Lab 12: Data Collection Using ArcPad and a Trimble Juno GPS Unit

 Introduction:

Figure 1: A Trimble Juno with ArcPad software, used for data collection

Throughout the semester, we have had several activities held at the Priory in Eau Claire, Wisconsin, a piece of property acquired by UWEC that is being used as the daycare center that was formerly located in the old education building on campus. A tour of the location can be seen by viewing the video on the right. In addition to the physical building on site, which used to be a monastery, an extensive nature area exists, which served as the location to be test the accuracy of our maps.. With each visit came an increase in our familiarity of the area, features in the environment, and other things that come with repeated exploration of a new place. Using this knowledge, we were tasked with mapping features at the Priory by using a Trimble Juno, seen in Figure 1. In order to accurately do this, a geodatabase needed to be built, containing the features to be collected. From there, the geodatabase would be deployed to the Juno unit, and the data collection could take place. Once accomplished, the data would then be exported into ArcGIS 10.1 for manipulation. The collection of this data by each group can then be used in clean-up projects that desperately need to be done at the Priory. This blog post will detail the process of doing this, and demonstrate the result of the data collection.

Methodology:

Figure 2: A TruPulse Laser, used to gather height, distance, and azimuth values

For our specified features to be mapped, our group decided to map dead trees, standing and fallen. Two features classes were created in a geodatabase with a variety of fields needed for each one, and then projected in NAD 1983 HARN Wisconsin TM coordinate system. Our group was interested in height, trunk diameter, woodpecker use, fungal presense, decomposition and placement of the trunk of the fallen trees, and leaf type of the standing trees. Each field was added to the appropriate feature class, and then domains and subtypes were attached to each of the fields. Domains and subtypes allow for a sort of feature class within a feature class function in which each field has a set of choices associated with it. For instance, the decomposition field was broken into three categories, 1 being light, 2 being medium, and 3 being heavy decomposition. Adding these domains and subtypes allowed for the user to select one of the numbers, instead of manually entering 'light', 'medium', or 'heavy', saving a considerable amount of time. .The last step was to add a base layer to serve as a reference point on the map that was going to be displayed on the Juno. The Eau Claire West, Southeast Quadrant aerial photo was used for this purpose, providing a good deal of detail on the Priory and the surrounding area. The geodatabase, complete with features and base layer, was then imported into ArcMap and saved as a map document. By using the ArcPad Data Manager, the features and basemap were then transferred to the Trimble Juno to be used in the field. Data collection consisted of getting GPS points by standing near the tree, measuring the trunk diameter by using a tape measure, and getting tree height and azimuth values of fallen trees by using a TruPulse laser, seen in Figure 2.

Figure 3: Bearing Distance to Line tool used in the activity

After data collection, the collected features were then imported into ArcMap for manipulation. Having the geodatabase set up made this very simple, with the only real task that needed to be done was finding the X and Y coordinates of the fallen trees to run the Bearing Distance To Line tool. This was done by adding X and Y fields to the Fallen Tree feature class, and then using the distance values provided in ArcMap to add the appropriate values for the corresponding tree. Using these values, and the length and azimuth fields recorded by the TruPulse, the Bearing Distance to Line tool, seen in Figure 3, could then be used to compute the size of the fallen tree and its direction on the ground, as seen in Figure 4.
 

Figure 4: Output of the Bearing Distance to Line tool, showing the direction of the trunk of the fallen tree

 Discussion:

 Using the data collected, a number of maps were able to be created demonstrating the various characteristics of the trees examines. Figures 5 and 6 show the presence or absence of fungus growing on the dead trees.  Figure 7 displays if the trees that were still standing had evidence of woodpecker use, as dead trees are popular nesting sights for woodpeckers. Figure 8 demonstrates the decomposition level of the fallen trees, with 1 being light, 2 being moderate, and 3 being heavy. And finally, Figure 9 shows the leaf type of the standing tree, either coniferous or deciduous. With the exception of one tree, all of the trees examined were deciduous. These maps can be used to investigate subjects like woodpecker habitat in the woods surrounding the Priory, or for determining the location of fallen trees and positioning of the logs for cleaning up the grounds.

Figure 5: Fungal presence of dead trees

Figure 6: Fungal presence of fallen dead trees

Figure 7: Woodpecker use of standing dead trees

Figure 8: Decomposition levels of fallen dead trees

Figure 9: Leaf types of the fallen trees

Conclusion:

This activity was another great exercise in preparing for a field exercise. Having to sit down and plan out the various fields that each feature class completely organized an otherwise chaotic experience. Having each field that we were interested in included in the geodatabase told us exactly what we wanted to examine while in the field, allowing us to start collecting data as soon as we got to the site. Ensuring that the ArcPad file worked prior to going out in the field was extremely important to the process, as this also allowed for immediate data collection. Knowing that the program was going to work exactly as it was designed to was a great feeling, as that would be one thing that would not have to be fixed in the field. All of this preparation led to a straightforward data collection with no real issues. While these maps are by no means a comprehensive list of all of the dead trees at the Priory, the database can be expanded at any time to include all of the trees if that was something that was determined to have importance. Having experience with both the Trimble Juno and the TruPulse laser certainly helped with the process as well, cutting down on the learning curve and allowing us to smoothly collect the values we needed in order to get the desired data.

Lab 11: HABL Launch

Introduction:

A significant section of this course in geographic field methods has been that of balloon mapping, an inexpensive way to obtain fairly high quality imagery of the earth that can be used to provide more up to date images to detail areas where recent satellite imagery has not been taken, specifically the University of Wisconsin Eau Claire campus. A previous blog post detailed the use of a balloon mapping rig, using a Panasonic Lumix eight-megapixel camera on continuous-shot mode, and incorporated those images to create a mosaic of campus, using the software programs MapKnitter and ArcGIS. This is not the only use for a balloon mapping project, however. In addition to the tethered balloon, the rig can be let go and fly into the sky, equipped with a tracking beacon and insulation to keep the camera from freezing up at high altitudes. This blog will detail the processes involved in the flight of the HABL, and the subsequent recovery and post-processing imagery that was obtained through the flight.

Methodology:

Figure 1: Construction of the mapping rig.

Figure 2: Taking the fully inflated balloon to be released

The most important part about preparing for this activity was the construction of the HABL rig itself, which was done mostly in Lab 3. Taking a styrofoam container and modifying the lid so that the FlipCam could point its lens downward in the right direction was important to produce quality images that would be clear, despite the high winds that day. This was done by securely attaching  the FlipCam to the lid by using Velcro pads, with the final product seen in Figure 1. Teamwork was a critical step in getting the camera packed into the rig and getting the rig secured to the balloon.
The materials inside of the rig included a waterproof case for the camera with hand-warmers packed around it to prevent freezing at high altitudes, and a GPS tracker to allow for recovery of the unit. The rig was hung from rope on each of the four corners; about 3 ft. in length, and then the pieces are tied together at the top so that the carriage can swing in flight. Pieces of packaging tape were used to secure the lid on the bottom of the rig, so that the camera would not fall out during the course of the flight. The camera carriage was fastened to the balloon by a series of carabiners.. With the rig already built, the only real preparation that needed to be done on the day of launch was to fill up the balloon to be used. This balloon was significantly bigger than the one used in the previous balloon mapping activities, and made of stronger latex to withstand the amount of pressure that would be bearing down on it as the balloon gained altitude. The balloon was 8 feet in diameter, roughly 3 feet larger than the first one, with the sheer size of it visible in Figure 2. Being bigger meant that more helium would be required to fill it up, taking approximately one hour to fill the balloon up fully and ensure that the mapping rig was securely fastened. We did not want to fill it to the max as the balloon would need room to expand as it rises, so it was not fully inflated for this particular activity. Once the balloon was filled to the desired level, the neck of the balloon was secured with a few zip ties and then folded it over, using a liberal amount of duct tape to ensure that the balloon was closed up. The camera carriage and parachute were then fastened and the rig itself was then ready to fly.

Discussion:

It was roughly two hours when we found out that the balloon had landed in Spencer, WI, with the path of the balloon being visible in Figure 3. The balloon had actually landed in a tree, seen in Figure 4, on a private landowner's property. Negotiations were held to allow us to go on the recovery mission to get mapping rig. After consent was given, the tracking device was followed to lead the retrievers to the selected tree where the parachute had delivered the remains of the balloon and the mapping rig. 

Figure 3: Map showing the path of the balloon rig

Figure 4: The tree in which the HABL rig was found in Spencer, WI, close to 80 miles away from Eau Claire.

Figure 5: Recovery of the balloon, parachute, and mapping rig.

 The entire apparatus was in surprisingly good condition, as demonstrated in the picture in Figure 5. After recovery, the process of getting the data off of the camera could finally be done, and we could then see just what kind of imagery we had collected throughout the course of the balloon's 78 mile flight. Figures 6, 7, and 8 show some frames of the video shot with the camera, in fairly good quality imagery. Haas Fine Arts Center on the UWEC campus is clearly visible in Figure 6, and the same goes with the higher altitude image of the Chippewa River in Figure 7. The balloon eventually got high enough so that the curve of the earth was seen, visible in Figure 8. The video of the entire trip, which was limited to just under an hour due to the amount of memory on the FlipCam, can be seen just below Figure 8. The end of the video, where the flight starts at 7:24, is a little unstable, due the rig just being attached to the balloon by a string, and the winds bombarding the rig. It is the presence of this wind, however, that is the reason the shots of the curve of the Earth in Figure 8 can be visible at all.

Figure 6: Aerial footage of Haas Fine Arts Center, at the beginning of the HABL flight

Figure 7: Aerial image of the Chippewa River, seen from the balloon

Figure 8: Picture demonstrating how the rig captured the curve of the Earth

Conclusion:

The HABL launch was the conclusion of a nearly semester long process of trial and error in the balloon mapping field. Balloon mapping hasn't become widely used in the mainstream geography community yet and we're already on the forefront of it at UW-Eau Claire, demonstrating just how ahead of the curve we are. It is very unique that as undergraduate students we can be a part of the small percentage of people who are using this technique. The technical aspect of this course has given us the opportunity to be hands on with our education, and has given us a vested interest in seeing the success of our project. The teamwork aspect was critical, as this project would not have been completed without the input of the entire class. Delegation of responsibilities created manageable jobs for everybody involved, and covered all of the necessary aspects in preparation for the flight. Throughout the course, the importance of ensuring that all is done before going out in the field was stressed, and the conduct of our class throughout this activity exemplifies our understanding of this crucial step in the field methods process.

The article put out by the UWEC News Bureau for the HABL can be seen here. 

Lab 10: Aerial Imagery Collected by a Helium Balloon

Introduction:

There are a variety of mediums available to gain access to aerial images that can then be used for analysis in the future. Using an airplane or a satellite are common ways, with each equipped with a high resolution capture device to take the desired images. Each of these techniques tends to produce great quality pictures that can then be used in a variety of applications. But they also are extremely expensive ways to collect the data. A cheaper alternative to getting aerial images is through the process of using a helium balloon equipped with a camera in continuous shot mode. An earlier post in this blog detailed the construction of the various rigs that would be needed in order to house the camera securely to the balloon, while still allowing for quality images to be taken. At this point in the semester, the weather was sufficient to take the balloon out and acquire images that can be then used for later analysis, since most satellite imagery of the University of Wisconsin Eau Claire does not cover the recent construction and renovation of campus. This post covers the testing, flight, collection of pictures, and the mosaicking of those pictures.

Methodology:

Getting the Balloon Ready for Launch:

Figure 1: Measurement of rope to determine distance of balloon in the air

Deploying a helium balloon to take aerial photographs may seem like a fairly straightforward process, but it actually takes close to an hour of preparation. The first step involves the marking of rope segments at specific measurements to ensure that we had accurate height distances for each of the balloon flights. Figure 1 demonstrates how this was done. Segments of rope were measured at 100 meter increments, and then marked. Next, the mapping rigs needed to be examined to see how it would fly on the balloon. There was quite a bit of change from the first mapping rig, which can be seen in Figure 2, to the one that was used in the second mapping activity, shown in Figures 3 and 4. The first mapping rig did a sufficient job of protecting the camera, but was very susceptible to wind in the air, being fairly cumbersome and not very aerodynamic.

Figure 2: Examining the mapping rig that was used in the first mapping activity.

Figure 3: Mapping rig used in the second activity, with bottle attached

The design was reworked in the second mapping activity, taking this information into consideration. The rig initially had a two liter soda bottle that would house the camera, but this was determined to defeat the purpose of the arrow design. Instead, the bottle was removed from the design, and the camera was attached securely to the arrow itself, providing the best design to cut through the wind, which would keep the camera. and images that were taken, stationary.
Filling up the balloon with helium was the next step. As the balloon, when fully inflated, was five and a half feet in diameter, this process took a long time to complete. Full inflation ensures that the balloon flies as high as possible, and that, in turn, guarantees the best possible pictures. Just how big the balloon got can be seen in Figure 5, with a student's wingspan being used a gauge to determine if the balloon was fully inflated at that point in time.

Figure 4: Mapping rig for second activity, with bottle removed

Figure 5: Checking to see if the balloon was fully inflated.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Flying the Balloon:

Figure 6: The first mapping rig, airborne and taking pictures

Figure 8: The second mapping rig, which was considerably more aerodynamic.

With our rigs constructed in Lab 3, and the weather cooperating, we were ready to obtain some aerial pictures with the helium balloon. The total airborne object would consist of the camera with a 32GB memory card, Styrofoam case, a Garmin E-Trex, a tracking device, and the balloon, all of which can be seen in Figure 6. The second mapping activity had significantly less equipment with the camera, only consisting of the mapping rig and camera, as seen in Figure 7. Once in the air, a walker on the ground would pull the string attached to the balloon with them as they walked around the UWEC campus. The continuous shot mode on the camera enabled many pictures to be taken in each outing. The first balloon trip resulted in 321 images, and the second trip managed to give us a staggering 4,846 images. The first round of pictures were taken around the main academic center of campus, while the second balloon trip covered a significantly greater area. In addition to the main academic area, the Davies Student Center, Haas Fine Arts building, and some sections of upper campus, where the residential halls are located, were mapped.

Georeferencing:

Figure 8: GPS crew collecting ground control points for georeferencing process

After collecting the images, they were uploaded for the next step of mosaicking. A number of software options were available to us for complete this task. MapKnitter, a website that provides a freeware mosaicking program, ArcGIS 10.1, and ERDAS Imagine, provided the needed functions that we needed. In the first activity, MapKnitter was used to produce a mosaicked image, and ArcGIS was used in the second. The first task that needed to be done in any of the programs was to georeference the pictures. Georeferencing is a process involving relating the picture to a physical area in space. To do this, an aerial image of campus was used as a basemap, and the pictures from the balloon were laid over the image, paying attention to scale of the picture. In MapKnitter, this was done by resizing, rotating, and stretching the images to produce a fluid grouping of pictures that looked like one image, instead of over ten. ArcGIS required setting ground control points, or GCPs for each picture on the basemap. Placing good GCPs is a challenge itself, especially considering the current aerial images for the UWEC campus does not show the addition of the new student center, and all of the construction that is currently taking place. So good GCPs were obtained by using things like corners of existing buildings, lightpoles, lines painted on the streets, and, when nothing else was available, gaps in sidewalks. In the second mapping activity, a group of four was walking around the main areas of campus with Trimble Juno GPS units, seen in Figure 8. Their goal was to establish mapping points that we could use in the georeferencing process. Getting these points would speed up the process, as it would be simple to reference these points in multiple photos, ensuring the highest degree of accuracy. Images were selected based on the amount of overlap, which allows the operator to produce a better looking image that is more consistent, rather than simply selecting single images that show the area of interest, and then moving to another area that did not have any of the features from the first image in it. This also ensures that buildings, lightpoles, and streets are in the same perspective, generally speaking. In order for a visually appealing image, the parallax of each building should look like one is directly over a feature, instead of on the side of it, which would result in a warped view.
  

Discussion:

Figure 9: Mosaicked image of the campus mall, produced with MapKnitter software

When mosaicking the pictures and experimenting with the software that was available for us, each had its benefits and drawbacks. MapKnitter, for example, was extremely user friendly, using simple functions like rotate, resize, distort, and different transparency levels to place each picture in the correct spot at the correct resolution. This made it simple to place the sidewalks in the correct location, as it was only a matter of making one image transparent, and then lining it up with the image that was already laid behind it. Figure 9 shows the finished mosiac of the images from the first mapping exercise, using MapKnitter. The ease of use for this software was also its biggest drawback, however. Toned down for simplicity's sake, MapKnitter lacked the more advanced functions of mosaicking programs, notably the inability to place GCPs. Additionally, the limited number of pictures taken in this first round, and the relatively small area covered, did not allow for a complete scan, which can be clearly demonstrated by the distortion of Schneider Hall, the building on the far right in Figure 9. If more passes with the balloon were taken near that particular area, more overlapping pictures could have been taken, which woudl have resulted in a better quality image.

Figure 10: Mosaicked image of our group's area using ArcGIS Desktop 10.1 to georference and mosaic

Mosaicking in ArcGIS was a different process altogether. The placement of GCPs was critical to having a correct georeferenced image. To ensure this, at least 20 GCPs were used per image, to eliminate the possibility that the image would shift due to the placement of a point in an isolated section of the image. Having these GCPs, in addition to a significantly larger area covered and pictures taken, did make for a better finished image, which you can see in Figure 10. Despite the number of pictures taken, the mosaicked images appears more or less seamless, the goal of the project. The only real problem with this mosaic is the lack of coverage on the south side of Haas Fine Arts center, the large building in the middle of the picture. This is due to us pulling in the balloon, to check if the camera was still taking pictures. After checking that everything was still functional, we did not deploy the balloon again until we crossed the footbridge back to the main area of campus. Had we had the balloon up in the air during that walk, a better group of images would have been taken on the south side of Haas, and would have resulted in a more complete picture.
With the ultimate goal being the production of a single aerial image of the entire campus and surrounding area, the class was divided into six groups to each produce a mosaicked image of a particular area of campus. Once this was done, each of the images would then be mosaicked together to produce a single image of campus, which can be seen below in Figure 11. While being by no means the quality image of something like Google Earth, for the most part, the final image is a highly accurate and detailed picture of the University of Wisconsin Eau Claire campus. 

Figure 11: Final mosaicked image of all six group's areas using ArcGIS Desktop 10.1 to georeference and mosaic

Conclusion:

This activity was an incredibly valuable experience.  To the best of my knowledge, not too many people have done balloon mapping, especially in the scale that we had.  The second balloon outing especially confirmed this to me, with close to 5,000 images being taken. This large amount of pictures ensured that issues like inadequate coverage, which was present in the first balloon activity, would never happen. The large number of pictures did create quite a daunting task, however, as going through them to find the image with the right amount of overlap, the desired features being clearly displayed, and ones with the rope from the balloon in the correct area to be covered up by another picture, was a time consuming process. After using both MapKnitter and ArcGIS to mosaic, it is clear that, despite its user friendly nature, MapKnitter was the worst of the two programs. The act of georeferencing and the placement of GCPs is critical to a good image, and ArcGIS is the software to use to do this effectively. Since there has not been much prior documentation on the process, we were left to our own devices as to how to conduct the process. This was a learning experience for the entire class, since nobody had ever mosaicked pictures taken from a device that they had control of. But by being involved in the entirety of the process, we gained a real appreciation of what was needed to get a quality image of the UWEC campus. There were issues that occurred while mosaicking, such as dealing with software issues like the mosaic not finishing, or the order of the pictures being incorrect. But this forced us to understand the entire georeferencing and mosaicking process, and by the end of the activity, we went from being relatively inexperienced to being proficient at it. Knowing how to georeference correctly is an important skill set for any person conducting GIS analysis, and this activity gave us a trial by fire to ensure that we knew exactly what we were doing, leaving us with knowledge that we can use in countless applications. 

Lab 9: Mosaicking Aerial Images Collected by a Helium Balloon

Introduction:

There are a variety of mediums available to gain access to aerial images. Using an airplane or a satellite are common ways, with each equipped with a high resolution camera to capture the desired images. Each of these techniques tends to produce great quality pictures that can then be used in a variety of applications. But they also are extremely expensive ways to collect the data. A cheaper alternative to getting aerial images is through the process of balloon mapping. An earlier post in this blog detailed the construction of the various rigs that would be needed in order to house the camera securely to the balloon. This post covers the testing, flight, collection of pictures, and the mosaicking of those pictures.

Methodology:

Figure 1: The mapping rig, airborne and taking pictures

With our rigs constructed in Lab 3, and the weather cooperating, we were ready to obtain some aerial pictures with the helium balloon. The total airborne object would consist of the camera with a 32GB memory card, Styrofoam case, a Garmin E-Trex, a tracking device, and the balloon, all of which can be seen in Figure 1. Once in the air, a walker on the ground would pull the string attached to the balloon with them as they walked around the UWEC campus. The continuous shot mode on the camera enabled many pictures to be taken in each outing. The first balloon trip resulted in 321 images, and the second trip managed to give us a staggering 4,846 images. The first round of pictures were taken around the main academic center of campus, while the second balloon trip covered a significantly greater area. In addition to the main academic area, the Davies Student Center, Haas Fine Arts building, and some sections of upper campus, where the residential halls are located, were mapped.
After collecting the images, they were uploaded for the next step of mosaicking. A number of software options were available to us for complete this task. MapKnitter, a website that provides a freeware mosaicking program, ArcGIS 10.1, and ERDAS Imagine, provided the needed functions that we needed. In the first activity, MapKnitter was used to produce a mosaicked image, and ArcGIS was used in the second. The first task that needed to be done in any of the programs was to georeference the pictures. Georeferencing is a process involving relating the picture to a physical area in space. To do this, an aerial image of campus was used as a base map, and the pictures from the balloon were laid over the image, paying attention to scale of the picture. In MapKnitter, this was done by resizing, rotating, and stretching the images to produce a fluid grouping of pictures that looked like one image, instead of over ten. ArcGIS required setting ground control points, or GCPs for each picture on the base map. Placing good GCPs is a challenge itself, especially considering the current aerial images for the UWEC campus does not show the addition of the new student center, and all of the construction that is currently taking place. So good GCPs were obtained by using things like corners of existing buildings, light poles, lines painted on the streets, and, when nothing else was available, gaps in sidewalks. Images were selected based on the amount of overlap, which allows the operator to produce a better looking image that is more consistent, rather than simply selecting single images that show the area of interest, and then moving to another area that did not have any of the features from the first image in it. This also ensures that buildings, lightpoles, and streets are in the same perspective, generally speaking. In order for a visually appealing image, the parallax of each building should look like one is directly over a feature, instead of on the side of it, which would result in a warped view.
Unfortunately for this assignment, the lack of communication was an issue. The assignment was to use the images collected through the mapping rig to create a seamless image of the entire UWEC campus that had coverage. As this was an extremely time consuming task, campus was split into six sections. This seemed like a good idea at the time, but there was minimal communication between the groups, and this resulted in hardly any of the mosaicked TIFF files being uploaded into the geodatabase so that a total mosaic could be built.    

Discussion:

Figure 2: Mosaicked image of the campus mall, produced with MapKnitter software

When mosaicking the pictures and experimenting with the software that was available for us, each had its benefits and drawbacks. MapKnitter, for example, was extremely user friendly, using simple functions like rotate, re-size, distort, and different transparency levels to place each picture in the correct spot at the correct resolution. This made it simple to place the sidewalks in the correct location, as it was only a matter of making one image transparent, and then lining it up with the image that was already laid behind it. Figure 2 shows the finished mosiac of the images from the first mapping exercise, using MapKnitter. The ease of use for this software was also its biggest drawback, however. Toned down for simplicity's sake, MapKnitter lacked the more advanced functions of mosaicking programs, notably the inability to place GCPs. Additionally, the limited number of pictures taken in this first round, and the relatively small area covered, did not allow for a complete scan, which can be clearly demonstrated by the distortion of Schneider Hall, the building on the far right in Figure 2. If more passes with the balloon were taken near that particular area, more overlapping pictures could have been taken, which would have resulted in a better quality image.

Figure 3: Mosaicked image of our group's area using ArcGIS Desktop 10.1 to georeference and mosaic

Mosaicking in ArcGIS was a different process altogether. The placement of GCPs was critical to having a correct georeferenced image. To ensure this, at least 20 GCPs were used per image, to eliminate the possibility that the image would shift due to the placement of a point in an isolated section of the image. Having these GCPs, in addition to a significantly larger area covered and pictures taken, did make for a better finished image, which you can see in Figure 3. Despite the number of pictures taken, the mosaicked images appears more or less seamless, the goal of the project. The only real problem with this mosaic is the lack of coverage on the south side of Haas Fine Arts center, the large building in the middle of the picture. This is due to us pulling in the balloon, to check if the camera was still taking pictures. After checking that everything was still functional, we did not deploy the balloon again until we crossed the footbridge back to the main area of campus. Had we had the balloon up in the air during that walk, a better group of images would have been taken on the south side of Haas, and would have resulted in a more complete picture.

Conclusion:

This activity was an incredibly valuable experience. To the best of my knowledge, not too many people have done balloon mapping, especially in the scale that we had. The second balloon outing especially confirmed this to me, with close to 5,000 images being taken. This large amount of pictures ensured that issues like inadequate coverage, which was present in the first balloon activity, would never happen. The large number of pictures did create quite a daunting task, however, as going through them to find the image with the right amount of overlap, the desired features being clearly displayed, and ones with the rope from the balloon in the correct area to be covered up by another picture, was a time consuming process. After using both MapKnitter and ArcGIS to mosaic, it is clear that, despite its user friendly nature, MapKnitter was the worst of the two programs. The act of georeferencing and the placement of GCPs is critical to a good image, and ArcGIS is the software to use to do this effectively.



Lab 8: Culmination of Field Navigation Exercises

Introduction:

The past three entries in this blog have covered topics involving field navigation techniques. Using an aid to orient oneself with an area that is unfamiliar is a key component of being a geographer, and the principal application for a map. A good map leaves the viewer more spatially educated with the content being displayed. The importance of accurate navigation was applied more practically with these previous lab exercises, with a new skill being learned in each one. The first, the creation of a field navigation map, demonstrated the importance of accuracy in cartography skill, as the map, in combination with the compass, was the only aid in navigating the environment surround the Priory, a piece of real estate purchased by the University of Wisconsin Eau Claire. The second activity involved using the map and compass to effectively navigate around land surrounding the Priory, visiting certain navigation points along the way. The third activity transitioned to more technology-intensive field navigation techniques, and employed the use of a Garmin E-Trex GPS unit to navigate to certain coordinates where the navigation points were located. Each activity educated us in the various ways to accurately position oneself in their environment to get where they needed to go. The final activity served as a culmination of each of these sets of skills. For this week, we were to visit all of the navigation points, instead of only following a specified route like the previous activities. Eliminating the start points of each loop left each group to find 15 points, using a field map and E-Trex for assistance. To make the activity more interesting, we were equipped with Tippmann A-5 paintball markers, shown in Figure 1, and told to engage other groups if we encountered them. This added another variable to the activity, with what was previously a leisurely walk in the woods to a fast-paced, competitive scenario that required our navigation skills, as well as testing us physically.

Figure 1: A Tippmann A-5 paintball marker, used in this activity

Methodology:

The first process that needed to be done for this activity was to create another field navigation map, similar to the one that was created in the first activity. Once again, we were given a good deal of creative freedom with what to include in our maps. Similar to the first navigation exercise with the map and compass, a UTM grid was essential for navigation, as the coordinates could be used in conjunction with the navigation points that were provided in the previous lab activity to use the Garmin E-Trex to drop a way point at the desired navigation point, and then use the GPS to navigate there. A number of images of the City of Eau Claire could be selected as a base layer of the map, to get a reference point before starting to navigate the terrain. Like in the first exercise, an aerial image of Eau Claire West, the Southeast quadrant, served as the base layer, which was obtained through the WROC_Specs PDF file in a data folder that we were provided with prior to map design. One piece of data that was not included in the first field navigation map creation activity was a No Shoot Area, which we were required to put in the map. Due to the Priory's primary function as a daycare center for young children, we needed to have a wide buffer between where the kids could be, and where we, and our paintball markers, were. The resulting map that was used in the field activity, which clearly demonstrates the No Shoot Zones, can be seen in Figure 2 below.

Figure 2: Field Navigation map used in the paintball activity, with the No Shoot Zones and the navigation point boundary clearly marked for reference

Figure 3: Latitude, longitude, and UTM coordinates of navigation points for the lab activity

Once arriving at the Priory, each person was given an E-Trex, and a Tippmann A-5. The GPS unit was used primarily to provide a record of the tracklog, so that we could examine our routes after completing the activity. Another function that the GPS provided was the ability to drop way points on our current location, serving as proof of each group arriving at a particular navigation point. Figure 3 shows the UTM coordinates of the fifteen points that we had to navigate. Our group did not utilize these coordinates, however, choosing to rely on our paper map and experience in the wilderness for the two previous activity, to navigate the Priory. Like the previous navigation activity, after completing the navigation, we were required to take the GPS unit and download the tracklog and way point data. Again, we used DNRGPS, an open source piece of software that was built to transfer data between Garmin handheld GPS receivers and GIS software, like ESRI ArcMap, the software package that we used for this activity. After being downloaded, the tracklog and way points were imported into a geodatabase as point feature classes, and then brought into ArcMap to be manipulated further to create the maps found below.
Navigation was significantly more taxing this time due to the presence of the other groups that could open fire on us at potentially any time. The map of our tracklog, shown in Figure 4, demonstrates this perfectly. The route that we took was not organized well, with no real strategy employed from getting from point to point.  The video below the map, made by using the time field on the GPS tracklog data, is a time-based animation, showing the order of points collected.

Figure 4: Field map with imagery of the Priory, no-shoot zones, navigation points, and way points dropped on the routes to get to each navigation point.

Discussion:

Conducting this activity as the capstone of our navigation activities that have been occurring the past few weeks was a great idea. Adding that element of surprise provided by the use of paintball guns was a great challenge to keep us on our toes and change up the dynamic of the class. However, with all of the excitement and potential for engagement of other teams, collecting all of the navigation points proved difficult. Our group, Group 2, managed to collected the most points, but we were one short of getting all fifteen, and I believe that can be attributed to the excitement that led up to a massive firefight that we were involved in. Figures 5-9 below show maps of the other five groups' routes to get from navigation point to point. As we were looking for point 5A, our group saw two other groups that had joined forces and were coming right for us. After engaging them, we forgot to look for the point we were so close to. That is something that would not have happened while simply navigating on foot, without the potential of having to shoot at other teams. Additionally, in the excitement of a firefight, sometimes the GPS unit accidentally dropped a way point, which caused confusion when trying to gauge how many more navigation points our group needed to collect. This also was a contributing factor in failing to collect all of the points, as we believed that we had all of them for at least half of the duration of the activity.

Figure 5: Field map showing the movement of Group 1 in their route to visit all of the navigation points. They managed to arrive and drop way points 11 of the 15 possible navigation points.

Figure 6: Field map showing the movement of Group 3 in their route to visit all of the navigation points. They managed to arrive and drop way points 13 of the 15 possible navigation points.

Figure 7: Field map showing the movement of Group 4 in their route to visit all of the navigation points. They also managed to arrive and drop way points 13 of the 15 possible navigation points.

Figure 8: Field map showing the movement of Group 5 in their route to visit all of the navigation points. They appear to have only managed to arrive and drop way points 8 of the 15 possible navigation points, despite the tracklog record saying that they came close to two points, without dropping a waypoint

Figure 9: Field map showing the movement of Group 6 in their route to visit all of the navigation points. They managed to arrive and drop way points 10 of the 15 possible navigation points.

An issue that did manifest itself in the activity, and the resulting data manipulation was that of GPS accuracy, which was addressed in the previous post. As visible on the map in Figure 4, the way points that were recorded are off slightly from the coordinates of the navigation points, when they should, in theory, be right on top of one another. This is due to a phenomenon called Positional Dilution of Precision, or PDOP, and is an issue that plagues most GPS units, especially smaller ones like the Garmin E-Trex. If there are not enough satellites being able to be received by the GPS, the accuracy of the points can be incorrect. So even though all of the way points were taken right next to the navigation points markers, the heavy tree cover in the area disrupted the clear transmission of the signal to the GPS, resulting in a position disparity between the way points, and the coordinates of the navigation points.
The benefit to using both a GPS unit and a field navigation map in conjunction with one another was clear. Instead of relying on the field map and compass alone, like in the previous activity, and having to walk a straight line in even-paced steps to successfully find the navigation point, the map was used as a supplement to the GPS unit, which was the most effective way to navigate that we have utilized. In the previous blog post, I went into the positives and negatives to relying solely on one of the methods, and ultimately arrived at the conclusion that using both a GPS unit and an accurate field map would ultimately produce a navigation route that would present the least amount of obstacles to the navigator. The paper map would not run out of batteries, and would not have PDOP issues, while the GPS unit would not require the user to plot out bearing directions and walk in perfectly straight lines to reach the desired destination. The two, when used together, made this the most effective navigation exercise, despite the fact that there was the possibility of other groups engaging us with their paintball markers on the way to drop way points at the navigation points.

Conclusion:

The navigation activities at the Priory, and this exercise which required us to use all of our newly acquired knowledge were fantastic learning opportunities. Being able to apply what we have learned in an environment where it is required is one of the best ways to truly understand something, because your ultimate success or failure is based entirely on your mastery of the content. It is up to you if you get all the points, or if you get lost, confused, or do a poor job preparing for the outing. While a lot of individual work was required for this project, ultimately it was team exercise that demanded effective communication in order to be successful. Having each person keep a lookout for other teams and the navigation markers was absolutely essential to completing the activity in a timely fashion. Additionally, if a group member was able to traverse the snow faster than others, they could get the navigation point, while others who would be better reading the map could tell them where to go. Every group member's strengths could be highlighted during this activity, and effective utilization of these skills was a key factor, I believe, in our group's ability to get the most amount of navigation points collected.