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.