Pix4D

Introduction

Over the course of the semester, the class has learned many useful techniques and strategies to help survey data in the field. Much of these techniques were learned using either specific survey equipment or more traditional methods. In this final assignment, the goal was to show how the use of UAS and software for UAS can be used to complete most of the tasks that we completed over the semester in a much shorter time. In this assignment, Pix4D was the software used to explore a UAS mission flown by Dr. Hupy over the Litchfield Mine. 

Methods

All of the data that was used in this project was provided to us by Dr. Hupy. The first step in this project was to create a new folder and paste the necessary data in it. Next, Pix4D was opened and a new project was created. Inside the new project, control points were placed to create a processing area for when an image was going to be processed from the mission. The project that was run, creates an orthomosaic and a digital surface model of the specified processing area. Once the project starts to run, it is a waiting game. The software has to process all of the images taken by the UAS during its mission and then mesh them all together to complete a full image. Once this is done, there are many different tools that you can run with the finished product. For example, you will see in the results, a calculation for the volume of one of the sand piles in the mine, the distance of a line, and the area of a specific zone on the mine. These are just some of the ways that Pix4D can be used along with a UAS to survey land. 

Results

To start off, Figure 1 shows the mission that was run by the UAS. Each red dot signifies an image being captured. Starting from the top right and snaking to the bottom left. The red box with a grey fill represents the processing area that was chosen. 

Figure 1. Shows the mission that was run and the processing area that was chosen.

Next is the results of the orthomosaic and the digital surface model. You can see that both the images are in the shape of the previously defined processing area. Figure 2 shows the orthomosaic on the left and the digital surface model on the right. 

Figure 2. Orthomosaic and digital surface model from the project.

Next, when going over the results of the process, Pix4D gives a report to notify you on how the process went. One of the helpful tools provided is Figure 3. It shows how much overlap in images there was in the processing area. The more overlap of images, the more accurate that area will be. It is ideal, in this case, to have as much green as possible. 

Figure 3. Shows the overlap report from the image processing. 

As stated earlier, there are tools that can be run once the processing has been finished. One  of the tools that was run was the length tool. Figure 4 shows the length tool run. The upper right side of the screen shot is where the results are shown. The line segment that is measured is located towards the bottom left of the image.

Figure 4. shows the length tool being used. 

Figure 5 shows another tool that was used in this project. That tool is the area tool. This tool allows you to create a polygon and measure the area within that space. The green area shown in Figure 5 represents the area that was measured. The results are posted in the top right of the image similar to the previous figure.

Figure 5. shows the area tool being used. 

Figure 6 shows the last tool that was used in this project. The volume tool. This tool, as you can imagine, can measure the volume of an object in the digital surface model. To show how this works, one of the sand piles was used. In Figure 6 you can see the mound is highlighted, and the results are posted in the top left of the image. 

Figure 6. shows the sand mound that had its volume measured. 

Conclusion

Over the course of the semester we learned many ways to survey data out in the field. From the total station survey to the ArcCollector survey, I feel that I learned a lot about the different ways of going out to collect data. This UAS and Pix4D assignment was the perfect assignment to end on for this course. It was an excellent way of bringing everything together to show the class that UAS can be one of the most useful and versatile pieces of surveying equipment that we can access. The power that they have to create mosaic images and digital surface models has changed the game. I wish that we would have been able to work with the UAS more through out this class so that we could get an even better feel for its potential. 

Topographic Survey

Introduction

This weeks assignment focused on using a survey grade GPS to take elevation data from specific areas on campus. The data was taken from a small patch of grass located in the Campus Mall. The study area was surveyed because the land was rather uneven and it would give the class a chance to utilize the high powered GPS to create digital elevation models. 

Methods

This assignment required the class to make a trip outside to collect some data. The overall study area was small. A small patch of grass located in the campus mall was used as the study area. The class used a high precision, survey grade GPS. The GPS unit was used by every individual in the class. This means that there were roughly 20 points taken to create a DEM. The survey method used was a random sample. This means that the GPS unit was moved to random areas throughout the study area to make sure that all of the points were not being taken from the top of the hill area or at the bottom of the hill. The GPS unit collected both Latitude and Longitude as well as an elevation value.

Once each student got a chance to use the GPS to survey a point, the data was transferred from the GPS to a text file. The data was then imported into an excel spreadsheet from the text file. Once there was a working spreadsheet, it was time to create a file geodatabase. With the file geodatabase, the excel spreadsheet data could be imported and used to create the DEM. This was done by importing the spreadsheet and creating a point feature class. Using the point feature class, different tools could be run to create different DEMs. An IDW, Kriging, Natural Neighbor, Spline, and TIN were all created from the points. All of these DEMs were created in WGS 1984 UTM zone 15 projection so that there is minimal distortion. With these rasters, maps were created to show the elevation changes in the study area. To finish off, these rasters were placed into ArcScene to create 3D images of the rasters to better show the elevation changes. 

Results

The results of this project include all of the different raster DEMs created as well as the different 3D images created. It is interesting to look at how the different interpolation methods change how the data is interpreted. Figure 1 shows the final rasters created in ArcMap.

Figure 1. This is the compilation of all of the rasters that were created in this assignment using different interpolation methods.

Figure 2 through 6 show how all of the different interpolation methods are different through the 3D image creation of ArcScene. 

Figure 2 shows the Kriging interpolation method displayed in ArcScene.

Figure 3 shows the Nearest Neighbor interpolation method displayed in ArcScene.

Figure 4 shows the Spline interpolation method displayed in ArcScene.

Figure 5 shows the TIN displayed in ArcScene. 

Figure 6 shows the IDW interpolation method displayed in ArcScene. 

From looking at these different interpolation methods, you can see which methods worked well for this project. Seeing as there was one larger hill on the Southern side of the raster, the method that truly best displays it is the Nearest Neighbor method. The TIN and Kriging also represent this fairly well. The issues that arise with the IDW and the spline are that you can see specific holes and mounds that do not actually exist in the area.

Conclusion

This project gave the class the opportunity to work with a high precision, survey grade GPS in order to create multiple DEMs to represent a small area on campus. This was a nice project to get to work with the survey grade GPS because we were surveying such a small area that it was very easy to visualize what the DEMs should come out looking like. It was interesting to see how different interpolation methods can change the output by so much. For example, the IDW ended up looking like a completely different area. Overall, this was a very good assignment to teach us how the survey grad GPS works and how to use it as well as refine our skills in creating DEMs using different interpolation methods and being able to interpret them. 

Hadleyville Cemetery ArcCollector

Introduction

The Hadleyville cemetery project that was completed earlier in the semester was a large group project where the class had to go out and collect data on graves and put together a final map showing grave locations and containing a database for the graves. There was a high potential for error when the data was compiled into one big database because each team collected data from different sections of the grave. The purpose of this assignment was to go back to the cemetery and utilizing ArcCollector, test some of the graves to see if there were any errors in the original data.

Study Area

The data was collected from the western half of the cemetery. The cemetery is located in Eleva, Wisconsin. Eleva is approximately 15 minutes south of the Eau Claire campus. Figure 1 is a map of the study area for this project. 

Figure 1. Map of the study area for this project.

Methods

This project was heavily reliant on the geodatabase that was created before the data collection process. The database had to be created with domains so that the data entry process in ArcCollector would go as smoothly as possible. The first step was to create the geodatabase. The next step was to create the domains in the geodatabase. The domains that  were created were Date of Birth, Date of Death, and status. These domains would be helpful when entering certain pieces of data. The next step was to create the actual feature class and add all of the necessary attributes that would be recorded. The attributes in the feature class were grave ID, first name, last name, DOB, DOD, status, and joint tombstone. These were the attributes chosen in the previous project so it made sense to use them again. Once the feature class was created, it was time to go to the cemetery and collect the data. This portion of the methods is rather self-explanatory. Each grave was surveyed to the best ability. Some of the graves were difficult to read but collecting as much data from each grave is important. Once the data collection process was finished, it was time to compare the data. 

Results

After going back and looking at the two databases, the data is, for the most part, the same. The biggest issues withing the two databases is that some of the graves are shown in different locations. This changed up some of the grave IDs. Other than that there were only a few errors. Figure 2 shows the final map for the original Hadleyville project. 

Figure 2. The original Hadleyville project map.

The online map that was created with ArcCollector can be found at this link: ArcCollector Map

As you are able to see, between the two different maps, some of the placement is off. This is because when using ArcCollector, the GPS isn't as accurate as we might like. This is especially true in this case. Because the cemetery is in a rather remote location, the cell service isn't very good. This had a negative effect on the results. Figure 3 shows the original data table where you can see some of the similarities and issues.

Figure 3. Part of the original dataset.

Conclusion

This project allowed me to go back and revisit a previous project to see if it was done accurately. This was a good learning experience because I got to see what it was like to basically do the same data collection in two different ways. This allowed me to see the pros and cons of the two data collection processes that were used. If I were to do this again, I would have brought the original data that was collected along when doing the ArcCollector process. This would have helped to see right away if there were issues in the original data. To conclude, it would seem that, for the most part, the original Hadleyville project was done accurately. 

Microclimate Data Collection

Introduction

The purpose of this activity was to utilize Arc Collector to collect micro-climate data from many different locations on the UWEC campus. Everyone was divided up into two person teams and sent to specific zones within the study area. The class as a whole was supposed to walk around and collect climate data for specific points. The data that was collected included the temperature, dew point, wind speed, and wind direction. This data was all able to be collected by the use of a tool that could digitally measure all the required data. 

Study Area

The study area for this project was the main Eau Claire campus. This excluded Mcphee Center and any buildings South of that. The Study area was divided up into zones so that each team was able to collect data in a smaller region. My group was assigned zone 2. Zone 2 included areas around Schofield Hall, Schneider Hall, Centennial Hall, Hibbard Hall, and the Zorn Arena. Figure 1 is a map of the entire study area and the different zones within the study area. Figure 2 shows zone 2 within the study area.

Figure 1 is the entire study area and the zones
it is divided into.

Figure 2 highlights zone 2.

Methods

The methods in this assignment were relatively straight forward. Each group was deployed to their zone and were told to collect somewhere around 20 points. In zone two, the best strategy that was developed for the collection method was to start on the left side of the zone, and move north towards Hibbard Hall. Once at the top of the zone, we zig-zagged back South to try to cover the middle and Eastern portions of the zone. At each point, the temperature was recorded along with the dew point, wind speed and wind direction. 

Some of the points that were taken were in areas that may have been blocked by the wind. There were some points taken in the shade to see if there was a temperature change. All of the data collection was done on each persons smart phone through the Arc Collector app. Because the map itself was shared between the class via ArcGIS online, the map would constantly be updating with other groups collected data points. 

Results

The results for the micro-climate data collection were all compiled in the ArcGIS online map that the class shared. Because the points and data were so easily combined, the only thing that needed to be done was bring the data into ArcMap and create maps showing the results. Maps showing the results of the temperature, wind speed and wind direction were all created. 

Figure 3 shows the temperature data for each point collected. This was an interesting assortment of data. The values range from a max temperature of 66 degrees to a minimum temperature of 48.6 degrees. Some of the factors that could have changed the temperature so much would be direct sunlight versus shade. Another factor that was found was heating vents in the sides of buildings.

Figure 3 shows different temperature data collected.

Figure 4 shows the difference in wind speed. This category was a little more difficult to measure because gusts of wind could effect the maximum reading. Our group tried to find the average reading over roughly 20 seconds. Throughout the groups, the maximum wind speed collected was 10 mph. The minimum wind speed was zero. places where no wind would be found would be directly behind buildings. The two windiest spots collected were both located under the Hilltop bridge. This bridge would create a sort of wind tunnel effect. My group's highest wind speed was collected right off the edge of the Chippewa River on top of the bridge.

Figure 4 shows the wind speed data collected. 

The last map that was created is shown in Figure 5. This map shows the direction of the wind collected at each point. I believe that this map is not as accurate as it should be. This is because all of the groups didn't go over the same method of collecting the wind direction. My group collected the angle in which the wind was coming from.

Figure 5 shows the direction of wind at each point.

Each point has all of these pieces of data stored inside of it. To show this, figure 6 shows what happens when you use the identify tool and click on a point. Figure 7 then shows a sample of what the attribute table looks like for the point feature class.

Figure 6 is the data that is stored in each point. 

Figure 7 shows a sample of the attribute table for the point class.

Discussion

It was interesting to use Arc Collector for this assignment. It seems like it is an effective way to collect simple data and easily record it into ArcGIS online. The major issues that my group ran into were issues involving cell service and phone battery. Today many phone GPS are very accurate, but a lot of the time the accuracy can be effected heavily if the cell service is lacking. Both my partner and I didn't have an accurate GPS position until we were outside. Regarding phone battery, my phone died just before we finished. This could have been a lot worse if we weren't in groups. Overall, for a relatively simple survey, it seems like Arc Collector works very well.  

Conclusion

Overall, this assignment helped to familiarize the class with Arc Collector and a new way to go out and collect data. Using ArcGIS online was also a relatively new experience for me. It was nice to experience such an easy transfer of collected data into a GIS. This assignment proved that Arc Collector is an easy way to create a geodatabase of surveyed data. 

Priory Navigation

Introduction

In this weeks activity, the navigation maps that were create were put to use in the Eau Claire Priory. The Eau Claire Priory is a large, mostly wooded region with many hills and cliffs that make it difficult to traverse. Each group was given five different UTM coordinates and one of the maps that were created in the last activity. To find each one of these points, the groups needed to use classic navigation techniques to find each point. 

Methods

Study Area

The study area for this activity is the priory of the University of Wisconsin Eau Claire. This is a large wooded area that would be difficult to traverse with simply an aerial photograph. Figure 1 shows an aerial image of the priory. 

Figure 1. The study area of the Eau Claire Priory is shown by the black rectangle.

Tools Used

In order to navigate through the priory, different tools were used. The most important tool that we used was a compass. The compass allowed us to find the necessary bearing to make it to the next point. Another important tool that was used was a GPS. The only thing that the GPS was used for was to create a track of where each group had walked. 

Navigation

The actual navigation process was somewhat difficult. As stated before, each group was given a set of five different navigation points in the form of UTM coordinates. The points that my group had to find were:

618011, 4957883

618093, 4957823

618107, 4957942

618195, 4957878

618220, 4957840

Using the UTM navigation map, each point was placed on the map. The next step was to go out and find the points. In each group, it was important that each person play a different role to help navigate. Some of the roles included a pace counter, azimuth control, and leap frogger. The pace counter would walk between points, counting their paces to establish distance. The azimuth control would stand at a point and ensure that the pace counter was heading in the correct direction. The leap frogger would run to a landmark in the general direction of the desired bearing. At each point, the bearing had to be taken so that the group was headed in the right direction. The bearing was found using the compass. It required the use of the "red in the shed" technique. Each point was represented by a pink marker that was either wrapped around the tree or hanging on a branch. Figure 2 shows the first point that was found. 

Figure 2 shows the marker for the first point. The marker was wrapped around a large
tree and was somewhat hidden in the brush.

The next point was one of the few issues that the group ran into. The correct location, according to the UTM coordinates, was found, but the marker was not found. The groups were told that this could potentially happen. In that case, the marker was to be placed. Figure 3 shows an eager navigator marking the second point. 

Figure 3. There was no marker for point 2, so the group had to make sure that it was marked.

The next two points were rather straight forward. All the group had to do was get a bearing. For both the third and fourth points, the leap frogger was able to walk out and see the marker. This was possible because these markers were located in more open areas compared to the first and second points. Figure 4 and 5 show the third and fourth points that were navigated to. 

Figure 4. Another eager navigator found the third marked point. 

Figure 5. The fourth navigation point was found by the group without any issue.

The fifth and last point in the navigation exercise was located in another difficult to reach location. The group was required to climb down a steep cliff into a large valley and then up the other wall to reach the marker. The fifth and final point was located towards the end of the wooded area in the priory. Figure 6 shows a picture of the final marker. 

Figure 6. The fifth and final marker in the navigation.

Results

The results section will be very brief in this assignment because the vast majority of it was the navigation methods. The one thing that needs to be shown is the final track from the group's GPS. Overall, the track looks accurate. Figure 7 shows the navigation map with the GPS track that the group recorded. 

Figure 7. The final navigation map with the GPS track recorded on top. The track is represented
by the maroon dots.

Conclusion

This activity was extremely enjoyable and educational. Learning to navigate without the use of some of the high tech geospatial equipment is very important. There were many things that I learned throughout this experience. Personally, I am an outdoors person, so getting out and navigating through the woods was a blast. All of the things that we saw on this trip, from the wildlife to the miscellaneous items in the woods, brought together an important learning experience.  

Creation of Priory Navigation Maps

Introduction

For this project, the class will be going to the priory in Eau Claire to use navigation maps to find different locations within the grounds. The first step for this project was to create the maps. This post will go over the methods involved in creating the navigation maps. The next post will talk about the process of navigating the priory with our navigation maps.

Methods

Data

To start off, the data that was provided came from the USGS. There was an aerial image of the area, a contour map, a digital elevation model, the study area and an example of a navigation map that showed some contours and some of the physical features in the area. These pieces of data were used together to create an effective navigation map.

Coordinate Systems

For this assignment, two different maps needed to be created.  One of the maps used the WGS 84 coordinate system. The other map used the NAD UTM Zone 15. There are different reasons that these two coordinate systems. WGS is a coordinate system that is standard through out the entire world. The coordinate system is relatively accurate and doesn't have much distortion. Usually it will be used to map at a smaller scale. Universal Transverse Mertacor (UTM) is a system that is divided up into 60 zones that stretch 6 degrees from east to west and from pole to pole. These Zones stay a little more true to the shape of the Earth's surface at a larger scale. UTM is good for mapping more specific areas because more distortion occurs around the edges of the zones. This means that any areas that are spanning across more than one zone will have more distortion. So to quickly summarize the two systems, WGS is relatively standard throughout the world meaning you will get the same amount of distortion just about everywhere. UTM is split up into zones and is more accurate for smaller things that are inside these zones, but becomes less accurate when larger areas are being mapped.

Creating the Maps

First, each map was assigned the correct coordinate system. The next step in the creation process was to bring the study area into the map. This is represented by a black box on the map. The next step was putting in the aerial image of the priory and making sure that it was set to the correct coordinate system. Once the imagery was brought in, the next step was using the tool to convert a DEM into a contour feature class. The contour feature class that was created has a 5 foot contour interval. The contour feature class was put on top of the aerial imagery so that the map will give a sense of the elevation of the landscape as well as showing physical features of the landscape. Once both of these were together on the map, a grid was created. The grid will give measurements and help to show how far different things on the map are from each other. The WGS map grid uses decimal degrees as units and the UTM map uses meters as units. Once the grids were in place on each map, the final map elements were added. This includes a title, author name, legend, north arrow, and scale. 

Results

The results from the first part of this assignment are the two navigation maps that were created. Figure 1 is the map that uses the WGS 84 coordinate system. Figure 2 is the map that utilizes the UTM Zone 15 coordinate system. Both will be used in our next activity when navigating the priory.

Figure 1 is a map in decimal degrees and uses the WGS 84 coordinate system.

Figure 2 is a map in meters and uses the UTM Zone 15 coordinate system.

Conclusion

This activity helped to, first off, get a good visual of the area that we will be navigating. The mapping of the area itself was a relatively straight forward activity but it was enjoyable. I am looking forward to our next activity where we will get to put our maps to use and see the differences in navigation maps using WGS and UTM coordinate systems. 

Distance Azimuth Assignment

Introduction

GPS technology has improved a significant amount throughout the years. At this point, it would not be ideal to leave your GPS technology at home when conducting a survey. However, there are situations where GPS technology and equipment will not be available and a knowledge of manual survey techniques is very important to have. Surveying with a grid based coordinate system will but in many cases, it will not be the ideal survey method. In this lab, a basic survey technique with distance and azimuth was used to map out the locations of trees in Putnam park at the UW-Eau Claire campus. 

Study Area

The study area of this project was Putnam park in the UW-Eau Claire campus. The surveying took place on Putnam Trail located behind the Davies Student center. This was an ideal location to survey different tree locations because of its interesting geographical location. One side of the trail is located in a flood plain that turns into a swampy land in the spring. The other side of the trail is part of the famous hill on the Eau Claire campus. Figure 1 is a map of the study area and where the surveying took place on the campus. 

Figure 1. This is a map that shows the study area of a
survey of tree locations in Putnam park. The study area
is shown by the green box. 

Methods

The class divided into three different groups. Each group had their own origin location where the distance and azimuth was calculated from. Varying forms of technology were given to each group. Some of the equipment that was used was a basic GPS unit to find our origin point, a tape measure to measure out the diameter of the trees that were mapped, a tape measure or a rangefinder to map the distance each tree was from the origin point, and a compass that could calculate the azimuth by looking through it. All of the data was recorded into a notebook to ensure that it could be kept and entered into a spreadsheet later. 

The methods to collecting the data were relatively straight forward. One or two team members would be standing at the origin point in order to collect the initial latitude and longitude of the point. Those team members would also measure the distance each tree was from the origin point as well as collect the azimuth angle. One or two other team members would be located at the tree that was being surveyed. These team members would identify the tree species as well as measure the DBH (diameter at breast height) of the surveyed tree. The remaining team members would be standing by to record the surveyed data. Team members rotated duties so that everybody was able to get experience with each responsibility. Once the required ten trees were surveyed, the group shared the collected data so that everyone had their own hard copy. 

Once all of the groups had completed the survey, all of the data was collaborated into a single spreadsheet. The collaborated data was imported into ArcMap so that the survey could be represented with a map. The Bearing Distance to Line tool in ArcMap used the table that was created to map out the azimuth and distance in a vector format. A line stemmed out from the origin and pointed to each surveyed tree. The next step was to use the Feature Vertices to Points tool to show the origin point along with each of the surveyed trees. 

Results

The original results of this survey were not ideal. Figure 2 shows the original mapping of our points. As you are able to see, the points are not all located inside of the study area. One set of surveyed trees shows up miles south of the study area. It is hard to tell at this point if the reason for error is human error or technological error. There is potential for error in both. It is very easy to incorrectly record the collected data into the spreadsheet. It is also very possible that the GPS was giving an inaccurate location. Regardless There were two sources of error in our original data. One of the sources I am very confident that it was human error and one of our origin points had two numbers mixed up in the X value. Because the study area is such a small scale, minor errors like that can throw off the data to a large degree. 

Figure 2. This map shows the error in the original survey data.
Luckily the errors were easily fixed and the final data is
much more accurate. 

The final map created includes the fix to the large error as well as a smaller error that offset an origin point less than 100 meters. Luckily the errors that were found in the data were easily fixed. Figure 3 shows the spreadsheet of data that was collected out int the field. Figure 4 shows the final map of the study area and the surveyed trees in the study area. The trees are represented by green triangles and the distance and azimuth data is shown by the orange lines. 

Figure 3. This is the final spreadsheet of data that was collected
in the survey. 

Figure 4. This is the final map of the survey showing the azimuths from the origin points
and the surrounding trees that were surveyed.

Conclusion

It was interesting to learn about azimuths and how we can use them to create maps when we are without technology in the field. For the most part, I think that the data that was collected was pretty accurate. I do believe that the errors that were encountered were due to the data entering process. Fortunately, we only were using three origin points. This made it easy to find where the errors were coming from. I think that this lab helped me to learn about other ways to collect data when technology isn't available for use. This is very important in this field because technology is great, but we can not rely on it. If you rely on technology and it fails, you are going to want to be able to work around that.