into the following columns:

• x: longitudinal GPS data (cells formatted to numerical with 6 decimal places)
• y: latitudinal GPS data (cells formatted to numerical with 6 decimal places)
• Distance: distance from GPS point to tree (cells formatted to numerical with 2 decimal places)
• Azimuth: compass direction from GPS point to tree (cells formatted to numerical with 1 decimal place)
• DBH: tree diameter in centimeters (cells formatted to numerical with 1 decimal place)
• Tree_type: type of tree
• P_number: GPS point number (1, 2, and 3)

Once the data was entered and formatted, the next step was to import the data into ArcMap in order to create a map of the tree points. The steps taken to be able to use the Excel data to create the map are as follows:

1. Project the data frame in NAD 1983 HARN Wisconsin CRS Eau Claire (meters)
2. Create a work folder for the project, connect to the folder, and create a file geodatabase within that folder 
3. Bring the Excel spreadsheet into ArcMap to look it over before performing tools
4. First tool: ArcToolbox > Data Management Tools > Features > Bearing Distance to Line, and then enter the following into the appropriate fields:
• Input table: Excel table
• Output: tree_survey (save in previously created geodatabase)
• x field: longitudinal field
• y field: latitudinal field
• Distance field: Distance column (meters)
• Bearing field: Azimuth column
5. Once all of this is entered, run the tool
6. After the tool is run, run the next tool: ArcToolbox > Data Management Tools > Features > Feature Vertices to Points, and then enter the following into the appropriate fields:
• Input features: output from Bearing Distance to Line (tree_survey)
• Output: tree_points (save in the geodatabase)
7. Once everything is entered, run the tool

The 'Bearing Distance to Line' tool take the x-coordinate field, y-coordinate field, azimuth field, and distance field and creates a new feature class containing lines (figure 2). The 'Feature Vertices to Points' tool then takes the vertices of the lines created by the previous tool and creates a feature class of these points (figure 3).

Figure 2: Lines created from 'Bearing to Distance' tool

Figure 3: Points created from line vertices using the 'Feature Vertices to Points' tool

Results/Discussion

The resulting image produced from the survey can be seen below (figure 4) where tree points (green triangles) are shown against an aerial image basemap. While the final image appears to be quite accurate and is representative of the data collected, there were some obstacles that arose and adjustments made along the way.

Figure 4: Tree points produced from survey

The biggest problem that occurred was that, after the 'Bearing Distance to Line' tool was initially ran, two of the three GPS points and subsequent lines were inaccurate, where GPS point 3 was highly inaccurate and was approximately 3,000 meters south of the actual location (figure 5, lower red dot at the bottom of the blue ellipse). This error could have been a result of one or both of the following:

1. Equipment error: the GPS unit did not calculate the correct coordinate (highly unlikely, especially for GPS point 3, but possible), or
2. Human error: the GPS coordinates could have either been recorded and/or transferred into Excel incorrectly (most likely cause)

Figure 5: Image of the three GPS points and (red dots within the blue ellipse) where GPS point 3 is located at the bottom and shows just how far off it was compared to the other two points  

GPS point 2 was also not in the correct location, appearing in the middle of the parking lot behind the Davies Center about 75 meters north of the actual location (top group of lines in figure 6 below). GPS point 1 appears to be accurate, therefore no correction will be needed. The GPS points were all chosen along Putnam Drive (the path running from the upper left corner to the lower right corner in figure 6), so to line up GPS points 2 and 3 with the path, the latitudinal coordinates were adjusted. 

Figure 6: Result of 'Bearing Distance to Line' tool after GPS point 3 (left group of lines) was corrected, but GPS point 2 (upper group of lines) is still inaccurate

Using the 'Identify' tool in ArcMap, by clicking in a location directly above GPS point 3 and below GPS 2, coordinates were displayed in the spot that was clicked. Using the given latitude coordinates, these were then used to replace the related coordinates in the Excel table. After the table was updated in Excel, the 'Bearing Distance to Line' tool was ran, this time with a more accurate result (see figure 7 below). 

Figure 7: Result of 'Bearing Distance to Line' tool after latitudinal coordinate were adjusted in Excel for GPS point 2 and 3

Since the collection of data for this exercise was a group effort, it's difficult to discern just how accurate the adjusted coordinates for GPS points 2 and 3 are (GPS point 3 appears to be slightly off path). The highest confidence in accuracy goes to GPS point 1 which was not adjusted. Unfortunately for this exercise, sub-meter accuracy is crucial for pinpointing trees to their exact location, so if someone were to use the map produced from this survey, it may be difficult to locate the trees from GPS points 2 and 3 if the adjustments made for them were not accurate enough.


Conclusions

Overall this survey was fairly successful. All of the attribute table was measured and recorded without too many problems (although identifying certain tree species was somewhat difficult) and the methods worked well for accomplishing these tasks. However, either due to equipment or human error, the resulting map featured inaccuracies (some worse than others). It will be important going forward in future activities and surveys to be extra cautious, pay close attention to measurements and data recording, and double check that data transfers are correct.