UAS Data Processing with GCPs

Introduction
The purpose of this activity was to illustrate the importance of ground control points (GCPs) in processing UAS imagery and creating accurate digital models of the Earth's surface.  The data used for this project was the same as the previous activity, only GCPs were used in the data processing to create a result that is accurate and represents the true elevation of the area surveyed.

GCPs are points on the ground used to "tie down" an image to the representation of Earth's surface, either based on a geoid or an ellipsoid.  They are used to georeference images so that the locations on the images can be accurately located on the digital representative surface.  Sixteen GCPs were used when surveying the Litchfield mine.  They were spread out throughout the area of the mine so that when the resulting data would be processed distortion could be minimized.  Because the processing done in the last activity was done using no GCPs, the elevation was based on a ellipsoid, which is the default for DJI and other surveying platforms.  The GCPs allow the elevation data to be processed using a geoid, based on sea level, which is the desired result.

The study area is the same as the previous lab, the Litchfield Mine near Eau Claire, Wisconsin.


Methods
Basically the same process was used in this lab as the previous one, the only difference being the use of GCPs in the data processing.

The first step was to save the project after initial processing from the previous lab with a new name in a new folder.  Then the GCP manager was opened in the project tab, as shown in Figure 1.
 

Figure 1: The GCP manager is shown where the GCPs were to be imported.

Next the GCPs were imported.  Then using the basic editor, images were clicked for each GCP to assign them with a cross, giving each GCP one or two crosses, as shown in Figure 2.

Figure 2: The GCPs are clicked to their location by zooming into the cross of each GCP in the images. 

It was then reoptimized.  The Raycloud Editor was then used to improve the accuracy of each GCP, as shown in Figure 3.

Figure 3: The lower right corner shows the images with the GCPs where the crosses showing their exact location on the image can be adjusted to exactly where they should be, at the cross hairs of the square GCP.  

It was then reoptimized again.  Then the processing was run for 2. Point Cloud and Mesh and 3. DSM and Orthomosaic. Figures 4 and 5 show the quality reports for these processes.

Figure 4: The quality report from part 2. in processing.  

Figure 5: The quality report from part 3. in processing.  

The arcviewer was a viewing option to show where each GCP was located on the surface model.  Figure 6 shows this.

Figure 6: Arcviewer displays the surface model in 3D with the location of each GCP throughout the mine.  

Now that the processing was completed, the DSM and mosaic images were brought into ArcMap to create maps of the outputs.

Results

Figure 7: This map shows the DSM created using GCPs from the UAS imagery.  When comparing this to the image/map created with the same process but no GCP's, the main difference is the difference in elevation values.  This map shows the correct elevation between approximately 225 meters and 250 meters.  The previous map had a low of about 80 meters and a high of about 105 meters.  Because this map used GCPs it was able to use the correct elevation.  This is easily notable because the Eau Claire area lies in general between 700ft and 800ft, not around 300ft like the previous map showed.  The use of GCPs also allowed for a more accurate depiction of elevation change within the mine.  When calculating the difference of the map highs and lows for each map, the difference in elevation on map with GCPs is 23.708 meters, and the difference in elevation on map without GCP's is 24.1971.  When surveying/modeling an area and using volumetrics even one foot can mean a difference in quite a lot of material, so using GCPs ensures much more precision and is the more trusted method.  

Figure 8: This map shows the orthomosaic using the GCPs from the UAS imagery of the mine.  When comparing this to the map using the same process but without the GCPs, it is hard to notice much of a difference.  Though there are very slight differences observed when looking at this scale, it ends up looking very similar.  The knowledge that this image/map used GCPs and acquired the correct elevation makes this the desired result of the processing.  

Conclusion
The results of the processing with GCPs doesn't look too much different than the results from the previous lab, however when looking at the legend of each map one can observe the difference by over one hundred meters is very important.  Using GCPs helps to acquire not only the correct height above sea level for the entire orthomosaic image, but it also helps to create a more accurate model that shows the terrain and change in elevation throughout the mine. 

UAS Data Processing With No GCPs

Introduction/Background Information
On September 9th, 2017 imagery was gathered using the DJI Phantom 4, a drone platform with 13.3MP RGB sensor with a rolling shutter.  The purpose of this activity was to process this data using PIX4D and create usable maps including the elevation data. PIX4D is the current and premier advanced photogrammetry software that uses images to create professional orthomosaics, point clouds, 3D models and more.

Link to the Software Manual:

https://s3.amazonaws.com/mics.pix4d.com/manual_pdf/Pix4Ddesktop_Manual_3.0_October_2016.pdf 

The following questions were addressed to learn more about the software manual for PIX4D:

o Look at Step 1 (before starting a project). What is the overlap needed for Pix4D to process imagery?
     -The overlap depends on factors like the speed of the UAV/Plane, the GSD, and the pixel resolution of the camera.  The example in the manual uses 75% overlap.

o What if the user is flying over sand/snow, or uniform fields?
     -Areas that are sandy or snowy that are more uniform in look will need more overlap.  The manual recommends at least 85% frontal overlap and at least 70% side overlap.

o What is Rapid Check?
     -Rapic Check is an alternative process that is faster but produces lower resolution results in order to check if coverage was obtained.

o Can Pix4D process multiple flights? What does the pilot need to maintain if so?
     -Yes, multiple flights can be merged together, but each has to have the same horizontal and vertical coordinate systems.  Having enough overlap and similar weather conditions are also important when processing multiple flights.

o Can Pix4D process oblique images? What type of data do you need if so?
     -Yes, but the data needs to be tied down with GCPs, and it works better when the terrain is not flat.

o Are GCPs necessary for Pix4D? When are they highly recommended?
     -They are not necessary, but they are highly recommended to improve the georeference and accuracy of the reconstruction.

o What is the quality report?
     -The quality report documents the details of the results from the processing.

*This activity was used as an example of mapping data that contains errors.  Because GCPs were not used to tie down these images during the processing, the elevation is wrong.

Study Area
The study area is the Litchfield Mine, near Eau Claire, Wisconsin, as shown in Figure 1.  This is the same site as the activity earlier in the blog.

Figure 1: The study area is the Litchfield Mine outside of Eau Claire, Wisconsin.  

Methods
First a new project was created in PIX4D.  The project was named based on the date, site, platform type, and altitude.

Then the images were added to the project.  It was important to examine the camera properties and fix any incorrect information, because some of the settings were not correct.  Figure 2 shows the processing options that can be altered even after they are originally set, but before the processing has begun.

Figure 2: This image shows the Processing Options window where settings can be altered.  

The rest of the defaults were left as they were, and the initial processing was started.  Figure 3 shows the processes available.

Figure 3: The processes available to be run.  They can be checked and then started either one by one, or all at once.  

First the Initial Processing was completed, to ensure that coverage of the area was correct.  Then 2. Point Cloud and Mesh and 3. DSM, Orthomosaic and Index were run.  A quality report is produced to show the details of the process, as shown in Figures 4 and 5.  Overall, the processing took about 2.5 hours to run.

Figure 4: The quality report that was produced from the processing.  

Figure 5: These are some of the details shown and explained in the quality report.  

The results could then be viewed in several ways.  Some of the cameras were shown as red, meaning they were not processed correctly due to any number of reasons.  But because they are located in forested areas, which are of no interest to the project they can be clipped out.  A polygon can be drawn around the area of interest so that the rest of the area that was captured in the flight that is not necessary does not get included in the project.  Figure 6 shows the newly created surface being viewed in the Ray Cloud viewer, which is 3D.

Figure 6: The reconstructed surface being viewed in the Ray Cloud viewer.  The objects floating above the surface are the camera images, which can be turned off.  

This view can be used to really examine the surface and see the quality of the imagery and the reconstruction in 3D.

Now that the data had been processed in PIX4D it was time to be brought into ArcMap to create some maps that can help display some of the elevation data.  The DSM and Orthomosaic image were brought into ArcMap.  A hillshade was given to the second DSM map to better represent the elevation differences in the terrain.  The data was assigned the WGS84 coordinate system, and some maps were given a basemap to give them some context.

Results

Figure 7: This first map shows the DSM from the processed data.  It shows the elevation of that surface in meters.  This is where the error in elevation can be observed, because the actual values should be higher.  The WGS 84 coordinate system did work in georeferencing the surface in the correct location, because it does match up quite well with the imagery basemap.  

Figure 8: This map shows the same DSM map, only with a hillshade added.  It really shows the terrain better in regards to slope and texture of the surface.  

Figure 9: This map shows the orthomosaic that was a result of the processing in PIX4D.  It actually looked quite similar to the imagery basemap from ESRI.  The surface looks quite realistic from this scale, so it seems the drone did capture some valuable images after all.  The resolution does have its limit however, because when zooming in and examining in the 3D Ray Cloud viewer some of the objects on the surface are slightly distorted.  Things like cars and people are not perfect, but it does a very good job at reconstructing the surfaces to a decent enough degree that can model the things intended for the project.  

Conclusion
Overall this was a good introduction to the current premier photogrammetry processing software for UAS data.  It showed that remote sensing can be quite valuable for acquiring highly detailed imagery and producing a 2D or 3D model from the data.  When working with this data in the next project and actually using GCPs it will be enlightening to see how these models are not actually as accurate as one might think by just glancing at it. 

Sandbox Visualization

Introduction
The purpose of this lab was to follow up with the sandbox activity done earlier in the semester.  A digital surface was to be created of the sandbox terrain pictured and measured in the first part of the activity.

In the previous section, the sandbox terrain contained in roughly a square meter was shaped then measured on a grid of points from a "sea level", or a flat surface height from which all points were measured from above.  Because the points were all measured below the sea level, each point has a negative value.  The points with x, y, and z coordinates were recorded and put into an excel spreadsheet, mocking location in the sandbox.  The actual terrain of the sandbox is shown in Figure 1.

Figure 1: This picture shows the sandbox with the shape of the terrain and the sea level being set above the surface.  

Key Term:
Data Normalization: the process of organizing rows and columns of a database in a way that supports how that data will be used/displayed.
-In the case of this activity, the data had to be normalized from the previous formatting into a way that could be brought into ArcMap and easily understood and mapped.

Interpolation: a tool can be used to predict values in between points or cells in a raster.  It is used for continuous surfaces containing values for things like elevation, rainfall, temperature, etc.
-Five interpolation methods were used in this activity.

• IDW 
• Kriging 
• Natural Neighbor 
• Spline 
• TIN 

Each uses different equations/methods that result in a predicted surface, but only one was chosen as the one that best represented the actual sandbox terrain surface.


Methods
The first step was to create a personal folder for this project within the class folder, as well as a geodatabase for all the results produced throughout the project.  Figure 2 shows the geodatabase in ArcMap.

Figure 2: A screenshot of the geodatabase created for this project.  

Next the data in the original excel file had to be normalized from the mock map of the terrain into three columns: x, y, and z.  This was done so that ArcMap could easily recognize how the sheet and map it accordingly.

Figure 3: This screenshot shows how the data was originally organized in Excel.  

Figure 4: This screenshot shows the data after being normalized for this project into x, y, and z values.  

"Add XY Data" was used to bring points into ArcMap.  Once this was done it was converted into a point feature class.  When this was done a coordinate system was asked to be assigned, but this was not necessary because the survey was of such a small area and of such accuracy that it was not significant enough for a coordinate system to be important.



Next a continuous surface was created for each of the five interpolation methods.  The explanations, pros and cons of each of these techniques is listed below.  (information from ArcGIS.com)

http://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/how-idw-works.htm 

IDW (Inverse Distance Weight) - determines cell values using a linearly weighted combination of a set of sample points. The weight is a function of inverse distance. The surface being interpolated should be that of a locationally dependent variable.
pros: -when samples are densely located the surface will be very accurate
cons: -it can not show the highest peaks or lowest valleys if those values are not already sampled.

Kriging - a geostatistical model based on autocorrelation, the statistical relationships among the measured points.
pros: -has the ability to estimate the level of certainty or accuracy of the predicted surface.
cons: -extensive and often not necessary if there is no spatially correlated distance or directional bias in the data.

Natural Neighbor - finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas to interpolate a value (Sibson 1981).
pros: -simplest method and creates smooth surface.
cons: -only uses the closest points to estimate surface.  It does not infer peaks, low points, or trends.

Spline - estimates values using a mathematical function that minimizes overall surface curvature.
pros: -results in a smooth surface that passes exactly through the input points and good for gently sloping surfaces.
cons: -does not accurately represent surfaces that are naturally sharp, steep, or have a lot of variance in close proximity.

TIN - TINs are a form of vector-based digital geographic data and are constructed by triangulating a set of vertices (points).  The input features used to create a TIN remain in the same position as the nodes or edges in the TIN.
pros: -allows preservation of data points while modeling the values between points.  This model also takes very little storage space.
cons: -not a smooth surface, very sharp jagged.

Each tool was searched using the search bar and the spatial analyst of each tool was chosen to run the method, as shown in Figure 5.

Figure 5: The Search bar was used to find each of the interpolation tools, and the spatial analyst tool was chosen to run each process.  

Each of the surfaces created from the five methods were then brought into ArcScene to be displayed in 3D.  They were then exported back into ArcMap to be displayed next to the 2D surfaces.  Maps were created to show both the 2D and 3D surface models of the sandbox terrain.

Results

Figure 6: This map shows the resulting IDW map.  The result obviously displays exactly where many of the data points are located.  Each of those indentations or bumps makes the surface look unrealistic.  

Figure 7: This is the resulting surfaces map from the kriging tool.  It has a nice smooth surface, but it seems a bit too generalized when compared to the real sandbox terrain and the rest of the surfaces from other tools.  

Figure 8: This is the resulting map from the natural neighbor tool.  It actually does a very good job at estimating the surface.  It is smooth yet it does not over-generalize the details of the terrain like the slopes, high points and low points.  

Figure 9: This is the resulting map from the spline tool.  This tool did a great job and arguably best represents the actual terrain of the sandbox.  It does a good job of making the surface smooth, yet does not over-generalize the actual points just to make that surface smooth.  It seems just a little more realistic than the natural neighbor surface.  

Figure 10: This is the resulting map from the creation of the TIN.  It is observably blocky but it still produces a decent shape of the real terrain.  It shows where there may be the steepest slopes as well as high points and low points.  

Conclusion
This activity served as just a smaller version of a real survey done on a larger scale of real world surfaces/features.  There was a grid of recorded points with x, y, and z coordinates that were used to create a 3D model.  That model could be used to measure volumes and distances, create paths, or study observable patterns.  Grid based models like this however may not always be the answer because differences in various landscapes will demand specific concentrations of recorded points with x, y, and z values.

Interpolation methods can be used for things other than elevation.  It is commonly used for creating temperature, wind, and other weather maps.  It can be used for basically any sort of location based recorded data that needs gaps filled in between points to make a continuous (and sometimes smooth) estimated surface.


References
-ArcGISOnline.com
-Professor Joseph Hupy

ArcCollector Part 2 - Project

Introduction
The purpose of the project was to create a web map and series of maps to answer some type of research question.  The topic/focus of this project was measuring plant health at a local berry farm based on plant type, plant age, and plant height.  This type of project is a model of a type of work that can be done by setting up a database with ArcMap, using the ArcCollector app on a cell phone with GPS to collect data in the field, and making static maps and web maps as an end product to display the results. There is major emphasis on proper project design in this activity to demonstrate how important it is to set up a project that is easy for the collector to use.  If the project is set up thoughtfully and intuitively, the person collecting the data should have an easy time in the field collecting data and creating maps from the results.

The goal was to create a valuable end product that the farmer could use to keep track of which fields were planted well, as well as track plant height, which could be a factor in plant health.

Study Area
The study area is Little Berry Farm.  It is located roughly 4 miles south of the city of Eau Claire off Highway F.  Figure 1 shows the study area.

Figure 1: The Study Area Map showing Little Berry Farm and the surrounding areas.  

Methods
An ArcGIS Online tutorial ( http://doc.arcgis.com/en/collector/ ) was followed to learn how to set up the geodatabase necessary to create the maps.  This tutorial outlined the steps used in setting up the geodatabase and creating the web map.

First the database must be created using ArcCatalog.  It was created in the personal student folder for the class and then assigned appropriate domains, as shown in Figure 2.

Figure 2: The three domains were Plant Age, Plant Height, and Plant Type.  These domains were created so that when features are created they have these fields to fill in data for.  They specify characteristics about the feature and often have a list of available options to choose from for each category.  

Next a feature class is created with a name describing what will be included within it, as shown in Figure 3.  

A polygon feature was used because cell phone GPS is not currently accurate enough to measure specific plants.  Rather the plants were grouped into fields based on the type of berry and when they were planted.

Figure 3: The new feature class was created and named Plant Information.  

Next a coordinate system was chosen for the database, as shown in Figure 4.

Figure 4: The WGS 1984 Web Mercator (auxiliary sphere) coordinate system was chosen for the geodatabase because it is compatible with web maps.  

Next the corresponding fields were set up for the feature class based on the domains already created for the geodatabase.  Because data would be brought back into ArcMap later for creating the desired maps, the symbology and display details could be manipulated later.  The map was then published as a service through ArcGIS Online and shared with the class as well as the UWEC Geography department.  Capabilities and Feature Access were altered to allow the users collecting the data with the app to edit the features during and after they are recorded.

Next ArcGIS Online was used to add a basemap and the layer that was published and shared to the map.  One feature was created as a test to make sure that the correct fields showed up to be filled in.  That feature could later be deleted.  

The next step was to sign in on the app on the cell phone to download the map.  A workspace was then defined by zooming in to the farm, as shown in Figure 5.  

Figure 5: A workspace around Little Berry Farm was defined by zooming into it.  Some room was left around the farm in case the accuracy of the GPS left the boundaries of the actual fields.  

 Now that the geodatabase, web map, and basemap within the cell phone ArcCollector app were all set up, the next step was to go collect the data.

Upon arriving at the farm, it was clear that the basemap imagery was taken before the fields of the current berry farm had been planted, so the features on the maps do not exactly align with the previous farm fields.  At the Northeast corner of each field, a picture was taken looking at the row/field that was to be mapped, as shown in Figure 6.

Figure 6: The raspberry row is shown here as an example of every picture that was taken of each field mapped throughout the project.  The pictures to each corresponding field can be seen using the web map in the results section.  

Each picture was attached to each feature using the paperclip with a plus sign symbol shown near the bottom of Figure 7.  Figures 7 and 8 both show how the mapping looks during collection.  A point is recorded every 5 seconds, and a polygon is created from the string of points until the field is fully traced.

Figure 7: This image shows the points being recorded of the raspberry row.  

Figure 8: This image shows the polygon feature that resulted from walking around the raspberry row.  

The rest of the fields of interest were then mapped using the same procedure, filling in the information for each feature as shown in Figure 9.

Figure 9: The attributes are filled into this form on the ArcCollector app. 

Once the collecting was complete and wifi could be attained once again, the map was synced so that the information collected could be updated online, on the web map.  This is shown in Figure 10.

Figure 10: This cell phone screenshot shows the map being synchronized to the online map.  

Now that the data had been collected, the last step was to open the web map as well as in ArcMap Desktop so that static maps could be created to display each field that was mapped.

Results
The resulting web map can be found by following this link: http://arcg.is/9zK1L.  Within the web map there are pictures attached to view each field from when the data was collected.  It is also useful in that the user can manipulate the map to display different aspects of the data collected for the project. Figures 11, 12, and 13 show the maps corresponding to each data variable collected and mapped.

Figure 11: This is the map showing the fields based on type: Blueberry, Raspberry, and Strawberry.  Some fields are misshaped and overlapping due to the inconsistency of the cell phone GPS.  The blueberry fields and strawberry fields are similar in size because there are many more rows of them, while the raspberry section alone is skinny because it is only one row.  

Figure 12: This map displays the fields based on how many years before this they had been planted.  This map shows the years that each field was planted.  The older fields are darker while the newer fields are lighter in color as to represent younger vegetation.  This factor was chosen as a possible characteristic that could indicate plant health, size, and/or productivity.   

Figure 13: This map shows the average height of plants in each field/section.  The strawberry fields are all roughly 1 foot tall or less on average because they don't grow taller than that and the raspberry row was about 5 feet tall on average.  The height factor of the plants was mostly based on the blueberry fields because there are several of them and much more variance between them.  The middle section of the blueberries is a section of interest because it was planted originally 4 years ago, yet the same height as the section planted only 2 years ago.  This could be useful in indicating plant health or productivity, especially if one section were short in height and also not producing many blueberries during harvest season.  

Conclusion
This activity was a good demonstration of why it is important to design a project properly.  The domains, attributes, fields, etc. all have to be on point in order for things to go smoothly on the collector side of things.  If any of those are incorrect or missing it makes the job of the collector much more difficult.  This activity also taught a good lesson on what to do better in future projects that have a similar concept or end goal.  If this project were to be repeated it would be wise to speak more to the farm owner ahead of time to speculate on ideas for measurable factors that lead to productivity.  It would also be more conducive to the idea of increasing productivity for the farmer to measure/map these things during a harvest season or a time when crop yield could be measured.  This way the plants could be seen when they are most healthy and bearing fruit, rather than being prepared for winter.  This project type could be expanded in countless areas.  It could be beneficial to farmers specifically if they were interested in mapping these types of variables, or to any other type of sector that has characteristics to be measured, collected, and mapped.


References
-ArcGIS Online
-Dr. Joseph Hupy
-Gaye Brunkow (Farm Owner)

ArcCollector Part 1

Introduction
The purpose of this lab was to learn to utilize the ArcCollector app as a means for collecting data in the field and upload them in real time to a single destination where all the rest of the data collected by the class would be brought into a map.  Smartphones are becoming more common as a tool for data collection because they have more computing power than typical GPS units, and they have the ability to access online data.  The data points collected by the class had several attributes and each point was uploaded on the fly.

Study Area
The study area for this activity was mostly on the UW - Eau Claire campus, with a couple sections near Water Street.  The numbers in each section show the designated zones assigned for each student to collect data points, as shown in Figure 1.

Figure 1: The study area on UW - Eau Claire's campus and across the footbridge.  

Methods
First each student had to install the ArcCollector app, as seen in Figure 2.  The app allows you to choose a basemap, use the GPS on a smartphone to find location, and collect data points with multiple attributes that can be uploaded on the fly.

Figure 2: The ArcCollector app as seen on a smartphone.

Each student was then assigned a zone to collect data points within.  Each data point collected had the following fields to be input:
-Temperature (°F) 
-Dew Point
-Wind Chill
-Wind Speed
-Wind Direction
-Time

A Kestrel 3000 Pocket Weather Station (Figure 3) was used to find the weather information, a Suunto compass (Figure 4) was used to determine wind direction, and the phone was used for the time.

Figure 3: The pocket weather station used to collect weather condition data.  

Figure 4: The Suunto compass used to determine wind direction.  

The GPS on the phone and the map on ArcCollector were used to determine location and they helped to ensure data points were spread evenly across the assigned zone.  Over the course of an hour and a half, several data points were collected per zone and uploaded to a single destination where they could then be brought into ArcMap together to be mapped offline.  The resulting maps are displayed in the Results/Discussion section.


Results/Discussion

Figure 5: This map shows the temperatures from the recorded data points. 

By analyzing the temperature map in Figure 5 it can be observed that temperatures on both upper and the main lower campus vary considerably, in a range of about 15 degrees.  One possible reason this could occur could be due to some spots being in the shade and some spots being in the sun.  Another could be differences in the ways in which the students were collecting temperature.  If the pocket weather station was kept in the student's actual pocket before collecting data from that point, the data would be compromised because the temperature would be recorded higher than it actually was at that point and time.  Across the foot bridge the temperatures had a bit less variation, with temps ranging between 49 degrees and 58 degrees.  There does seem to be some observable clustering of temperatures in this area, with similar temps recorded being near other similar temps.

Figure 6: This map shows the dew points from the recorded data points. 

The dew point recordings on the map in Figure 6 seemingly show more patterns than the temperature map.  Dew point measures what temperature the air would have to be to become saturated, so it a measure of temperature and absolute humidity.  Upper campus shows the warmest dew points in general, while the recordings on lower campus and across the river show the colder dew points.  This could be because it is closer to the Chippewa River, the elevation is lower, or there were colder temperature recordings.

Figure 7: This map shows the recorded wind speeds and directions from the recorded data points. 

The map in Figure 7 displays the wind speed and wind direction.  It can be observed that wind directions on upper campus near the Southwestern end seem to mostly come from the Northeast.  Other than that they are varied.  On main lower campus they are widely varied in direction.  It could be speculated that the variance in wind direction recorded on both upper and lower campus are probably due to the fact that there are many large buildings scattered throughout campus, as well as forest.  These features tend to have a large impact on wind speed and direction.  It can also be observed that on the foot bridge and across the river the wind speed is generally much higher and follows a direction pattern.  The recordings along the river all seem to have wind coming from the East/Northeast.  This could likely be explained by the fact that wind can travel more easily over water than land with features like trees and buildings, because there is nothing obstructing it.

Figure 8: This map shows the recorded wind chills from the recorded data points.  

The map in Figure 8 shows wind chill, which is a recording of temperature with wind speed taken as a factor.  It can be observed that in areas on both upper and lower campus that are not near the river, the wild chill tends to be higher.  This is because both the temperatures recorded in these areas are higher and the wind speeds are lower.  Along the river almost all the wind chill recordings were between 36 and 52 degrees.  The variance in wind chill could be very dependent on wind speed variance.  Wind blows in gusts, so at any given time the recording could vary by several miles per hour, which would in turn have a big effect on the wind chill recordings.


Conclusions
This activity demonstrated that effective maps can be created using data collected with the ArcCollector app on a smartphone, a weather conditions collector device, and a compass.  The data was collected simply and quickly.  More precise collecting methods could be used to get a more reliable data outcome, but this activity demonstrated the effectiveness of the app and of a smartphone as a practical data collection device. 

Survey123 for ArcGIS Tutorial

Introduction/Background
The purpose of this activity was to go through the Survey 123 for ArcGIS tutorial to learn how to create a survey, map and analyze the collected data, and share the survey data by creating an app for users' smartphones or tablets.

There were 4 "lessons" within the tutorial:
1. Create a survey
2. Complete and submit the survey
3. Analyze survey data
4. Share the survey data

The survey app was explored thoroughly throughout the tutorial.  Screenshots were taken throughout the tutorial on both the desktop and the smartphone to document the entire process.  This is the link to the tutorial on the ArcGIS website: https://learn.arcgis.com/en/gallery/


Steps
1: Creation of a survey
A survey was created to help the homeowner association (HOA) assess their community members' disaster preparedness for earthquakes and home fires.  Figure 1 shows the first step in creating the survey: adding the name, tags, and summary.

Figure 1: Creation of the survey. 

Next, questions about the participant and their residence were entered into the survey using the design tab.  This is shown in Figures 2-4 show these steps.

Figure 2: The survey completion date was the first question added.  

Figure 3: Rules were set for some questions when specification was necessary.  

Figure 4: This screenshot shows the final questions entered.  There were 29 questions total.  

The survey was then published.  This enabled me to preview the survey to double-check that everything was entered correctly and navigate it how a smartphone or a tablet would view it (Figure 5).

Figure 5: This shows the preview of the survey in smartphone mode.  

2: Complete and submit the survey
Now that the survey was published the settings can be altered to decide who to share it with.  The survey was then opened in a web browser to test it out.  Figure 6 shows the completion of the survey as a test.

Figure 6: This notification showed that the survey was completed as a test.  

The Survey123 for ArcGIS app was then installed onto my smartphone, as shown in Figure 7.

Figure 7: This screenshot from my Android smartphone shows the Survey123 for ArcGIS app in the Google Store.  

Once the app was opened and I signed in using the UWEC enterprise account, the survey was downloaded to the phone in "My Surveys", as seen in Figures 8 and 9.

Figure 8: The surveys available for download are shown here.  

Figure 9: The survey is downloaded into My Surveys.

The survey was then completed to test it out using the app on a smartphone this time.  The survey was then completed several more times to collect enough data to be analyzed.  Figure 10 shows each survey that was completed.  The information in each survey was fake but varied just for the purpose of testing out the app and having enough data to be analyzed.

Figure 10: Each survey completed on this smartphone is listed here.  

3: Analyze survey data
The Survey123 website was then visited and its reporting capabilities were explored.  It gave detailed reports that could be analyzed in several ways.  The data collected from each survey is compiled here, and the interactive reports were also explored.  Figure 11 shows one of the tabs that shows statistics from the data collected in the survey and Figure 12 shows a map with the data.

Figure 11: This screenshot shows the overview statistics from the survey data.  

Figure 12: This screenshot shows the map from the Survey123 for ArcGIS website that displays various results from the survey.  

4: Share your survey data
The final step in the tutorial was to create a customized map with only specific fields included, as seen in Figure 13. Once the map was saved, a web app was created using the share button on the interactive map.  The interactive map was then finished and available for users.

Figure 13: This shows the final web app created throughout the tutorial. 

The map shows three different locations in which I determined where my residence was located.  Because almost all of the information used when taking the survey several times was false/fake, the patterns are random as far as the results in each field.  However this map is especially helpful because it is interactive.  Users can navigate through the map, between location points, and they can observe the results from each individual survey if they want to. 

Conclusion 
Survey123 for ArcGIS could be quite useful in future research, projects, or field work.  It has a relatively simple interface which allows for easy creation of surveys that can be mapped.  It is also nice that it has an app that can be used on smartphones and tablets.  Users can complete the survey and include their location, and the resulting data can be analyzed using the website. 

ArcGIS website citation:
“Lesson Gallery.” Lesson Gallery | Learn ArcGIS, learn.arcgis.com/en/gallery/.