Priory Navigation Exercise

For this assignment, the class headed over to The Priory in Eau Claire, Wisconsin and prepared to navigate the wooded area to find the location of a total of five trees. Once there, students were split into groups of three and given the UTM coordinates of the five tree locations. Additionally, each group was provided a can of spray paint and ribbon to mark their trees once found, and a tracker so that Professor Hupy would be able to review the group's course later on, and confirm that the trees in which they selected were marked correctly. Using a previously made map and handheld GPS device, the students began their descent into the forest, and began locating and making their designated trees. Figure 1 below shows a student marking a located tree with ribbon.

Figure 1: Marking a tree with ribbon

The terrain in the wooded area had extreme ranges in elevation at different various points. In some instances, it was more beneficial to walk around the ridges rather than attempting to cut through them, especially with the ground being wet and slippery from rain, which continued throughout the navigation excursion. The combined cloud and tree coverage made the GPS units lag quite a bit when travelling from site to site. Also, GPS units from the phone app synced to the tracking device and the handheld GPS device often did not match up. For these reasons, it was often difficult to pin-point the exact location of the tree in question. Professor Hupy advised that a 10 meter buffer from the exact point location would suffice for the purposes of the exercise.

The excursion forced students to get lost and rely on their mapping and location skills to return back to the original meeting point. Overall, the exercise was a stimulating challenge that allowed students to do some hiking and exploring in the backyards of Eau Claire.

Community Garden Sampling and Drone Work

Introduction:

This assignment aimed to collect surveyed data of a community garden located in Eau Claire, Wisconsin while simultaneously introducing students to a variety of new data collection tools. Tools utilized for collecting data included a survey grade GPS, thermometer, pH reader, and a TDR Probe for measuring volumetric water content within soils. Later, an additional drone survey of the landscape would contribute to the overall collected data. 

 Methods:

For the purposes of this lab, the class met at the garden location on April 26th and collaborated as a single, large scale group to collect the various types of data necessary within the community garden. Sub-groups of students within the class were created so that each group was responsible for a single data collection tool. Groups were encouraged to switch tools with other groups throughout so that each sub-team could become familiar with all of the data collection methods being utilized. 

The first data collection tool utilized in the community garden survey was a Dual Frequency Survey Grade GPS shown in Figure 1 below for assessing the very specific locations where data was being collected. Each of the points were marked by orange flags. The device surveyed a total of 30 data location points for the first 10 data points collected, but was shortened to 10 data points per stop after survey point 10 to speed up the collection process.

Figure 1: Using the Dual Frequency Survey Grade GPS Unit

Figure 2: pH Measuring Tool

The next tool was a pH probe that worked to measure the acidity levels found within the soil points surveyed.  A sample of soil from each of the data points was scooped into a container and diluted with water so that the probe could be inserted into the container to measure the pH levels found in the soils in that area. Figure 2 provides an image of the tool while Figures 3 and 4 show a peer collecting the necessary data. 

Figure 3: Reading Soil Content pH levels

Figure 4: Diluting the Soil with Water

Figure 5: TDR Probe Measuring Water Content in Soils

The third tool used for data collection was the Time-Domain Reflectometry (TDR) Probe, which sent out electrical pulses into the soil structure to measure each collection point's volumetric water content as a percentage. A picture of this tool is provided in Figure 5. 

The last group used a standard thermometer to measure the temperature of the soil at each of the marked survey points. Given the common use of the tool, a photo is not provided. 

A combination of the resulting data collected was entered in to the survey grade GPS at each of the surveyed points, storing the field data into its attribute table. Once all points were collected with measurements using each of these tools, the data is ready to be viewed and manipulated in ArcGIS. Since the project was a class collaboration, Professor Hupy offered to combine the data into a single spreadsheet for use by the rest of the class. 

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Figure 6: Ground Control Point

On day two of the project, students prepared the area for a flyover mission using a M600 UAS drone. Student's laid out the route of the drones mission by setting up ground control points (GCP) and collecting their x,y location data for input into the drone's course. A sample ground control point is pictured in Figure 6, and the collection of its specific location data using same survey grade GPS unit as was used in part one of the lab can be seen collecting its location point in Figure 7 below.

Figure 7: Survey Grade GPS marking the location of the GCP

With the GCP's laid out and mapped, the drone was ready for its flyover mission, taking pictures and collecting data of the surveyed area. Figures 8 and 9 show pictures of the M600 UAS drone used in flight, price tagged at $13,000. Once the flyover run is complete, to location points collected at each of the GCPs will be used when imported into ArcGIS to tie down the photos taken by the drone into a cohesive mosaic of the surveyed area.

Figure 8: M600 Drone 

Figure 9: Professor Hupy seen Troubleshooting

Finally, using the outputs of the data collected by the tools detailed in part one of the assignment in combination with the resulting final mosaic of the landscape surveyed in part two, a variety of maps can be made to symbolize the significance in the collected data.

Results:

The first of a total of five maps generated from the collected data was a locator map of the Community Garden Project within Eau Claire (Figure 10). Points where the data was collected within the garden are marked in yellow, paired with a base map at the bottom most layer, overlaid by the resulting mosaic generated in the flyover run to provide an updated image of what the site looked like at the time of data collection. While there may be a few feet in error to account for between the mosaic and the underlain base map, the two files appear to align fairly well.

Figure 10: Locator map of the community garden project location

Next, a series of four interpolation maps could be generated to showcase the distribution of the data collected at the various point locations. The first data symbolized was the elevation of the sites location, as shown in the map in Figure 11. The elevation of the sight was relatively flat, ranging only by about 1 meter in elevation between any two given points. The patterns in elevation tended to decrease slightly to the East, which could suggest that the watershed patterns would end up trailing to the East as well.

Figure 11: Site Elevation Interpolation Map

The next set of collected data accounted for the ground temperature of the analyzed soils. The map in Figure 12 shows cooler temperatures recorded at the western side of the garden and slightly warmer temperatures on the eastern half. Temperatures ranged between 11.6 and 13.1 degrees Celcius, fluxuating only by 1.5 degrees overall.

Figure 12: Interpolation map of ground temperatures at site location

Next, moisture content data was interpolated for analysis, resulting in the map in Figure 13. As predicted by the elevation map, the percentage of water content is lower to the northwest, and higher to the southeast, most likely following the areas larger scale drainage basin. Moisture content in the soil tended to have the widest values range of all the explored variables, ranging up to a 13% difference between collected point data!

Figure 13: Map of soils moisture content in site area.

Finally, the last map in Figure 14 accounted for the pH Levels read by the probes in the soil content. In this map, water appears more acidic in the northwest, and more basic in the southeast. This could have implications on plant growth in these areas, as optimal pH levels for plant growth average at a pH level of 6.5. The higher levels accounted for in the southeast portion of the garden could threaten the plants attempting to grow in this area.

Figure 14: pH Levels of Soil Content in Site Location

Conclusion:

This project worked to introduce students to a variety of new tools that can be used for data collection while out in the field. Upon conclusion of this project, students gained experience working with the following field data collection tools: survey grade GPS, thermometer, pH reader, and a TDR Probe. It exposed students to the benefits of drone use in the field and how data can later be compiled to reflect various details in a given area.

ArcCollector: Sunday Church Parking around Randall Park

Introduction:

This lab works as an extension to the previous lab on the class collective ArcCollector project. In this lab, students create their own ArcCollector mapping projects and experiment with the use of domains while building individual databases which will host the collected point feature class data of their choice. 

The project which was ultimately decided upon for field study was the influence that Sunday morning church mass has on parking in the Randall Park area, as seen in Figure 1. The park area is host of two separate church affiliations with First Baptist Church located on the corner of Fourth and Niagara Streets, and First Congressional Church located on the opposite corner of Third and Broadway Street. Both churches hold regular Sunday Mass sessions at 9:00 am and 10:30 am, making parking impossible to find in these hours. By noon, however, parking density usually subsides to its regular patterns. This lab attempts to capture the congestion of parking on the Park block as it occurs regularly on Sunday mornings, and attempts to capture the distribution of cars parking locations in relation to their corresponding church affiliation. 

Figure 1: Map of the Study Area

Methods:

Before any data could be collected from the field, students first had to set up their geodatabases to host the data points. After creating a new file geodatabase in ArcCatalog, students moved to ArcMap to set the geodatabase domain properties. Domains are set to restrict the entry options available for input in the field as an attempt to reduce data variations and user error. Domains also make it easier to classify the data during later use in querying and symbolizing specific attribute details. For the purpose of the Randall Park Project, attributes set included Church Affiliation, Notes, Streets, Time, and Vehicle Type. Most of the domains used coded text values. "Time" was the only exception, this option using short integer values. Options for Church Affiliation included First Baptist, United Methodist, First Congressional, Unknown, or None. Options for Vehicle Type were limited to Car, Truck, Van, or SUV.

Figure 2: Database Domain Properties

The next step required the creation of a point feature class, in this instance named 'parkedvehicles.' The Projected Coordinate System used was the WGS 1984 Web Mercator (auxiliary sphere) to account for the latitude and longitudinal point location readings in the field. A field was created within the point feature class relating to each of the domains listed in the figure above. Finally, the geodatabase was prepared to be uploaded to ESRI's ArcGIS online platform for connection to the ArcCollector App, and the mapped data could be accessed in the field.

Field data was collected on three separate hours to account for the parked car traffic around the Park's block. These time slots included 9:00 am, 11:00 am, (to account for the 9;00 am and 10:30 am masses) and 12:00 pm (to account for normal distribution of parking traffic). The results are provided in the next section.

Results:

The embedded map linked below in Figure 3 shows the overall distribution of points collected around the Randall Park area over the course of the three observed hours. 

                   Figure 3: Randall Park Distribution of Parked Cars



Figure 4 shows the distribution of cars and their corresponding church affiliations at the three different recorded hours. The maps provided show clustering around the Park block with parking on both sides of the street for the first two collections, while the last map shows an example of what regular street parking looks like around this block. It appears that both the 9:00 am and 10:30 am mass sessions are equally popular in terms of attendance. While it was difficult to track where every car's passengers diverted to after parking, it seems that most of First Baptist's attendees tended to park on Fourth and Niagara Streets while First Congressional attendee's occupied Broadway and Third Street.

Figure 4: Map of Collected Church Parking Data

Conclusion: 

The results from the collection of this data shows the congestion in parking availability that occurs around the Randall Park area every Sunday morning. For those who live in the area and have to rely on road side parking, this can prove to be an issue during these time frames. Diverting the traffic to other areas in the neighborhood could help to better relieve the parking congestion that occurs around the park block .

ArcCollector Demo

Introduction: 

Smart phone technology is increasingly becoming a staple piece of equipment to have while out in the field. In addition to its standard multi-use field functionality, operating as a watch, compass, GPS, timer, calculator, flashlight, and camera, various apps are now providing live data collection and mapping software accessible in the palm of your hands! 

ArcCollector is an ESRI product which allows for the live, multi-user collection of various point-data locations with the use of a cell phone. Additionally, the program allows various attributes to be updated based on preset domains from the convenience of the field. In this lab, students demonstrate the data collection efficiency of ArcCollector with an on-campus field survey which worked to collect weather data using multiple point data locations from various prescribed zones on campus. 

Study Area: 

For the purposes of this assignment, the University of Wisconsin- Eau Claire's campus was divided into a total of seven zones as shown in Figure 1 below. On Wednesday, March 29 between 3:30 and 4:30 pm, the class went set out to survey the campus and collect specific micro-climate data at sites within each of the zones. To ensure that no area was neglected, the class was divided into a total of seven groups, leaving two people to manage the collection of approximately 40 total data points within each zone. This write up speaks specifically to the data collected in zone four of the University. 

Figure 1: Group Zones and the Study Area

Methods:

For this exercise, students began by first downloading the ArcCollector app on each of their mobile devices. Students could then sign in to their enterprise accounts and ask permissions to join the class group on the department home page. With Professor Hupy's granted permission, students could then access the geodatabase made for the purposes of this assignment. One of it's most convenient features, the ArcGIS online functionality allowed for students to each update data points and attribute fields in real time, simultaneously. The class was given roughly an hour to collect their data point site locations and update each of their corresponding attributes using only their cell phone and a Kestrel 3000 pocket weather meter shown in Figure 2. While in the field, a point could be established on the online map by selecting the "add point" tap within the mobile application. The selection proceeds to an editing page which allows for modification of attribute fields (guided by preset domains), before finally establishing the point and updating the live-feed map. Some of the attributes collected at each of the sites included:

Figure 2: Kestrel 3000

• Group (1-7)
• Temperature (F)
• Dew Point
• Wind Chill
• Wind Speed (mph)
• Wind Direction (azimuth)
• Time
• Notes

The final locations of the data points collected by the class is showcased in Figure 3. 

Figure 3: Micro-Climate Data Point Collection Sites

Once all data points were obtained, the data and its corresponding attributes could be viewed and manipulated using the user friendly functionalities found in ArcGIS online. Some examples of ArcGIS online functionalities include proportional symbol mapping and IDW interpolations as demonstrated in the micro-climatic maps displayed in the results section below. Additional modifications could be made again after downloading the layers into ArcMap desktop software.

Results: 

The first map represented here as Figure 4 reveals the interpolation of temperature using the temperature attribute data collected in the field. Light blue colors symbolize the lowest ranging temperatures (with the lightest color ranging between approximately 49 and 51 degrees Fahrenheit) while the darker blues indicate the highest temperature range (the highest between 57 and 60 degrees). Looking at the dispersion of temperature differences, the map indicates that temperatures were at their lowest in areas around the river on lower campus. At home near Milwaukee, they call refer to these type of significant temperature differences near the Lake Michigan coastline as "The Lakefront Effect," where the lake is responsible for producing cooler temperatures in its coastline areas during the summer, and warmer temperatures in the winter. In Eau Claire, perhaps this would be referred to as a "Riverfront Effect." Contrastingly, temperatures appear to be significantly warmer within specific areas of upper campus (in some areas, up to 10 degrees warmer!). This could be due to the tendency of warm air to rise, in this case, to a slightly higher elevation. It could also be a result of its increasing distance from the Chippewa River. 

Figure 4: Temperature Interpolation Map

The next map (Figure 5) focuses on representing the wind speed and wind direction data that was collected at each of the plotted data point locations. Speed is symbolized by the size of each arrow symbol while the arrows direction indicates the direction from which the wind was blowing. The map shows wind speed to occur at its highest speeds around areas on upper campus, the bridge, and within parking lots. This can be expected as they each are effected by a higher elevation and/or a lack of wind disruptive features such as buildings or trees.

Figure 5: Wind Speed and Direction Map

The last generated map (represented as Figure 6) once again uses the ArcGIS online interpolation function to illustrate the distribution of data collected by measuring the dew point of each site location. The map illustrates high dew point levels occurring in the area occupying lower campus, especially near to the Chippewa River and Little Niagara Creek. This makes sense as these areas may be expected to have a higher moister content in the air. Areas on upper campus, contrastingly, had much lower dew point observed numbers as a result of it's significantly higher elevation.

Figure 6: Dew Point Interpolation Map

As with all good field experiences, the completion of this lab was met with a few minor complications and faced some known areas of error. For starters, while the ArcCollector app can prove a useful infield tool, the tool can only remain useful for as long as the cell phone battery is able to last. This created a problem in the field when one of the cell phones used by a data collector in the area four zone died mid-way through the collection of these data points. Luckily, there was enough time left over for the zone's second point collector to cover the remaining of uncovered ground in the area. A second example of error occurred in the set domains of each of the provided attributes. Domains were set so that even though the Kestrel 3000 read its values to the nearest tenth, the data could only be recorded as a whole number in the online geodatabase. This resulted in a rounding of values which may have skewed the numbers in the overall class collective dataset.

Conclusion: 

ArcCollector is an efficient and easy method of in-field data collection. Its multi-user, simultaneous accessibility allows data surveying to be completed on mass scales at a rapid pace, and the overall user-friendly functionality linking the data and attributes to ArcGIS online makes the manipulation of various datasets quick and resourceful. In all, the program proved to be a useful tool to keep close at hand for any surveying endeavors that might arise in the future.

Conducting a Distance Azimuth Survey

Introduction:

Figure 1: Survey Stations along Putnam Trail

While surveying using a grid-based system can be useful for mapping smaller plots, technological advancements with GPS has allowed field surveying to become an easier, faster, and more accurate tool for obtaining and displaying field data for scales of various size. However, the use of any type of technological equipment comes with risk for technological failure while the surveyor is out in the field. Still, the job must get done! For this week's lab, students used various a variety of tools at varying locations along Putnam Trail to survey a series of select trees using the old-school Distance-Azimuth survey method. This method requires the measurement of distance and compass degree between several surveyed points (10 trees) and one pin-point location tied to a latitude and longitude coordinate pair (data collection stations) to be used later for mapping. The surveyed area and stations are pictured in the reference map (Figure 1).

Methods:

Data Collection:

To best familiarize students with a variety of equipment that could be used to obtain distance and azimuth data in the field, students divided into three groups and worked as a team to collect 10 data points from each of the three stations, using the various tools provided as they rotated through.

Figure 2: Image of TruPulse 360 

At Station 1, a TruPulse 360 range laser was used to determine the distance between the surveying pin-point and its selected surrounding data points, as seen in Figure 2. Other necessary tools included a standard compass for measuring the azimuth of the surveyed trees, and a basic GPS unit to measure the coordinates of the central point, or data collector's position. At the data collection point, the latitude and longitude pair read 44.796 deg. N and -91.5016 deg. W. While collecting the data, students alternated roles operating the TruPulse 360 range viewer and compass, measuring the diameter of selected trees, and recording the data until reaching the data collection total of 10 various points. This method was especially accurate in measuring the distances between collection point and tree. Also noteworthy of this tool's function, the measuring units displayed on the reading scope could be easily changed in the equipment's settings to read in either imperial units, metric units, (as used), or in degrees. Some challenges that arose when using this method, however, was the sensitivity of the tool's reading. In some instances, the tool tended measure the distance of a small branch that intercepted the scope on the way to the intended select tree. For this reason, many of the collected data points at this station had to be double, and triple checked for distance integrity of the actually intended data point.

Figure 3: Measuring Diameter of Trees at Breastlevel

Station 2 was the most time consuming because aside from the need for the GPS to denote a specific coordinate point, this station made due without the use of technology completely. Instead, students used a tape measure and compass to determine distances between data collection point at 44.79585 deg. N and -91.50033 deg. W and its surrounding trees. For this reason, the group did not travel nearly as far for plotted points, and the second station appears to be the more clustered data collection group of the three methods. While this method can be especially handy in case of equipment failure, it was also the least accurate of the three methods. Again, students alternated roles between holding and leading the tape measurement, reading the compass azimuth, measuring tree diameters, and recording the resulting data. Figure 3 shows a fellow group mate taking the circumference of a tree at standard breast level in order to find the diameter. Some complications the group faced in gathering data included the struggle to pick trees that were a far enough distance away to appear significant when plotted on a map, but not so far that another tree would block the tape measure's route, causing a curve in the tape and skewing the measurement's reading.

The last station surveyed used a range reader and receiver combination to record the distance. The data collector held the range reader gun at 44.795383 deg. N and -91.499388 deg. W while the person measuring the tree's diameters held the receiving end of the pair. The reader would measure and display the distance between it and the signals picked up by the receiving device. Some of the challenges that arose from this method was the occasional inability for the reader to pick up on the signal sent out by the receiver. This usually just required minor positioning adjustments so that signals would send properly without signal interruption.

Data Normalization and Mapping:

Once all of the data had been collected in the field, it was then necessary to format the data into an Excel file and normalize into a format compatible with ArcMap. A sample snapshot of the final data sheet is displayed below in Figure 4.

Figure 4: Normalized Data Table in Excel

Figure 5: "Bearing Distance to Line"

and "Feature Vertices to Point" 

Commands Location

After creating the table, the routes and distances measured between the three central data collection points and their corresponding trees were imported and plotted onto the map using the Bearing Distance to Line command in ArcToolbox under Data Management >> Features as shown in Figure 5. Figure 5 also references the location of the tool used to place the points where the trees surveyed were stationed at the end of the measured distance line. This tool could be found in the same section of ArcToolbox labeled Feature Vertices to Point. Finally, a topological image was placed beneath the resulting plotted points for reference.Figure 6 illustrates the results generated after the use of both tools in inlay of a basemap.

Figure 6: Tool's Resulting Map Image and Plotted Data

Results:

From the data points and features plotted on the map in the image above, the following map (Map 1) was constructed to further showcase the resulting distribution of each of the three methods and the corresponding trees which were selected in collecting data from each of the central points.

Map 1: Putnam Drive Survey Stations, Methods and Tree Data Points

The first station using the TruPulse 360 was the most effective tool used of the three stations for collecting distance data. It was quick, user friendly, and accurate in its measurements and could be measured with only one user. It also allowed the surveyor to collect these data points without actually having to approach any of the trees. Considering the steeply angled uphill slope of the terrain south of the trail, some of these trees were more difficult to reach by foot when necessary, as in instances when measurements were being taken at the second two surveying points. Station 3, for instance, had the convenience of using technology for measuring these distances as well, but the surveyor still needed a second person to hold the receiver at the tree's location. For this reason, most of the data points collected at the last two survey points were collected north of the trail to avoid any uphill hikes.

Conclusions:

Learning the Distance-Azimuth surveying method is an important skillset to use as a back up tool in the case of equipment or technology failure. Though the results produced are less accurate than those that may be obtained through the use of technology, the method still does a fairly good job of displaying the overall location of collected data points.

The Distance-Azimuth method can also be applied in the Point-Quarter sampling method used for determining the relative concentration of a species in a given habitat, especially those with a less defined shape as is the case with Putnam Trial. During this type of sampling, the same relative technique is used to get an estimate of the overall number of species that are within a given area. To perform sampling, a number of species in the area (in this case, trees) are sampled at random from a central point. Their correlating data is recorded and the trees are each prescribed an identifying number, just as performed in the lab detailed here. The methods begin to differ from here. In the Point-Quarter surveying method, once points are collected, a compass is used to determine and lay out four individual quadrants. The total sampled number of trees observed is multiplied by four (for four quadrants) to get the relative density of the area. This number is multiplied by the total density (calculated from the tree diameters) in order to obtain the absolute density of a species within an area, in this case, the absolute density of each tree species along Putnam Trail.

Overall, this lab equipped students with the necessary knowledge to overcome potentially critical situations that may occur in the field that will enable them to still get the job done! Despite living in an age with ever-advancing technology, learning the basics of the trade and the "old-school" methods used to collect location based data is a handy tool set to have stowed away for the occasional instances in which they just might be needed in the future.

Pix4D Demo

Introduction:

This lab demonstration was designed to introduce students with Pix4DMapper, a program that transforms a series of aerial imagery into a single compiled mosaic capable of rendering both 2D and 3D surface models. 

An online Pix4D Software Manual was provided to familiarize students with the capabilities and limitations of the program's functions. Through exploration of this manual, students were prompted to answer the following questions related to the software:

-What is the overlap needed for Pix4D to process imagery?

        70% Frontal, 60% Side

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

        More overlapped imagery is needed in these instance: 85% Frontal, 70% Side

-What is Rapid Check?
        Rapid Check allows processing to be completed at a much quicker pace, but with a lower accuracy and resulting resolution.

-Can Pix4D process multiple flights? What does the pilot need to maintain if so?
        The software IS able to process multiple flights, provided that there is a thorough amount of overlap between photos and that the photographs were taken under the same conditions (same relative time of day, weather, ground layout, etc.).
-Can Pix4D process oblique images? What type of data do you need if so?
        Oblique images CAN be processed, provided there is enough overlap in and between datasets. 
-Are GCPs necessary for Pix4D? When are they highly recommended?
        GCP's are NOT necessary for Pix4D. They are however highly recommended during tunnel reconstruciton. 
-What is the quality report?
        The quality report works to identify errors within the processing of the given data imagery. 

Methods: 

Following an in class demo, students began by creating a new project in the program, importing the series of 68 provided images titled "Flight 1", and undergoing initial processing before finally completing the processing with Point Cloud and Mesh processing and DSM, Orthomosaic and Index processing.

The results from the initial processing stage generated a quality report (Figure 1) and a single mosaic (Figure 2) that could be used for 3D rendering after completion of the final two processing features (Figure 3). All 68 of the photos in the series were successfully processed.

Figure 1: Quality Report

Figure 2: Flight 1 Mosaic of Litchfeild Mine, Eau Claire, WI

Figure 3: 3D Rendering of Mine Site

Upon the successful completion of 3D rendering, students were able to use the program to produce a fly-by video visual of the 3D terrain surface examined in Figure 3. The resulting video revealing a glimpse of the 3D model from each of the four directional perspectives has been provided in Video 1 below:

Video 1: Flyover of Mine Site

Data Discussion: 

The overall results from the processing of the original 68 aerial photos proved to be successful in stringing together a singular mosaic which was later used for 3D rendering. Contributing largely to the success of this data processing was the high amount of overlapping images used in the data set. The diagram in Figure 4 illustrates where these areas of overlap occurred, as well as the degree to which overlap occurred. The bright green area occupying the majority of the figure is representative of the areas with five or more overlapping images for every pixel. Contrastingly, the red areas at the fringes of the study area depict a scarcity in image overlap per pixel, probably because it lies outside of the mining site.

                   Figure 4: Overlap

Completion of the second two processing stages resulted in a total of two rasters, one as a DEM, the other a Mosaic, as depicted in the map below. The DEM works to symbolize the elevation of the terrain in the area of interest while the mosaic provides the actual visual frame work. Areas with high elevation are pictured in red on the DEM image while areas of low elevation are pictured in blue.

Final Critique:

In all, the results of this lab are meant to demonstrate the functions and capabilities of Pix4DMapper as well as the potential benefits its use. While this demonstration only revealed a small fraction of the software's potential, it is evident that this program could prove powerful in the geospatial world, analyzing everything ranging from land use and agriculture to architectural and structural projects in urban planning. 

Using Survey123

Introduction:

There are a number of survey sets which Geographers often consult for the purposes of collecting data, the gold standard of surveys and go-to consult often being the US Census records. Sometimes, however, the surveys that are popularly referenced for consult do not host the information sought for in the data collection process. In these instances, developing personalized surveys are a key way for Geographers to reach out to the community for data collection of a particular topic of interests. Survey123 provides a user-friendly platform that allows students to quickly formulate and customizes online surveys for a community. This lab provided students with a tutorial (through https://learn.arcgis.com/en/gallery/) on how to use Survey123 and manipulate details within its program for further use in future labs.

Methods: 

The tutorial was formatted in step by step exercises for students to replicate directly, and ultimately, produce a final product which, in the case of the example, would serve the Homeowner Association in evaluating the disaster preparedness of community homes in a given study area. The tutorial was divided into a series of four lessons overall, including: "Create a survey," "Complete and submit the survey," "Analyze survey data," and "Share your survey data."

During the first lesson under "Create a survey," students worked at constructing the overall survey survey form, focusing on the questions the survey will ask, and the standardized types of responses a surveyor may use to answer. The platform is set up in an easy to manage format that allows the survey builder to simply "Add" (Figure 1) a question with presets per each response type (numerical, multiple choice (single or multi-answer), text), and then use "Edit" (Figure 2) to formulate the actual question and modify any necessary response options.

Figure 1: Add a Question Type

Figure 2: Edit the Question and Associated Response Options

Once the survey was built and complete, students were able to move on to the "Complete and submit the survey" lesson portion of the tutorial. In this section, students perform a number of runs through their survey's final product, and submit them for use. In the case of this lab, the survey conducted a total number of eight times before evaluation.

The third lesson in the tutorial, "Analyze survey data." allowed students to visualize the statistical results of their collected survey evaluations. The Survey123 platform allows students to visualize their collective results to each question through the use of columns charts (Figure 3), bar charts (Figure 4), pie charts (Figure 5), and proportional symbol mapping (Figure 6). One of each of these methods is provided with the people per household question in the four figures below. Each question also hosts some statistical result tables posted below visual illustrations of the data collected, some include a number of statistics while others include simple percentages for certain responses.

Figure 3: Survey Question Results- Column Bar Graph

Figure 4: Survey Question Results- Bar Graph Chart

Figure 5: Survey Question Results- Pie Chart Graph

Figure 6: Survey Question Results-Proportional Mapping Graph

The last lesson on "Share your survey data" walked students through the process of publicly publishing their survey to be taken by the target community through the web. The final tutorial illustrates how the program allows for the generation of pop-up configuration maps with data collection points linked to text of their associated surveys as shown below in Figure 7. The survey creator can choose which survey elements they choose to remain in the pop-up configuration links within the maps so that personal information is not revealed to the public eye.

Figure 7: Data Collection Points and Pop-Up Survey Configurations

Conclusion:

Overall, experimenting with Survey123 through the tutorial provided was effective in helping students to learn the potential of the program, how to navigate its layout and manipulate its functions. Survey123 will prove useful in conducting personalized research which requires a self-generated survey to obtain new data about a population.

Sources:

Survey123
https://learn.arcgis.com/en/projects/get-started-with-survey123/lessons/create-a-survey.htm
ESRI