Processing UAS data in Pix4D

Charlie Krueger

GEOG 336
Processing UAS data in Pix4D

Part 1:

What is the overlap needed for Pix4D to process imagery?

The recommended frontal overlap is 75% and at least 60% side overlap.

What if the user is flying over sand/snow, or uniform fields?

The recommendation is to increase the overlap between images to 85% frontal and 70% side, fly higher, and have accurate image geolocation

What is Rapid Check?

The Pix4Dmapper allows the user to process using a template that templates are labeled Rapid/low res produces fast results at low resolution to indicate whether the data set is good or not.

Can Pix4D process multiple flights? What does the pilot need to maintain if so?

Yes, it can process multiple flights and the pilot must maintain the same height for both of the flights so the spatial resolution is similar.

Can Pix4D process oblique images? What type of data do you need if so?

Yes, Pix4D can process oblique images with others like interior/exterior and/or aerial/terrestrial and /or nadir.

Are GCPs necessary for Pix4D? When are they highly recommended?

GCPs are not necessary but greatly add to the improvement of the georeferenced and accuracy of the reconstruction of the image. In corridor mapping, building reconstruction, city reconstruction, mixed reconstruction, and large vertical object reconstruction

What is the quality report?

It gives information on the reconstructed surface about how successful it was and how many errors there were during the process. It is a big summary of everything that happened with the reconstruction of the image and puts it into a write up separated by sections of importance.  

Introduction:
This lab was slightly different then previous in the sense that the whole lab would be conducted using a computer. The data that was used in the lab was gathered already by Professor Hupy so it was already stored in a file that was accessible to the class. This lab would give the class a chance to use the program Pix4D, which is an amazing program for creating point cloud images. This program is one of the premier programs and is very easy to use so this is why it would be prefect to get a quick course it in. In a previous lab the class created some georeferenced mosaic of imagery, but the ones that would be created in this program would be much better in quality and would quickly give the measurements of imagery through this program. This program could do the work that top groups of the class like 3 hours to measure the height of the object 30 minutes to do a section at least 1000 times larger.

Methods:

To start the process of creating this image the data was taken from the TEMP folder of the geography program. This was data that was collected in Eau Claire not that far from the University of Wisconsin Eau Claire. The data was taken of a sand mine that worked near the Chippewa river and had large piles and other equipment on it. Data was then saved into a personal folder to be worked with in Pix4D. To start in Pix4D the images were loaded into the program then all the class had to do was sit back and watch the program run two different sets of images. In total it took about 25 minutes to get the data into the program and then to run the different image makers.

Results:
Below are all the results of the quality report produced by the image making process. It explains all the different actions that went into making this image and how well the image came out after the process. As it can be seen by the quality report it goes very in depth about how well the images captured by the drone were then turned into an image that could be used to survey the land and the mass on it.

Here is the start of the quality report which is a big summary of the process that occurred in the image

This image shows the flight pattern that the drone took while collecting data of the area

Here is the volume measurement not working in Pix4D. The lab had instructed the class to measure one of the piles of material in the sand mine to create a volume for that pile. This was not possible because the computer would close pix4D mapper every time that a volume was trying to be valued from the map. If this issue did not occur then a proper volume could have be gathered.

Below are two different measurements being taken from the point cloud data and these are a measurement of a late object in the image and the surface area of the whole image. These tools were easy to learn and really showed how useful this software could be.

Measuring a distance using the image. Here a point in the road is measured to a break in the road

Here is surveying the whole land cover and seeing how much surface area the image had.

Conclusion:
Through this lab the exposure to Pix4D really proved that this program is the best point cloud program out there for creating and analyzing data. Yes it was a bit confusing at first using the program but this is common with all new program users. The only downfall that was found was that the volume measurement tool caused the program to crash, but this could have also been from user error. Overall this program is well beyond any other program that was used during this course and if this program would have been used earlier a lot of the late nights in the lab could have been avoided because this program does it all.

GPS Topographic Survey

Charlie Krueger

GEOG 336
GPS Topographic Survey

Introduction:
This lab gave the class the task of gathering GPS points with a high precision GPS unit. The data points that were collected would then be used to create maps showing the change in something like elevation. The data was gathered with a GPS unit that gave the GPS position, the height above sea level and much more data. This lab was to created to show how the GPS locations could be gathered with different equipment and plus how the data collected can be used to show change by interpolating the data.

Study Area/Methods:
The study area of this lab was a section of land on the campus of the University of Wisconsin Eau Claire. This study area was chosen because Professor Hupy believed that it would show a decent amount of change from the data points gathered. The area was a small section of land on the campus between the buildings of Centennial Hall and Schofield Hall where the new construction had left a large hump of sorts.

The study area is near the circle growth on the map

When it came to collecting the data Professor Hupy demonstrated to the class about how to use the GPS unit that would take the point. This unit was one were a person would hold it and stick it into the ground so it holds the position and does not move around when collecting the data point. Once the point was it was stored inside of the GPS to then later be moved into an Excel spread sheet where the class could use the data. The data was then taken from the Excel document and downloaded into the program ArcMap. ArcMap would be the program that is used to create the maps with the interpolations. Once the data was downloaded and saved in a folder in ArcMap a shapefile would have to be created from the data so that it could be used when using the interpolation tools. The shapefile would be saved in a separate folder that only contained data for this lab. Once the shapefile was create the interpolations could be created using the program tools in ArcMap. These different types of interpolations would create maps from the data points using the Z value or height above sea level in meters in this lab as the main source. The different types of interpolations are defined in a previous blog named Visualizing and refining terrain survey Sandbox Part 2. This blog gives great definitions of the interpolations and when using certain ones comes in handy.

Results/Discussion:
Below are all the interpolations that were run in this lab and like in pervious lab there is obviously large differences between some of the interpolations. The interpolation that really captures the slope of the hillside would be Natural Neighbor because it shows the small step like lines increasing just like the hillside did when it was being measured and recorded. There was a small problem in this lab and it came from the time zones mix up when the GPS was still in time zone 16 when it should have been in 15. This was because it was used by Professor Hupy in Indiana prior to this lab and shows a great example of how technology can always get mixed up even though it was still giving results to the class.

Conclusion:
Overall this lab gave the class an opportunity to work with a very high grade GPS which some may use in their careers later in life. This lab was on the small scale but just shows how the data taken from the field can be used and analyzed by some many different programs such as ArcMap. The lab showed another good way to gather data and showed the class that even with such high grade equipment a small mistake like not changing time zones can throw off an entire dataset.

ArcCollector: Creating a database, features and domains for deployment

Charlie Krueger

GEOG 336

Arc Collector 2: Creating your own database, features, and domains for deployment and use in Arc Collector

Introduction:

For this lab the class was given the task of creating a question that would then be answered by collecting data using Arc Collector. Arc Collector is a program that pairs well with Arc Map Online which is where the data points would be stored before moving them to ArcGIS. Arc Collector allows for a person to plot points on a map in real time and assign data for that point. The data would be analyzed on ArcGIS and then be made into maps showing the answer to the question. The question that was created for this lab was “Do more trucks and SUV have hunting stickers on them then cars?” which would also track other types of stickers such as sports stickers or political one on all different types of vehicles.

Study Area:

The study area for this lab would be the campus of the University of Wisconsin Eau Claire. More specifically the study areas would be limited to the parking lots around the campus. This is where the vehicles that would be studied would be located so the parking lots around the dorm buildings and teachings buildings would be the focus areas. Not every parking lot would be covered during the lab but the data that was collected would be sufficient to answer the question.

Map of the study area with the locations of the data entries

Methods:

To prepare for the collection of the data Professor Hupy told the class that ArcMap Online had a very good tutorial that would help the class set up a database to use and a map to share. This took quite a process to create a database that could then have data added to it in live time when the students were collecting data on Arc Collector.  The database had to be created using ArcMap and was placed into a specific folder where it would be stored. This database would be created to each student’s individual needs for what they were looking to answer with the data that was collected. The domains were where the students would customize things that they were looking for in the research. The domains for the question that was being investigated were the sticker domain, vehicle type, estimated age, and upkeep of the vehicle. Each one of these things would help tell me something about the question that was trying to be answered. The next thing that would be created from the database is the feature class that would be the actual points that would be plotted when using Arc Collector. The feature class that was used was the vehicle type class because this seemed to fit the best and had the least amount of options when defining the class.  Once the database was set up it was to be shared to ArcMap online was that it could be used on Arc Collector. The feature class could then be added onto the base map that was online and then the map was saved and was ready to have data added to it. This was then when the students could go out and add research data to the maps that were created.

Results/Discussion:

Table of the Data

             When first analyzing the data it was clear that the campus of UW Eau Claire has much more cars and SUVs than it does trucks. This makes sense for a college campus because cars are usually cheaper to purchase and to fill up with gas. So the data does not have that many trucks, jeeps, or station wagons which were thought to be the vehicles to have the hunting stickers on them. The data that was collected did show that trucks did have more hunting stickers on them then cars and SUV do. So going back to the question of the project it was answered by the data collected even though the data lacked more trucks in the survey. When collecting data, it shows that the upper campus was more of the main focus and this was because the bottom part of campus can always be changing with vehicles coming and going where upper campus has less movement of vehicles because the drivers live in those dorm halls where the vehicle is parked. An issue that did arise during the collection of data was the fact that many vehicles would have more than one sticker on them and then the decision of which category they would be put in came up. The way this was resolved was if a car had more type of one sticker than another type it was placed in the sticker group with the higher court of that sticker. Another thing that was noticed was issues such as a UW Eau Claire sticker that also had mention of a sports team on it. This was difficult because it represented two groups of sticker at the same time while only being one sticker. Overall the data that was collected was very representative of the campus but as always with more data comes a more accurate answer to the question that is being asked.

Interactive Map of the Original Data Collected

Map of the Vehicle Locations of the data

Sticker type and the locations on them in the study area

Conclusion:

               The need for proper project design is very big when looking to answer even simple question such as the one in this lab. There were issues when collecting data that were not thought of before and after the fact it was too late because the database was already created. Yes, the question did get an answer for the collection of the data but could be better when looking at the amount of data that was collected. If one thing that could have been different it would have been the classification of the stickers and to think about the fact that usually people have more than one vehicle sticker. Also if given another project such as this it would be good to make sure that Arc Collector is working properly before heading out to collect data and finding out that the domains are not in it.

Micro Climate

Charlie Krueger

GEOG 336

Arc Collector Part One: Microclimate

INTRODUCTION

In this lab the class was given the task of gathering data about things like temperature, wind speed, and dew point on campus through a program called Arc Collector. Arc Collector is a program that can be downloaded onto a smartphone or a tablet and allows the user to collect data that will be uploaded to a map in ArcMap Online. Everyone in the class had to create an ArcMap Online account so that the data that was collected could be uploaded to the class map so everyone could access the data. Professor Hupy had already created a map of the study area that the class would be using and dissected that area into five sections so that the data was not all from a small section. The class was separated into groups of two people and then sent into different sections of the study area.

STUDY AREA

The study area that was set by Professor Hupy consisted of the campus of the University of Wisconsin Eau Claire. The study area was separated into five sections so that the groups would get a wide range of data of the area because of the fact that the University does have a wide variety of setting on the campus. There was a section that included the walking bridge and the other side of campus also a section that had the large hill and the area with many dorm buildings in it.

Area of Study divided into the Sections. The section across the river is area 1, to the right of that is area 2, below that is area 3, section 4 is along the river, and section 5 is the farthest to the left. 

METHODS

The methods that were used in this lab were done so with smartphones and a hand held Kestrel 3000 Pocket Weather Meter. The smartphones which had the Arc Collector program on them were used to track the GPS position of the groups as they moved about the campus. Arc Collector was used to plot the point where data was collected and to enter the information that was gathered there. A table would pop up on the app and then things like temperature, dew point, wind speed and direction were entered into the table to then be used later. This data was gathered by using the Kestrel 3000 Pocket Weather Meter which has a small display screen of the information that it is displaying and has arrow buttons to change what is being viewed. Both of these tools were easy to use because smartphones are so widely used in today’s society and the Kestrel was also very easy once Professor Hupy demonstrated with it.

Kestrel 3000 Pocket Weather Meter

            So with both of these tools the groups took off around campus to gather data from all different locations. The group was set into section five of the area of study which was up the hill and around some of the building on campus. Some locations were in the sunshine and blocked by buildings from wind, while others were very windy and in the shade of a building. Certain sections of the study area gave access to different types of environmental features like being over the river to take a wind measurement. Some sections were on top of the hill on the campus which is a very steep incline and had more wind because of the elevation. All the data points and the information taken at those points was sent back to the map in ArcMap online for the class to use the data in making map. The information was saved by each member of the class and then was used to created map that would interpret the data the was gathered and also look for changes on them.

Map of the area with the different groups points in different colors

The information was brought into Arc Map and all that it contained was data points and the information that the class had gathered. From here the data would be placed onto a base map that showed where in the study area the points were taking at. The next step was setting up a mask for the interpolations. This meant creating an outline for the points so that when an interpolation was run it would only use the data from inside that outline. Otherwise the interpolation would not be showing the change of just the data points but of a much larger area where no data had been collected. The final step was running the interpolations for the temperature, dew point, and the wind speed. The interpolation that was used was nearest neighbor which selects the value of the nearest point and then uses that to determine a value for the space. The interpolation spline was not working in Arc Map and gave very strange outputs where nearest neighbor looked to follow the data that was provided.

RESULTS/DISCUSSION

Temperature Change Map

This is the first map that was created from the data and used the temperature that was gathered at the points. The data that was collected shows the how the temperature is different around campus and the minimum and maximum temperature that were found. As the maps indicates the highest temperature was found around the center of the campus. This could be from the lack of wind from the surrounding buildings and the possibility that the points collected were directly in the sun light. The coldest temperatures were found near the very steep hill of the campus which is also surrounded by forest which is shown by the dark blue area of the map. A section that also shows dark red which is high temperature is across the river on campus. This is kind of an outlier but could be because of the exposed area and the sun light hitting that area.

Dew Point Change Map

This was the map that was created from the data collected on dew points. Dew point is the temperature of the atmosphere below which water droplets begin to condense and dew can form. The map shows that the higher dew points were found near on higher elevation. The outlier of this map would be the area in the middle of the upper half of campus, the blue section surrounded by the light yellow area. This area may have been created by inaccurate measuring or by the program that was ran to create the map. There may have not been points here to show that the dew point was closer to the color yellow and not blue.

Wind Speed Map

This was the final map and was created by the wind speed information that was gathered. There are not outliers in this map because the dark red section would be the windiest spots on the campus. The upper campus is usually more windy because of the elevation and even with the buildings there can be very strong gust of winds that funnel between the buildings. The other very windy spot is on the campus walking bridge which is normally very windy because the wind blows right down the river and has nothing in the way to create a wind break.

CONCLUSION:

This lab was successful in showing the changes in temperature, dew points, and wind speed. The data that was collected and then made into maps shows the areas around the campus were these changes happen. Arc Collector was effective for this lab and allowed all of the groups to go into separate areas yet still send data to the same map. It also helped when transporting data in ArcMap because the data was easily downloaded from Arc Online. Overall this lab was a success for the class.