Field Activity #7: UAS Flight Survey and Image Processing

Introduction

For this activity, we learned how to process UAS imagery by utilizing Pix4D software and use ArcGIS to preform the unsupervised classification technique. In this exercise, we processed Planet Imagery of the Chippewa Valley Area, UAS imagery from both drone flights from both Geospatial Field Method Section 1 and Section 2. These drone flights occurred in Putnam Park, a local park located behind Lower Campus, and next to residential areas.

This is the processed drone imagery of the November 6th drone flight of Putnam Park.

This is the processed drone imagery of the November 5th drone flight of Putnam Park.

Methods

Some important things to consider before flying a drone is wind direction and temperature. Our drone from the university would only operate 0-40⁰ C and with low wind speeds. Thankfully, although the temperature of the day of our flight was at freezing, we were able to get the drone up and flying.

After completing our drone fight on November 6th, Martin processed the 2D data, and 90 images of the flight, through Pix4D application. This application provided us with a lot of information of the drone flight, involving the elevation of the drone at each point, the type of drone, area covered, wind direction, how long the process was, etc. within the quality report.

This image displays the data being processed in Pix4D.

One error that occurred when processing the data after the drone flights, was although our imperial units in the field were set, they did not reciprocate within the Pix4D program, transforming the units into meters. What is also strange is that the meters to the amount of feet the drone flew did not match up. Therefore, this comes to show that processing drone data is a learning process and there is still so much to know.

This image displays the finished product after processing the data in Pix4D.

After processing different imagery (Planet Imagery of the Chippewa Valley (provided to us by our professor), UAS imagery from Nov 5 and Nov 6 Flights), we used that data in ArcMaps. I used the unsupervised classification using 4 classes, and for both the planet imagery and UAS imagery from the November 5^th drone flight, there turned out to be even a smaller number of classes. This is because depending upon the resolution and accuracy of the imagery, it directly impacts how many classes are produced.

Results

I then produced three maps of with the results of unsupervised classification. These maps were based on classifying the elements from the image. This was done by adding a new field (within the layer’s table), calling it “Class”, then enabled the editor tool to select the layer and type in what you interpret the elements to be. The three maps are shown below:

This map displays the USA Unclassified Image processing technique that was used on Planet Imagery, given to us by our professor, of Eau Claire county. The three classes that I have designated to this image involves Buildings and roadways, surface water, and vegetation.

This map displays the UAS Unclassified Image processing technique that was used with drone data from November 5^th, 2019. The three classes that I have designated to this photo involves build up, not classifiable (blank space), and vegetation.

This map displays the UAS Unclassified Image processing technique that was used with drone data from November 6^th, 2019. The classes I have designated for this data includes ground cover, not classifiable (blank pink space), snow cover, and vegetation/forest cover.

Discussion

With all data processed, it seems as if data from some flights are more descriptive than others, involving more classes and descriptive imagery. This makes me wonder how accurate or advanced drone technology is and why the data taken on November 6^th ended up with more accurate and higher resolution data than November 5^th’s drone flight data. Martin had explained how it takes a while to understand how to work with drones, so instead of thinking of error ridden data as a failure, it is good to think of it as a learning experience. In the long run, this is how you gain foundational knowledge about using drone technology and processing the data.

Conclusion

In conclusion, drone data and how to process it still has its uncertainties we have yet to understand, at least within UWEC’s Geography department. With this experiment however, it was very difficult to learn how to use a drone and learn how to process its data using Pix4D without specific guidance. This comes to show that when conducting a survey to gather data, it is best to understand your instruments fully in order to gather the best data possible in the field. Overall, it is important to look at these technological difficulties not as failures, but as adding a valuable tool to your toolbox.

Field Activity #6: Collecting Data Using Survey123

Introduction

In this exercise, we used Survey 123 for ArcGIS, a convenient tool for gathering field data by creating, sharing, and analyzing surveys by using our ArcGIS Online Organizational Account. As a class, we gathered the data for a survey the professor had supplied for us on “Trees on Campus” with the objective of answering the survey questions for 10 trees in our given area, and then plotting data from the survey. Questions that were asked of us include location, type of tree, structure quality of the tree, among 5 other questions. Although our professor provided us with the data from this tree data on UWEC’s Campus, I created my own project using ArcGIS Online Survey 123 Project. I chose to survey plant life along Little Niagara Creek on UW-Eau Claire’s lower campus, from Phillips Science Hall to the Nursing Building, as pictured below in the reference map.

The area I surveyed at was on UW-Eau Claire's lower campus along Little Niagara Creek. These points were taken using a survey I created with Survey 123 from ArcGIS Esri on my phone.

Methods

Our first step in conducting an experiment on campus, was to create a survey in ArcGIS Survey123. This was a little difficult for me to narrow down a topic, because I could have conducted any experiment I wanted. In the end, I picked this theme for my survey because I was able to recreate what I did for my internship with the Wisconsin Department of Transportation this past summer. Also, I have extensive knowledge on facultative wet and up plants and am interested in the plant life that runs through campus near the Little Niagara Creek.

After creating my survey, I added questions such as “what is the plant type” or “what is the type of foliage the plant has,” among other questions. The next step was then to test out my survey in the field and collect data outside of class.

This photo provides an example of the first few questions I asked in my survey to collect my data.

When collecting points, I mostly walked along one side of the river, as most of the plant life resembled the plant life on the opposite side. Unfortunately, I was unable to connect to the UWEC internet or cellular data, therefore, I took photos of 20 plants along the creek, and filled out the survey forms in the computer lab afterwards. Although this is not the most productive way to complete data in the field, it was necessary considering the equipment I was using, wasn’t reliable.

After completing my survey, I exported the data as a file geodatabase to create a map of my survey area using ArcMap. Below are maps that represent my plant life data.

Results

Plant type was predominately up and wet facultative plants. This shows the kind of plant life UWEC has on it's campus and perhaps the kind of landscaping required along Little Niagara creek.

The abundance of plants in the area spread out about half of the area they resided in, displaying how abundant the plant type is in that area, and where they like to spread out or grow.

The structure of the plant life was predominately good-fair, however, there was a good portion of plants that had poor structure. This can be interpreted as how plant life along the creek changes with the changing of seasons, specifically from fall to winter.

The foliage of the plant was mostly natural looking and fall colored. This can be interpreted as what percentage of plants are already changing colors along the creek, specifically with the changing of seasons from fall - winter.

Discussion

Although using Survey123 was our main tool for data collection, Other data or methods that could be used for collecting the same type of data would be a Trimble GPS unit or a data collector that takes points. The merits when using Survey 123 in my project is that this method provides structure to my survey and keeps this experiment controlled within the confines of the question, and my own subjective observations. However, the demerits of this method could also not provide as much flexibility for this project and the data collected. Overall, this experiment provided me with information and tools to conduct another experiment for personal curiosities within the community. I think this is really helpful as well for someone within the community who doesn’t get the chance to do research but can through creating surveys. 

Conclusion

It was unfortunate however, that my device was not cooperating with me during the experiment, and I was lucky to have been prepared to write down the answers to the survey by hand.  Overall, I really enjoyed surveying for a topic that pertained to what I enjoy and the work I do during my internship. The collection of data on our own using Survey123 can be done anywhere, not just on campus. This project felt very freeing for me, because I was able to complete my own project and go through the motions of how easy it is to be a community scientist, or anyone for that matter!