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!

Field Activity #5: Collecting Data Using ArcCollector

Introduction

In this assignment, our group collected microclimate data throughout the Lower Campus of UW-Eau Claire, using the Collector for ArcGIS application through ArcGIS online. Through this application, we collected data with a mobile device, a cellphone or tablet used by a student in our group, and created a database, domains, and feature classes from the surveying data collected. After collecting the microclimate data in groups, we merged all of our data into one dataset to use and create multiple maps depicting the data collected from our survey.

Methods

For these methods we used a Kestrel 3000 Pocket Weather Meter. This meter is an environmental tool for monitoring field conditions measuring dew point, wind speed, humidity, and temperature. Our microclimate data collection, included taking readings on this device for humidity, average wind speed, dew point, and temperature at 5cm and 2 meters above the surface.

For our group, we surveyed zone 2, located from Phillips Hall to Davies center and Campus Mall on Lower UWEC Campus. We began at the Parking lot in Davies Center, circling the building and through the Campus Mall, and ending at Phillips Science Hall. Armed with a Kestrel Weather Meter for each person, we compared data taken with each monitor. By doing this, we noticed that the weather meters would stop recording, and stopped at a certain number. From this point on, we noticed what monitors would take the correct measurements and reported the devices that were malfunctioning. 

This image depicts the different zones delegated to each group. The points illustrate each point taken collectively as a class, spanning the entire campus. The points covered a large portion of UW-Eau Claire campus, allowing variability in our data.

This image shows temperature (Degrees F) readings from 5cm across UWEC campus.

This image depicts temperature (Degrees F) readings from 200cm across UWEC campus.

This image displays wind speed (mph) readings across UWEC campus.

This image shows dew point readings (Degrees F) across UWEC campus.

This image depicts humidity (%) readings across UWEC campus.

Discussion

When analyzing the data collected in the field, there were multiple errors in the data. For example, a gust of wind occurs at the exact moment of recording data. Additionally, temperature variations for the few seconds holding and recording the data - which we had to calculate the average temperature as the device switched readings every second. This caused unwanted variation in our data, however, it does prove that the conditions were changing throughout our data collection process. The weather conditions however, were cloudy and slightly breezy at first, but towards the end of our collection, the wind had picked up, and the temperature seemed to be colder.

Conclusion

Overall, this exercise was a good introduction to using Collector through ArcGIS, and collecting data using your personal mobile device. For our group, it was important to be able to have the same goal when collecting data, and to do so in a timely manner. This exercise also introduced myself to assessing and surveying for climate data, and how to manage the data, and present it, by creating a geodatabase with domains, subtypes, feature classes, and fields in order to collect and analyze the data. After this field activity, I feel it is useful to those who want to conduct public science projects, and how to formulate a project and display data of a neighborhood or public area.

Field Activity #4: Conduction a Total Station Survey

Introduction

For this experiment, a survey was conducted within the campus mall of the University of Wisconsin – Eau Claire using a data collector and total station. Throughout this survey, we used the established control points, provided by the Eau Claire County Surveyor’s Office, and collected X, Y, and Z topographic points on campus. The area that we surveyed had relatively low topographic relief as well as a few planted trees and dolomite blocks to sit on provided by the university. The purpose of the experiment is to create a landscape model, using the TIN feature, of the UWEC Campus Mall using the X, Y, and Z points gathered.

Methods:

October 9, 2019

Sunny, ~ 21.3 degrees Celsius

After an introduction to the nature of the survey and the equipment we would be using, we headed from Phillips Hall to the UWEC Campus Mall. Our survey took place within the area below:

The equipment we used throughout this experiment included the data collector, dual frequency GPS unit, and the TopCon GPT Total Survey Station. Both tools were used to maintain data accuracy. Through using these two different methods, we had generated the same results. This allowed us to compare the methods used and which equipment is best suited for a given job.

The first tool that we used was the data collector, the HiPer GPS/ Tesla Handheld System as well as the dual frequency GPS unit. The tripod allows for leveling and allowed for a more accurate point. When taking each point, we averaged about 1 point every 50 feet for this exercise. It was important to listen to the instructor and the Surveyor’s instructions as we went through this process.

To start collecting data, you first start a new “Job.” This can be found by clicking on the “Job” icon and then renaming your file with a name. Next, when selecting “Edit” I manually entered the control point coordinates. In the field, select “Set Up” to orient the instrument. Select “Survey,” then “Topo” to start collection your X, Y, Z points.

The second method that we used was the T TopCon GPT Total Survey Station. This tool had to be set up at a leveled location before the points could be collected. This instrument was mainly used to measure the sloping distance of an object to the instrument, horizontal angles and vertical angles. The Total Station is an electronic/optical instrument used in modern surveying. This tool operates and collects data using an electronic transit theodolite along with an electronic distance meter (EDM). The data collected from this station can then be downloaded to the computer for further processing.

Using the established points provided by the Eau Claire County Surveyor’s Office, we collected the points automatically.  These points are entered into the data collector, and therefore, would be our zero-calibration line at 0⁰0’0’’. When taking a point, the total station then sends out a beam and the prism bounces it back. The instrument then takes the horizontal angle into account, and automatically calculates the correct XY location of the point of interest. The vertical angle is calculated by the data collector and takes a ground elevation point.

Results:

After completing the survey, the points are then processed by the Eau Claire County Surveyor’s Office and sent back to us (as it is their equipment we used). The CSV file with the X, Y, and Z coordinates is then imported into ArcMap using the “Add XY Data” function, converting into a 3D point shapefile.

Next, I created a TIN model in ArcMap and edited the collected points. I then generated 2-ft and 1-ft contours using the TIN and “Contours” tool to create my final result. Within this map, I have also added the surveying points that were taken using both methods.

Discussion:

Through Total Station Surveying methods, there is a relatively easy and quick data collection process involved. When taking the measurements, it was relatively easy and efficient. However, although gathering information proved to be a breeze, the coordinates were gathered calculated on a rectangular grid system, and not taking into consideration the curvature of the Earth. The rectangular coordinates must be transformed into the correct geographic coordinates when projects are larger scale.

Conclusion:

During this experiment we used a few expensive surveying equipment when gathering our data during this survey, however, it’s important to keep in mind that using higher quality equipment doesn’t always mean that there will be no error or malfunction. Equipment that needs to be charged before using, could become useless if the equipment no longer works. In addition, if equipment is slow in the field, it could put a strain on a day in the field when working on a project. Overall, the purpose of this experiment was to familiarize ourselves with using tools in calculating spatial relations with coordinate systems and professional surveying equipment. These methods are useful in accessing accurate and precise data when surveying an area within the field.

Field Activity #3: Conducting a Distance Azimuth Survey

Introduction:

For this experiment, a survey was conducted using the concept behind distance and azimuth. Although this is a basic survey technique, it works in many different situations. In this instance, we used sampling techniques to map point features on the University of Wisconsin Eau Claire campus, specifically trees around Phillips Hall Science Building. With today’s technology and the ability to acquire very precise location data, it is important to understand the basic techniques and tools used in field methods in order to function effectively and independent from advanced technology, such as drones.

For this exercise, a TruPulse laser distance finder was used to record the distance and azimuth readings for the data points we chose. We also used a compass that allowed us to record separate distance and azimuth readings. Although as a class we did not use the compass data, it is important to make comparisons of different technologies. We also used a distance finder or tape measure to measure the distance from our first station to the point feature (trees).

The TruPulse 360B Laser Rangefinder that was used to record our final distance and the azimuth readings for our chosen features (above-right). We also used a compass to compare azimuth readings between the two technologies.

My team and I chose to survey trees within the courtyard and surrounding Phillip Science Hall of UW-Eau Claire. Before conducting our survey, we were advised to take not of the concept of magnetic declination and how it differs from magnetic north. This refers to the magnetic pull on the compass due to earth’s magnetic field at our location on the planet, rather than what is known as “true North.” Below is a link to a video that was shown to the class for more information on magnetic declination:

Luckily, Eau Claire, WI has a small declination value of 2, so we know it would not impact our data collection. We then calculated the point features without using any coordinates, however, we will assign our starting point with GPS coordinates so that the data is usable in GIS systems.

Methods:

October 2, 2019

Cloudy, Rainy, ~ 15 degrees Celsius

Our first survey area was located within the courtyard of Philips hall, facing south towards Putnam Park trail. Once we decided our station area, we took the coordinates of that area, and began surveying from that point. We set up the TruPulse and began by taking readings of trees from right to left. Due to the bad weather that had rolled in that afternoon, our only option was to take points from the exit doors on the northeast side of the courtyard and take our survey from there. There were also heavy winds that would cause the TruPulse to shake, not allowing us to receive valid distance or azimuth readings.

This made collecting data extremely difficult, and due to time constrains, we couldn’t collect as many points as we could have. During this survey, we gathered three features. Our second survey station area was located south of the Phillips building near the bike racks and west of the large sculpture. After collecting data from this area, we had gathered a total of six point features.

After returning to the classroom, we transferred our data into an excel spreadsheet document for later use in ArcMap. Our data is shown below:

Table showing our data collected. For each point, we collected the azimuth, the distance from the station, attribute information of the trees, and the elevation of the tree.

Next, the excel file was imported into the geodatabase where we then used the “Bearing Distance Line tool to convert its information into a line feature class. 

We then converted our data to the points, using the "Feature Vertices to Points" tool.

The results of the tool and our data is shown below.

Results:

Discussion:

When looking at our map and data, it doesn’t seem like there is any input error, but that the readings from the TruPulse were off, causing our data to shift. It seems that even the smallest amount of error when calculating the azimuth will impact the resulting points. It is also possible that errors occurred when measuring subsequent readings of distance (meters) of the prominent features, especially considering the weather conditions throughout the course of this survey.

In the map above, the laser’s accuracy seems to decline as the object is farther and farther away from their actual locations. This error comes from the TruPulse’s decreasing accuracy with distance, as well as the users’ error as distance increases.

Conclusion:

Although we did use an expensive tool in gathering our data during this survey, using higher quality equipment doesn’t always mean that there will be no error. Utilizing simpler equipment may have been easier. Overall, the purpose of this experiment was to familiarize ourselves with using tools in calculating spatial relations without a coordinate system. These methods are useful in areas where there is no access to advanced global positioning and in situations of technological failure.

Field Activity #2: Visualizing Our Terrain Survey

Visualizations of Our Terrain Survey Data

For this portion of our experiment, we continued working on our survey that we had conducted previously from sand courtyard garden planter boxes to create an elevation surface of terrain. Using ArcGIS/ArcGIS Pro, we studied our data using various raster interpolation methods to interpret the results from our experiment and find a realistic and "best fit" representation of our survey. Raster interpolation tools are used to create a continuous surface by connecting the points, using sampled z-coordinate values, to create a 3-D visual product of our terrain. The various methods used includes: IDW (Inverse Distance Weighted), Natural Neighbors, Kriging, Spline, and TIN (Triangular Irregular Networks). After experimenting with our previous data using these tools, we observed where data should be added in order to accommodate the interpolation method of our choice, and re-sampled our terrain as a team.

Methods:
September 25th, 2019
Partly Cloudy, about 20 degrees Celsius.

Our first step was to import our first sampled terrain data into ArcMap and create a point feature class from that data in a new geodatabase. Once the data was imported, we started to experiment with our data using multiple different interpolation methods, and different parameters within those methods. We decided to use a contrasting color in order to visually see a difference in the elevation of the data, as seen below. For each of the methods used, we created 2-D imagery in ArcMap before importing the shape file into ArcScene for a 3-D visual representation. 

After experimenting with the data using these tools, we observed where data should be collected in order to accommodate the interpolation method our group choose. After re-surveying our terrain, we tested each interpolation method, as shown below using ArcScene, on our second time surveying our miniature terrain. 

Inverse distance weighted (IDW) determines the cell values using a linearly weighted combination of a set of sample points. The weight is a function of inverse distance and the surface being interpolated should be that of a locationally depended variable.

Natural neighbor finds the closest subset of input samples to a query point and applies the weights to them based on proportionate areas to interpolate a value.

Kriging is a tool that assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. This tool fits a mathematical function to a specified number or all points to determine the output value of each location.

Spline is a tool that interpolates a raster surface from points using a two-dimensional minimum curvature spline technique. The resulting surface passes through the input points exactly.

Triangular irregular networks is a tool that triangulates a set of vertices. The vertices are then connected with a series of edges to form a network of triangles.

After experimenting with various interpolation methods with our new data, we decided to focus on the Spline method. We decided to add dramatic relief changes - on the valley, hill, and ridge. This involved some conceptualizing, as we had to rethink our data, our survey, and how to accurately represent our new model.

Discussion

Similar to the last exercise, it was very insightful as it displayed the multiple ways to approach looking at a terrain three-dimensionally. It was again challenging for our group, as we were required to decide a precise representation of our model between the various interpolation methods - we chose the Spline method. This method provides a nice, smooth curvature to the areas with low and high relief and still maintains the original values of the data. Previously, there was a larger plain, and less drastic relief, but with this new model, there is more variation in elevation and is ascetically pleasing to the eye - less negative space.

Conclusion:
This exercise explores situations in field analysis where it helps us to find new methods in expanding our research, and revisiting our data. This is something that is extremely important in field work, and when conducting a survey or research project for a large company. Overall, when revisiting data and modeling, it is important to discover error and consider new methodologies that would prove useful in expanding our research. 

Field Activity #1: Collecting Data

Elevation Surface Model Creation

For this experiment, a topographic model was constructed from sand in courtyard garden planter boxes to create an elevation surface of terrain in order to practice collecting, analyze, and interpret geographical information. This simulation explores situations in the field where there are multiple ways to approach conducting a terrain analysis and challenges our critical thinking skills. Using grid lines created by string and pins, measurements were taken using a meter stick and a clipboard for recorded measurements. Through the data collected, a map will later be developed showing the topography of the surface created using a selected coordinate system in later exercises.

Methods:

September 18^th, 2019

Sunny, about 21 degrees Celsius.

Firstly, the class was divided into two groups and discussed ideas of how to create a possible terrain. Afterwards, we headed out to the courtyard garden planter boxes filled with sand to build our terrain. Using spoons and our hands, we created a variety of terrain including a ridge, hill, depression, valley, and plain.

After our terrain was complete, we constructed a coordinate system using string and thumb tacks. The thumb tacks were displaced every 6cm around the rim of the box. We then stretched the yarn around the pins to successfully create our coordinate system.

Next, one group member collected samples in centimeters using a meter stick within each 6cm x 6cm box, producing a grand total of 200 samples. Lastly, after cleaning up from our experiment, the member who recorded the measurements recited them aloud while another typed the data into an excel document, which was later saved as a .csv for later experiments.

Discussion:

The simulation created during this exercise was very insightful, because it showed that there were many different ways to conduct this experiment. It helped develop critical thinking and teamwork skills, something that is parallel to full scale field experiments. We also faced some challenges, such as multiple people recording measurements. Consistency is important in professional experiments and is something to take note of in our error analysis. To decrease the risk of error in our experiment and in future experiments, we found that one member of the group should be taking the measurements the whole time.

Conclusion:

This exercise explores situations in the field where there are multiple ways to approach conducting a terrain analysis. This experiment overall challenges our critical thinking and teamwork skills. Personally, I found that when conducting an experiment in the field, it is important to plan out the methodology of the experiment before heading into the field.