Navigational Map Activity

Nathan Sylte
Navigational Map Activity

Towards the beginning of the semester a navigational map was generated to be used at the University of Wisconsin Eau Claire Priory. The map can be viewed on a previous blog post Navigation Map. The map possessed two sides with one side containing a UTM grid and the other side containing a lat/long grid. In our case we used the side that contained the UTM grid in order to navigate the assigned course (Figure 1).

Five UTM points were assigned per group as part of the navigation course. In our case the course included the area with many pine trees in the north/northwest portion of the map (not the pine plantation in the southeast/central portion of the map. There were also many ravines in our section of the course which made navigation very difficult. It was very difficult to maintain a pace count due to the terrain and foliage. Therefore, the GPS came in handy at times when the terrain made navigation difficult. Pictures were to be taken at each of the points but there were technical difficulties with both of the phones. Therefore, the group was unable to take any pictures.

Figure 1. Navigational map of the UWEC Priory.

Overall, the course took two hours to complete and several lessons were learned about navigation. The first take away from the activity is that one cannot always rely on technology. Both the cell phones ran out of batteries and the GPS would sometimes have trouble picking up a signal. A quality navigation map become important in this case for the topology lines had to be used on several occasions for orientation. Another lesson that was learned from this activity is that navigation is very difficult when the terrain is uneven. Uneven terrain makes keeping direction and pace count almost impossible. Finally, this activity proved to be very educational. It demonstrated the importance of a good old map and compass.

Topographical Survey: Using Dual Frequency GPS and Phantom 3 Drone

Nathan Sylte
5/2/2017

Surveying Point Features with Dual Frequency GPS

Introduction:

This weeks project is part of a two week project that involved surveying point features with a survey grade dual frequency GPS. The second portion of the project will also involve a drone flight over the survey area. Therefore, the purpose of collecting GPS points will be to use them as ground control points for the aerial photographs that will be taken with the drone. Aside from collecting survey points other attributes were taken at the GPS point locations. These included soil attributes such as pH, soil temperature, and volumetric water content.

The location the survey is taking place includes the community garden to the south of South Middle School in Eau Claire, Wisconsin (Figure 1) (Figure 2). The garden (outlined in red) is bordered to the north by a row of pine trees and a football field. To the south the garden is bordered by a road and a marsh area.

Since this aerial photo is dated, a more recent photo of the community garden is also provided to gain a better understanding of the study area (Figure 3).

Figure 1. Locator map for the survey location. 

Figure 2. The topographical survey location south of South Middle School.

Figure 3. Google Earth image of the community garden that was also the location of the topographical survey.

Methods:

First, the class was divided into groups to perform the different tasks. However, everyone got a chance to use the GPS unit.

To begin the survey the dual frequency GPS unit was deployed to collect ground control points and soil attributes (Figure 4). The survey unit possessed two components which included a top receiver and a TESLA unit (Figure 5). The TESLA unit is essential a mobile field collecting device (tablet). After both of the components were turned on the accuracy of height and vertical distance of unit were set. This would insure the points that were collected were accurate.

Once the survey platform was leveled at each of the desired GCP locations the GPS captured 30 points. The points each varied in height and vertical distance, and the points were then averaged by the GPS unit to provide an accurate reading. After the GCP was taken an orange flag was placed at the location of the GCP for soil attributes to be collected. These soil attributes were then entered into the GPS unit tablet.

At each of the orange flags (GCP locations) soil attribute data were collected. As stated before the attributes that were collected included pH, soil temperature, and volumetric water content. PH was collected by using a high accuracy pH meter. Soil temperature was sampled using a soil temperature thermometer, while volumetric water content was taken with the use of a TDR Probe.

Figure 4. Dr. Hupy demonstrating how to deploy the GPS unit.

Figure 5. GPS unit deployed at one of the GCP locations. Orange marking flag is also visible.

UAS Flight 

Unmanned aerial systems are becoming ever more present, useful, and can perform a vast array of tasks. After, GPS and soil data were gathered an unmanned aerial flight demonstration took place with a Phantom 3 Drone (Figure 6).  A key aspect of unmanned aerial flights is the initial planning portion of the flight. This is done in detail to insure that the drone flies on the correct path and collects quality data. During the initial pre-flight planning specific altitudes and sensors are selected depending on the data that are to be collected. In our case the flight over the Eau Claire community gardens took around 10 minutes and many aerial images were taken. It is important that many overlapping images are taken to insure data quality. Once the flight had taken place a quick post flight procedure followed. The conditions of the environment were taken by the drone incase another flight over the area was to take place (Figure 7). After the images are taken the data can be processed in Pix4D (see previous lab Pix4D Lab). In our case Dr. Hupy processed the flight data in Pix4D to save time.

Figure 6. Phantom 3 Drone

Figure 7. Post flight 

Results and Discussion:

The first result includes a comparison between elevation and moisture (Figure 8). Soil samples indicated that the soil with the lowest moisture could be found in the eastern section of the garden. However, there were other dry section in the northern part of the garden as well. The elevation was also the highest in the eastern portion of the garden where the soil was the most dry. But upon looking at other portions of the garden this pattern was not consistent. Many other factors could account for soil moisture other than elevation. Soil cover could be a great example of another factor that can influence soil moisture. 

Figure 8. Soil elevation and moisture. Dry and high areas are shown in darker colors. 

Soil moisture was also compared with pH (Figure 9). Upon first glance there appears to be a correlation between soil pH and moisture. However, there are several discrepancies that would indicate the relationship is small at best. There are dry areas that have both higher and lower pH levels. For example the north section of the garden is dry with low pH and the east section of the garden is dry with high pH. 

Figure 9. Soil moisture and pH. Darker colors indicate dry areas and high pH. 

When looking at the relationship between soil moisture and temperature it can be seen that there may be a relationship (Figure 10). The most dry area in the east section of the garden was also the warmest area. Other dry areas around the garden correspond to other areas of warm temp. This can been displayed in the north section of the garden. The relationship between moisture and temperature is present but not extremely strong. 

Figure 10. Soil moisture and temperature. Warm and dry areas are displayed in darker colors. 

A digital surface map was also created by using the data from the aerial flight (Figure 11). Surfaces with high elevations are displayed in darker colors such as red. The areas that are the most red include the tops of trees while the lowest area included the marsh. The marsh can be located in the southern portion of the map. 

Figure 11. Displayed above is the DSM of the community garden area. Tree tops are represented by dark red. 

Conclusion:

Overall the lab proved to be very practical and applicable. Whether a power line company needs to survey power lines with an unmanned system to save money or an agricultural company needs to collect crop data, unmanned systems are certainly going to become common place in the future. Developing familiarity with unmanned systems during this lab will prove valuable in future geospatial projects.  Using the GPS total station was another key component to the lab. This piece of equipment demonstrated how survey grade GPS coordinates can be efficiently gathered with the use of a portable system. Finally, the biggest learning curve of the lab was becoming familiar with the Phantom 3 Drone. Although the drone is not overly complex to operate, there are logistical matters that take time to become familiar with.  



Soil Health Survey: ArcCollector Part 2

Nathan Sylte
4/25/2017

Soil Health Survey

Introduction:

The objective was to use the geographic inquiry process to conduct a study. The geographic inquiry process involves developing a geospatial question. Then, relevant data is collected and analyzed to answer the question. Our task was to generate and deploy a geodatabase with domains to ArcCollector. It should be added that the proper usage of geodatabases and domains is critical when collecting data. Data would then be collected in the field using ArcCollector to answer a geospatial question. 

Specifically, the geospatial question that came up was , "Are the soil homogeneous throughout the UW-Eau Claire campus?" 

To answer this question different soil measurements and observations had to be made. In this case, soil PH, moisture, and grass appearance were used to determine the consistency of the soil throughout campus. Grass appearance does not necessarily correlate with soil health. However, healthy grass has a certain aesthetic appeal to it so it was included in the data collection. 

As previously stated, the study area for this project will include the UW-Eau Claire campus. The campus of the University of Wisconsin Eau Claire is fairly diverse with the campus being divided into an upper and lower section. There is also a very heavily forested area and hill dividing upper campus from lower campus. This area is part of Putnam Park. Upper campus is primarily comprised of dormitories, while lower campus is mainly made up of academic halls. Lower campus also extends across the Chippewa River with a walk bridge connecting the two parts of lower campus. Throughout campus there are several large open areas. These large grass open areas are where the data will be collected. The smaller patches will not be touched. 

To view the study area check out the embedded map below.

Also, view Figure 1 to see the zones where the soil data were collected in. 

Figure 1. Different zones where the soil data were collected shown outlined in red. 

Methods:

Before any data collection took place a geodatabase for the project with domains was created so the project could then be deployed to ArcCollector. For this study three domains were created. Moisture, PH, and grass health comprised the three domains that were created. In the geodatabase for the soil health project a soils feature class was created. This feature class contained three fields. These fields included moisture, PH, and grass health. Moisture and PH were both set to float and grass health was set to text. Grass health included three different categories which included excellent, moderate, and poor. These three attributes were to be determined based off the appearance of the grass.   

After the geodatabase was set up, the project was deployed to ArcCollector by following the create and share a map tutorial on ArcGIS Online (ArcCollector Project). 

Once the project was deployed to ArcCollector, data could then be gathered. This involved going out to the different grass sites with a hand held meter that collected moisture and PH. The overall health of the grass was also observed. Points were taken at intervals so that a good portion of the zone would be covered. Following the collection of soil data, maps showing PH, moisture, and grass appearance were generated. 

Results/Discussion:

There were several interesting finds after analyzing the data. First, there were several locations that were more acidic than expected (Figure 2). The grounds crew regularly maintains the grass/grounds therefore a neutral PH was expected to be present in the soil throughout campus. One of these acidic locations included the large grass area just west of the Haas Fine Arts Center. Another location that proved to be more acidic than expected was the grass area just north of the Nursing Building. It should be added that soil that contains a PH of lower than 7 is considered acidic. However, a PH of 5 to 6 is not considered a strong acid. All together, a good majority of the soil had a fairly neutral PH. This is representative of the work that the grounds crew does to maintain the campus yards. 

There were many inconsistencies with regards to soil moisture levels throughout campus (Figure 3). Certain areas such as the yard to the north of the Nursing Building possessed a very high level of moisture. Meanwhile, areas such as the yard west of Towers Hall and the grass area north of Davies Center were very dry. These areas also received a poor to moderate grade as far as the grass health observation (Figure 4). The likely cause for the poor to moderate grass health grade and the low levels of soil moisture would be the fact that these areas receive a high amount of foot traffic. This foot traffic can destroy the grass and decrease the grasses ability to hold moisture. It should be added that there was an area with a high amount of moisture and very poor grass. One of the yards to the north of the Nursing Building had very poor grass and extremely high moisture. It is possible that the grass there is receiving too much moisture. 

Figure 2. Soil PH represented in shades of red. IDW interpolation method was used to map PH. 

Figure 3. Soil moisture shown in shades of red and blue. IDW interpolation method was used to map soil moisture. 

Figure 4. Grass health observation map. Healthy grass shown in green, while unhealthy grass shown in red. Grass health was assessed based on appearance of the grass where healthy grass was the most green and thick. 

Conclusion:

To answer the geospatial question of "Were the soil homogeneous throughout the UWEC campus?", the conclusion can be made that the soil is in fact somewhat heterogeneous throughout campus. Although there are many locations where the soil quality is very similar, several locations held different qualities. Though the differences in soil health were not extreme, the grounds crew may want to attend to several areas throughout campus. One of these areas includes the yard west of Towers Hall which needs to be watered and re-seeded. 

Once again ArcCollector proved to be very useful and applicable (view previous blog for more ArcCollector uses MicroClimate Survey). This soil health project and the micro climate survey from the previous week demonstrate ArcCollectors usefulness. The next step to the soil health project could be to create an web application that could be used by the grounds crew and people traveling about campus to monitor the campus grounds. This would be done by using web app-builder for ArcGIS. Overall, the lab proved to be very useful in further developing the ArcCollector skill. 

Microclimate Survey: ArcCollector Part 1

Nathan Sylte
4/11/2017

Microclimate Survey 

Introduction: 

The objective of this lab was to become familiar with ArcCollector by conducting a microclimate survey of the campus at the University of Wisconsin Eau Claire. A microclimate survey simply analyzes the different environmental conditions around lower campus. In this instance temperature, wind chill, dew point, wind speed, wind direction, and humidity were measured as climate variables. The survey involved the entire class collecting climate data at once with the use of ArcCollector on the individuals smart phone. 

The campus of the University of Wisconsin Eau Claire is fairly diverse with the campus being divided into an upper and lower section (Figure 1). There is also a very heavily forested area and hill dividing upper campus from lower campus. However, the two sections of the campus are fairly homogeneous when compared with themselves. Upper campus is primarily comprised of dormitories, while lower campus is mainly made up of academic halls. Lower campus also extends across the Chippewa River with a walk bridge connecting the two parts of lower campus. This walk bridge is often described as the "coldest place in the lower 48 states", and should present different data than the other parts of campus.The primary goal of the microclimate survey was to investigate and compare the different climatic conditions between the upper and lower campus sections. 

There are several advantages of using ArcCollector to gather field data, as well as some disadvantages. One of the primary advantages of using ArcCollector is that it can be installed on anyone's smart phone. The application is also very cheap which adds additional flexibility to ArcCollector. Another advantage of ArcCollector is that multiple people can use the application at once. If an organization needed to collect quantities of broad data all at one time then ArcCollector should be considered as an option. One disadvantage of ArcCollector on a smart phone is that the cell phones built in GPS is not as accurate as a survey grade GPS unit. This adds limitations to the type of projects one may use ArcCollector to perform. If a high grade of GPS accuracy is required for the project then an additional method other than ArcCollector should be utilized. 

Figure 1. The campus of the University of Wisconsin Eau Claire. The red lines represent different survey zones. The different regions of the campus are labeled in black. 

Methods:

Before the survey took place the project was first deployed to ArcGIS Online and then to ArcCollector. Also, a pre-created geodatabase was entered into ArcCollector. This geodatabase contained the necessary domains and feature classes required for the survey. These steps were performed in ArcGIS Online by logging into the UWEC enterprise account. The project/basemap had to also be shared with other UWEC members to allow for collaboration.Another step the class had to perform before completing the survey involved downloading the ArcCollector application on their smartphones. This would allow the individual to collect and share data with other members of the class.  After the survey took place the geodatabase could then be brought into ArcMap, and the data could be analyzed and mapped. Another option was to map the data in ArcGIS Online and then publicly share the maps. This option was not used in this particular project but will be used in the next lab.

The survey methods are below. 

First, the campus was divided into different zones to insure the class was evenly distributed throughout campus (Figure 1). Each individual that participated in the survey was assigned a zone and hand held device that could measure temperature, wind chill, dew point, wind speed, and relative humidity. Wind direction was measured with the use of a hand held compass. The individual was to collect data from 20 different locations within their assigned zone (Figure 2). 

Figure 2. The location of each survey point collected with the use of ArcCollector. Look to figure 1 for additional reference and comparison. 

 Results/Discussion: 

Maps of temperature and wind speed/direction were generated in ArcMap to represent the different micro climates throughout campus. Temperature was fairly homogeneous throughout campus (Figure 3). However, there were several hot spots located throughout campus. The average temperature on March 29 (survey date) was between 49 and 51 degrees F. This was around ten degrees cooler than the temperature at some of the hot spots. The largest hot spot was located on upper campus near Towers Hall which is a dormitory. These hot spots are created from the warm air leaving certain buildings via exhaust. The exhausted increases the temperature several meters away from the hot spot and dissipates over a relatively small distance. 

Wind speed/direction varied throughout campus. There are many factors that could have altered wind speed and direction. One of these factors is the time at which the reading was taken. The wind speed/direction could have easily changed depending on the time. Another factor that can influence wind speed and direction in this case includes the layout of the buildings. The buildings can block and vector the wind. Overall, the greatest wind speeds recorded were on the walk bridge (Figure 4). March 29 was not a particularly windy day with an average wind speed of less than 5 mph. However, on the walk bridge a wind gust of 33 mph was recorded. Other gusts between 5-7 mph were also recorded on the bridge. This is indicative of the un-sheltered nature of the walk bridge. This high amount of wind also contributes to the cold temperatures often felt on the walk bridge. 

Wind direction was very heterogeneous throughout campus (Figure 4). This has to do with the layout of the buildings that hinder and vector wind in certain areas. The wind direction on the walk bridge which is in a very high/open area had the wind coming from the South/SouthEast. This is likely indicative of the true wind direction on March 29. 

Figure 3. Interpolation map of temperature throughout the UWEC Campus. 

Figure 4. Map of wind speed and direction. Wind speed is represented by colored dots with the highest speed shown with the darkest dots. The arrows are pointing in the direction the wind is blowing. 

Conclusion:

ArcCollector proved to be a very interesting, flexible, and useful application. For surveys like the microclimate survey ArcCollector should be considered as method and application for collecting data. ArcCollector also demonstrated that a large quantity of participants can all work on collecting data at once, henceforth collecting a large volume of data. The next lab which also involves using ArcCollector should prove to be very interesting and applicable.  

Distance Azimuth Survey

Nathan Sylte

Azimuth Tree Survey

Introduction:

A common method used in surveying involves the collection of distance and azimuth measurements from a certain point. The distance azimuth surveying method provides an extremely versatile way to collect data when other methods and technologies fail. Preferably, the distance azimuth method involves two people with one person stationed at the starting point. The starting point is where the azimuth is measured (degrees). The distance to the desired object is also measured. Essentially, a standard point is created (starting point), and all the other survey points are measured based off of the standard point. This insures that the survey area is portrayed accurately. The methods versatility arises due to the fact that this method can be performed anywhere under any conditions.

Study Area:

The distance azimuth survey took place along Putnam Drive in Putnam Park on the campus of the University of Wisconsin Eau Claire. Specifically, the general location of the survey was the stretch of Putnam Park directly south of Davies Center (Figure 1). This area is not uniform in forest type and elevation, with the area possessing two general habitat descriptions. Part of the study area is lowland and is generally wet year round. Therefore, the types of trees that grow in this area are different from the higher ground and ridge area which makes up the other part of the study area. The frozen ground allowed for easy surveying so the lower area was selected as the primary survey area. The ridge area was not selected because the frozen ground made for difficult sampling. For the sake of time the lower area was selected. The outline of the ridge can be seen in (Figure 1).

Figure 1. Shown above is the study area where the distance azimuth survey took place. UW Eau Claire lower campus is on the north side of the image (top side). 

Methods:

To begin the survey using the distance azimuth method a starting location at each site was chosen. This would be the designated spot where the distance and azimuth measurements where taken from.Trees where then selected and the distance was recorded from the starting point to the tree. Multiple devices where used to measure distance between the starting point and the tree. One method involved measuring tape while the other method involved remote devices such as the Sonic Combat Pro device. This device involved a receiver at the tree that was to be sampled while a person held the device at the starting location (Figure 2). The Sonic Combat Pro emits a sound wave to measure distance. To measure the azimuth one method involved a hand held compass that involved looking into the device to get a visual of the azimuth (Figure 3). The other methods involved remote devices such as the True-pulse 360B device (Figure 4). This device is similar to a range finder however it contains other options such as the azimuth option. To use this device one must simple aim the device at the desired point. Next,  the circumference of the tree was measured in centimeters.

After the data were recorded and normalized in excel, the data were brought into ArcMap (Figure 5). Once the data table was added into the map, instead of adding using the add x,y data option, the Bearing distance to line tool was used to import the data. Next, the data were converted to points using the Feature Vertices to Points tool. This tool can be found by simply entering its name into the search option. Finally, maps were able to be generated using the survey points.

An important note! The compass would not work properly to begin with. Make sure when using the compass or any other devices that there are not magnets near by. For example, little magnets in gloves can interact with the compass. Also, when entering data the x coordinates must be a negative value otherwise the survey points will end up on the opposite end of the globe. Another important note involves the Feature Vertices to Points tool. The end option must be selected in order to create points at the end of the distance line.

Figure 2. Using the Sonic Combat Pro device. 

Figure 3. Using the compass to find the azimuth.

Figure 4. Using the True-Pulse 360 B unit to find the distance and azimuth. 

Figure 5. Survey data as show in excel. 

Results/Discussion: 

The survey yielded a very mixed variation in the sizes of trees in the study area (Figure 6) (Figure 7). Several trees had a circumference greater than 150 centimeters while multiple trees had a circumference between 16 and 25 centimeters. The largest trees were located in site three which is the cluster of points in the south east portion of the map. A close up view of the survey locations are shown in (Figure 7). 

Figure 6. Graduated symbols map of the distance azimuth survey trees. 

Figure 7. Graduated symbols map of the distance azimuth survey trees in the three different locations. 

Part of the lab involved investigating another survey method. The survey method was the point quarter method described in a lab report Point Survey Method. The point survey method could be used to survey the trees surrounding the different site locations. In the case of the point survey method the survey points are randomly determined to insure an accurate representation. There are four quadrants with a center point.The distance azimuth method can be used to enhance the point survey method. It can do this by speeding up the process by adding a degree component to the quadrants. Samples can be taken at a specific interval of degrees.The azimuth can be taken at the survey points which in turn enhance the spatial accuracy of the survey. 

Conclusion: 

Overall, the azimuth survey possesses many beneficial qualities. The method is very quick, efficient, and applicable. A great advantage of the azimuth method is that it is low tech. Many times technology fails and the azimuth method can be used as a great back up option. Also, the azimuth method is very compatible with GIS which adds to the list of beneficial qualities. 

Sources:

http://www.saddleback.edu/faculty/steh/bio3afolder/Point-Quarter%20Lab.pdf

Processing UAS Imagery with Pix4D

Nathan Sylte
03/14/17

Digital Surface Modeling Using Pix4D

This lab/post was different than some of the previous technical labs and posts. Compared to the technical format that we usually use this post will be broken into three parts. First, background on Pix4D will be provided with an emphasis on its capabilities. Second, the Pix4D software will be discussed. Finally, several maps will shown that were the products of Pix4D software.


Part 1: Becoming familiar with Pix4D. How is the program used, and how is the data processed?

Pix4D essentially generates a three dimensional images. First, a drone will fly over the area of interest and take aerial photographs in a specific manner. Then Pix4D will overlap the photos pixels with specific ground points. The camera position is then calculated using an algorithm so a 3D image can be developed.


Using the Pix4D manual, some key questions can be answered regarding the dynamics of Pix4D.

1. What is the overlap required for Pix4D to process imagery?
A high amount of overlap is required to obtain accurate results. This means that a very specific plan to acquire the images must be put in place to insure the proper amount of overlap. When making the "image acquisition plan" the ground sampling distance must be known along with the terrain type and other project specifications. Failure to have a proper plan will result in low quality data.

2. What if the user is flying over sand, snow, or a uniform field?
The manual states the recommended overlap should have at least 75% overlap. There must also be 60% side overlap between flying paths. A uniform surface such as those listed above will require at least 85% overlap and 70% side overlap.

3. What is rapid check?
Rapid check quickly determines if the images taken are good enough to sufficiently cover the area of interest. It determines whether the image can be processed.

4. Can Pix4D process multiple flights? What does the pilot need to maintain if so?
Yes, Pix4D can process multiple flights. However, a specific number of overlap points are required (figure 1). Figure one displays what this might look like.

Figure 1.

5. Can Pix4D process oblique images, and what type of data would you need to do so?

Pix4D can process oblique images, but the camera must take pictures at a 90 degree angle to the ground or a 45 degree angle to the ground.

6. Are GCPs (ground control points) required for Pix4D or are they just recommended?

GCPs are not required, however, using GCPs will greatly increase the accuracy of the data. The project can then be placed on the exact position of the Earth.

7. What is a quality report?

After the points taken by the drone are processed a quality report is then generated. The quality report will include information pertaining to the processing of the image. If there are questions about the integrity of the image generated the quality report should be viewed.

Part 2: Using Pix4D.

After starting Pix4D one will simply go to "start new project". Next, the images must be added from the drone flight. This is done by copying all of the images from the drone flight into Pix4D.

Below are the image points as shown in Pix4D (figure 2).

Figure 2.

Then, you must select the type of project you with to create. We selected create a new 3D map (figure 3).











Figure 3.

After the data is uploaded it must be processed (figure 4). We first selected initial processing making sure the other options were unchecked. It is important to make sure that "Point Cloud and Mesh", and "DSM, orthomosaic, and Index" are unchecked initially. After the initially processing the two previous options were then checked and ran.

Figure 4.

Once processing is complete a quality report is then generated (figure 5). Figure five was taken directly from the quality report.

Figure 5. Fist displayed in the quality report is the initial quality check. Our initial quality check was very promising. A total of 68 images were used. 68 out of 68 images calibrated correctly.

This image taken from the quality report shows that our image had a great amount of overlap as indicated in green. There were a couple small areas with poor overlap on the outsides of the image. This is because there were no other images taken outside the AOI.

Here are some of the geolocation details.

Next, an animated fly over was created to provide a great view of the mine. 

After the animation was created I wanted to take a volume measurement from one of the sand piles (figure 6). This was done for future reference. The volume measurement was fairly straight forward. It simply required digitizing the desired area. As shown below.  

Figure 6. The total volume of this sand pile was 7195.05 meters cubed. The error was + or - 76.10 meters cubed.

Part 3. Maps

The first map created was a map showing the 2D aspect of the Litchfield Mine (figure 7). This was done in ArcScene and ArcMap. The DSM (digital surface model) portion displayed on the top of figure two clearly shows the physical features of the mine. The individual piles are clearly shown whereas the mosaic on the bottom is less clear.

Figure 7. Here is the Litchfield Mine shown in 2D. The sand pile that had the volume measurement taken is labeled in the bottom portion of the map. Metadata is provided in the center of the map.

The second map created displays the Litchfield Mine in 3D (figure 8). In ArcScene the base heights were set to 1 to best display the 3D aspect of the mine. In 3D the mosaic image on the bottom of figure 8 better portrays the mine compared to the 2D mosaic image. An advantage of the mosaic image is that equipment can be portrayed whereas in the DSM equipment cannot be made out.

It should also be pointed out that the pile that the volume measurement was taken from is shown as the dark pile in the mosaic in the west central portion of the map.  

Figure 8. The Litchfield Mine shown in 3D. The same metadata used in figure 7 applies to figure 8.

Conclusion:

Pix4D turned out to be a great tool for processing 3D images. There are also some features that are very practical such as the volume measurement feature and the animation feature. The manual proved to be very informative and easy to use. The main critique would include the time required to initially process the images. This can take a significant amount of time.

Sources:

Pix 4D Website and Manual Website, Manual

Creating a Custom Survey with Survey 123

Nathan Sylte

HOA Survey

Survey 123: Collecting Data with a Smart Phone 

Introduction:

A majority of smart phones now have similar capabilities to a computer. Smart phones can be linked to different data collection mechanisms or use their GPS to collect data in the field. This lab involved the use of Survey 123 for ArcGIS (Survey 123) to create a survey to be used on a mobile device for field data collection. Instructions on how to create the survey were found in an ESRI course (Survey Instructions). In this case, the survey was to be conducted by the HOA to determine a communities readiness in the event of a natural disaster. After the simulated survey was conducted the survey data was then put into a geodatabase to be used for analysis purposes. 

Methods:

The HOA survey was generated on the Survey 123 website. The first step involved going to the create a new survey tab (Figure 1.) 

Figure 1. Create a new survey tab. 

Then by going to the design tab, different survey parameters could be set including what questions were to be included in the survey. In this instance, the survey questions related to safety information pertaining to the survey participants residence. After the survey was created, the survey was then submitted so that people could take the survey. This was done by sharing the URL with members of the University of Wisconsin Eau-Claire organization (Figure 2.) 

Figure 2. Sharing the Survey URL

The Survey 123 application was then downloaded via smartphone. Once the app was downloaded the survey could then be taken. Multiple hypothetical surveys were then completed via smartphone and the data was complied on the Survey 123 website. The survey format as seen on a smartphone is shown below (Figure 3) (Figure 4). 

Figure 3. Survey as seen on the Survey 123 application. 

 Figure 4. Completed surveys as seen on the Survey 123 application. 

The survey data was then analyzed by accessing the analyzed tab. Many different analyses are performed on the data which can be used. In our situation, the data analyses could be used in disaster planning. After viewing the different analyses the data was then downloaded as a geodatabase to be used in ArcMap. Finally, a unique values map was generated. 

Results/Discussion:

After viewing some of the important statistical analyses of the HOA survey data, several important observations can be made. The first being the presence of fire extinguishers in a large majority of the residencies (Figure 5). A total of 87.5 percent of the residencies that participated in the survey contained fire extinguishers. This is important information to consider in disaster planning. Another important statistic relates to the type of residence that survey participants reside in (Figure 6). Twenty five percent of survey participants resided in a type of residence other than a single family house or a multi-family apartment complex. When planning for a disaster it would be critical to know the other type of residence people were living in. The "other" type of residence may influence evacuation plans. 

The HOA Survey results were important for uncovering how many people were living in the particular residence (Figure 7). This information would be critical to know in a disaster situation. Out of the five local survey participant locations the participant that lived on Niagara Street location lived with the fewest amount of people at 3. In contrast, the participant living at The Pickle on Water Street was living with 50 other people. This could pose a severe evacuation risk in a disaster situation. It should be noted that the nearest hospital (Sacred Heart Hospital) is located only around 1 mile from The Pickle. In a disaster situation knowing where hospital locations are will be critical for saving lives. 

Figure 5. The bar graph above that was retrieve from the survey data online shows that 12.5 % of survey participants do not have fire extinguishers in their residence. 

Figure 6. The bar graph above that was retrieved from the survey data online shows that 50% of survey participants live in single family residencies. 

Figure 7. Above is a unique values map of the five local survey participant locations. The number of people living in each residence are depicted by different colored dots. The participants living with the smallest number of people are shown in light green while the participants residing with the most people are shown in red (danger). 

Conclusion:

To conclude the Survey 123 lab various applications of Survey 123 were visited. One application that Survey 123 could be used in is public surveying by the DNR (Department of Natural Resources). For example, the DNR could use Survey 123 to survey hunters after they register deer to determine whether that particular hunter has noticed any chronic wasting disease in the region. The survey data could then be used to help track the spread of chronic wasting disease in Wisconsin. Overall, Survey 123 is a very practical application that could be used in countless situations. 

It should be noted that the above survey was just hypothetical and does not contain precise data about residences in the Eau Claire area. This survey was simply conducted for educational purposes. 

Sources:

"Lesson Gallery Learn ArcGIS." Accessed March 7, 2017. https://learn.arcgis.com/en/gallery/.

Notitle. Accessed March 7, 2017. https://survey123.arcgis.com/.