Field Activity 12: Processing UAS Data with Pix4D

Pix4D Overview

Pix4D is a drone photogrammetry software that uses images to create point clouds, DSMs, orthomosaics and more. It is a survey workflow that allows for a variety of professional fields such as; construction, agriculture, and real estate to access quality software for analyzable results. Users can utilize Pix4D with any camera, photo, or it's app, Pix4Dcapture, to generate data that is easily shareable. It is available online or offline so no internet connection is needed.

Pix4D FAQs

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

It is recommended that users have at least 75% frontlap and 60% sidelap.

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

With snow and sand in uniform areas, 85% frontlap and 70% sidelap is recommended.

• What is Rapid Check?

Rapid check is a fast processing method that creates a visual surface very fast but with low resolution. This is great for field workers who need a quick check to view their work.

• Can Pix4D process multiple flights? What does the pilot need to maintain if so?

Yes, Pix4D is capable of processing multiple flights. The pilot needs to maintain the same vertical and horizontal coordinate system throughout the whole project if they wish to merge multiple flights.

• Can Pix4D process oblique images? What type of data do you need if so?

Pix4D can process oblique images. It is recommended to take images every 5-10 degrees if doing so, as well as capturing two sets of data at different heights.

• Are GCPs necessary for Pix4D? When are they highly recommended?

GCPs are not necessary for Pix4D, but they are highly recommended especially when a project has no geolocation

• What is the quality report?

The quality report is the description of how the data displayed after the initial processing. It gives a summary of the entire dataset, and how good of a quality result it processed in.

Using Pix4D/Methods 

When Pix4d is opened, Projects is clicked so that one can open a New Project. Name the project something relevant, hopefully coordinating with a naming convention, and save it where it can be found later. From there, the "Select Images" screen opens up. At this point, all of the flight image files collected with a drone can be added. Click on one image, and then hold shift and click the last image in a folder to add all images at once. Click "Next" once this is done, review the Image Properties, and within that page select "Edit" within the camera model to change the Shutter Model to Linear Rolling if the camera model used collects images this way. Click "Next" and review the Output Coordinate System page to ensure accuracy. Click Next and select the type of processing to be completed. In this case, it will be "AG RGB". Creating a study area can be helpful to make processing faster. To do this, select "Map View" and then select Processing Area and delineate the area wanted to study. When first running the processing, only select "1. Initial Processing" to view to data's quality before the rest of the processing can occur. This will generate a Quality Report to be viewed to ensure that quality is high enough to process. Once this is reviewed, the point cloud mesh and dsm, orthomosaic  and index can be processed.

CALCULATING AREA OF A SURFACE

1. Select the view
2. Select the rayCloud
3. Select New Surface
4. Click to select vertexes, and right click to finalize polygon

MEASURE LENGTH OF A DISTANCE

1. Select the view
2. Select rayCloud
3. Select New Polyline
4. Click to select distance to be measured

CALCULATE VOLUME OF 3D OBJECTS

1. Select view
2. Select volumes
3. Select New Volume
4. Click the vertexes around the object and right click to finish the object shape

CREATE A FLY-BY ANIMATION

1. Select view
2. Select rayCloud
3. Select the camera icon from the create box
4. Either choose User Generated Waypoints or Computer Generated Waypoints
5. Select the duration and speed of the flight
6. Save the file using the browse button
7. Render the video to save the file

Results

Figure 1: Mosaic image with the shapfiles of the distance, volume, and area calculation over-layed.

Figure 2: DSM result of Pix4D processing.

Figure 3: Fly-By Animation of entire captured area.

Pix4D Review

Pix4D is a great program for processing UAS imagery. Even those who have no knowledge of geographic skills could have a basic understanding of how to use the program. It creates high quality output with relative ease, and those who spend time getting to know how to use the ins and outs of Pix4D could create incredibly accurate photos for a variety of different professional applications. It may not be as accurate as LiDAR, but using the processing tools can make output that is usable if LiDAR is not accessible. 

Field Activity 11: Surveying Points Using a Dual Frequency GPS

Introduction

The field activity's purpose is to acclimate oneself with using a dual frequency survey GPS to collect points and create a continuous surface raster layer with the data collected. The topographic survey is meant to get students comfortable using the equipment in the field, using a recognizable land feature to visualize the results before bringing the points into ArcGIS. 

Methods

STUDY AREA

Figure 1: Study Area of the hill

The survey is completed by bringing a dual frequency GPS out to the hill in the center of campus of UW-Eau Claire. From there, students get in groups of three and take turns operating the GPS to collect points all along the hill to gather a comprehensive topographical survey. This is done by leveling the GPS receiver, and collecting the point on the survey GPS hand held touch screen. After the points are collected, the students go back to the lab to import a .txt file of the data into an xcel file. From there, the excel file is imported into ArcScene as a table. This table is then turned into xy data points, using the tool. From there, a variety of interpolations are completed to create a raster output image of the hill to be viewed in 3D.

Figure 2: Using the Dual Frequency GPS.

Results

After the interpolations were ran, different results showed the data collection through different lenses. This was completed because of the different interpolation methods ran create the points between the points in different ways. The best interpolations for the students were probably the TIN  and Natural Neighbor interpolation. The outcomes were very similar to the sandbox field activity completed in terms of quality of actual representation.

Figure 3: IDW Interpolation Result

 The IDW result weighted the high point too much, which created a little tip in the center of the top of the hill. Other than the point, the rest of the hill was mapped fairly well.

Figure 4: Kriging Interpolation Result

The Kriging interpolation was a pretty good representation of the hill. It has a fairly gradual slope down, which imitates the actual hill very well.

Figure 5: Natural Neighbor Interpolation Result

The Natural Neighbor interpolation result is probably the second best interpolation for the various results. It used the outermost points to create a polygon that shows the actual shape of the hill that students naturally gravitated to to collect points around the size of the hill. The slope is represented fairly well too.

Figure 6: Spline Interpolation Result

The Spline interpolation was probably the worst representation of the topographic survey. It created much more dramatic inclines and slopes than were actually there.

Figure 7: TIN Interpolation Result

The TIN results were on par with the Natural Neighbor results in terms of quality. It created the actual shape of the hill, and provided a gentle slope where necessary, and more steep areas where the hill changed elevation much faster.

Conclusion

The field activity was very good to gain an understanding of how a dual frequency survey GPS works. These skills can be transferred to real world experience when working in the field. The technology worked very well for this activity, and allowed for a thorough completion of the topographic survey.

Field Activity 10: Developing and Deploying an ArcCollector App

Introduction

The field activity is intended to deploy an ArcCollector app that can help to answer a geographically based question. The objectives are to fully understand what goes into the back end of a geodatabase, and how to replicate the results later on in life. The question at hand for this project is: What houses need to be serviced for the Office of Sustainability's $core Program. This program goes to student housing in Eau Claire and rates their housing on different levels of sustainability. This app will help them to identify the houses they need to service, as well as give them a good background of information before they do. The proper design is important for developing the geodatabase, because every field must be populated with the correct data, and so developing each domain along the way must be very accurate 

Study Area

The study area was all of Eau Claire, as student housing can be almost anywhere in the city. 

Figure 1: The study area of Eau Claire.

Methods

The first step was to create a geodatabase to deploy later on to ArcGIS online. The next step is to create the domains that will be used for the fields in the feature class. After this is done, a feature class is created to store the data, and all of the fields are generated to allow for proper data collection. 

Figure 2: Geodatabase creation, along with domains.

As Figure 2 shows, the SCORE.gdb in the top left shows the geodatabase ready to launch. The main viewer in the figure shows all of the domains in creation. The geodatabase is then shared to the to ArcGIS online as a service. At this point data collection in the field is possible.

Results/ Discussion

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Figure 4: Real time map results.


As Figure 3 shows, most of the points are near UW-Eau Claire. In fact, every point but one is within a one mile distance to the campus. This shows trend correlates to the fact that most student housing is concentrated along water street. This is also just a preliminary finding, but the trend would suggest that if more points were added to the map service, it would show similar findings. One suggestion for fixing, is for the Apt # field. The long integer did not allow for decimals, and most apartments with an apartment number were 1/2, or letters such as a or b. In this case, a solution could be to go back and change the field to text and limit it to 4 characters. The embedded map, Figure 4, also did not work.

Conclusion

The need for proper project design is very important. A project cannot be completed if the design does not allow for the work to be done correctly. The question of what houses need to be serviced was answered pretty clearly, and gives $core workers a good idea of where they need to go when they go out into the field to service these houses. If this were to be made into a bigger project, Survey123 would be a good alternative, as it provides a good interface for the project at hand.

Field Activity 9: Using ArcCollector to Gather MicroClimates

Introduction

The purpose of Field Activity 8 is to gain a working understanding of ArcCollector through the means of creating a geodatabase, deploying the project, and accessing it in ArcCollector. ArcCollector is a very useful application for mobile collecting of data in the field. It offers many of the good features of ArcGIS out in the field, all on a mobile device. This activity can help one learn how to deploy a project that is needed when group participation is needed in mapping an area without the ability to have one person go out in the field. This streamlines it and allows many to collect the data into one geodatabase.

Study Area

Figure 1: Study Area

The study area was the entire lower campus of UW- Eau Claire's campus. Many different groups went out to collect data in all of the delineated spaces in Figure 1. He(the writer) could not attend class, so the data was made available to him to study.

Results/Discussion

Figure 2: Temperatures collected around campus in microclimates.

The distribution of points suggests that the tree covered microclimates have a lower temperature, such as in the southeast microclimate, and part of the westernmost climate near the river. This can also be confirmed by the concentration of warmer temperatures collected near all of the buildings in the east microclimate, and the northernmost part of the southeast climate. These areas are all relatively treeless and would have less tree cover to absorb some of the radiation.

Figure 3: Dewpoints collected around campus within microclimates.

In comparison to the temperature map above, the dew point map shows trends using similar distribution techniques. The tree covered areas have higher dew points that the non-tree covered areas, which suggests that there is higher water concentration in the area because of that. This information could be used to interpolate that trees are in areas with a higher concentration of high dew points if this study were completed on a larger scale.

Figure 4: Wind Speed Collected at different micropoints around campus.

The wind speed map shows information that could be inferred from a topographic map as well. As the points get to an overlook on a higher elevation, they are higher, as well as when one is over the river, where the wind speed would be higher too.

Figure 5: Wind Direction by Angle Collected around campus 

The wind direction map is an interesting way to look at the area. The directions tend to swirl near some of the buildings, but for the most part all head N NE and then head NW when it interacts with the river.

Conclusion

ArcCollector is a very effective means to collect information. The lab shows that the maps generated from collected data is very accurate and highly informative. With a large army of people, one could collect just about anything. It is a great tool to collect information that could be displayed on maps, and solves the goals of projects when planned out effectively.

Field Activity 8: Navigating With a Map And Compass

Introduction

Field Activity 8 is designed to utilize maps created in Field Activity 7 to navigate through terrain at the Priory on UW- Eau Claire. The construction of maps beforehand is extremely important so that one may get an idea of what terrain they should expect when out in the field. The Priory is an extremely densely wooded area, so one should be prepared to navigate through that. Activity 7 creates topographic maps so that when one goes out in the field, they are prepared to view the terrain around them and navigate without aerial images of the study area helping them. Much like many studies, the points are pre-plotted so, having very accurate maps is important to be able to find them when a geographer goes out into the field. Having analog maps, and being able to navigate them is important because technology can always go down, and it is better to be prepared than not if one is forced to use non-technological means to complete study.

Figure 1: Study Area

Methods

Materials

• Field Notebook
• Pen/Pencil
• GPS
• Topographic maps (Field Activity 7)
• Compass

The first step to do when one gets to the Priory is to mark down the five points that they are going to go find out in the woods (Figure 2). The GPS used is turned on, and set to UTM coordinates, to coordinated with the topographic map and the next step, which is a pace count. To accomplish the pace count, one marks out a length of 100 meters, walks down and counts their paces, walks back and counts again. Once this is completed, the person studying the area averages the two counts to discover their pace count. This is important to get an idea of distance when they are out in the field using the maps an way points to discover their location. It is a good idea to write down their pace count down on the map to remember it when they are out in the field. 

Figure 2: Field Map with points and bearing written down

The next step is to record the bearings from the starting location to the first point, and then from point to point for each point after that (Figure 2. This gives the geographer a basis to navigate through the woods, before going out. After the bearings are recorded the next step is to go start finding points. At the starting point, and each consecutive point, a person hold the compass flat at chest height and sets the bearing using the red in the shed method. After that, the a spotter int he group starts walking, counting their paces until the get to their pace count which equals 100 m, and then the group continues to follow that method, going along by an interval of 100 m, eventually getting to their point of interest. This is done to navigate all the way from the start to point 5. The GPS is consulted if one feels completely lost.

Results/ Discussion

As with most studies, the results are not as easily collected as the instructions say. Due to the incredibly dense woods of the Priory, many struggle getting through the brush without losing their direction and pace count, when it is not possible to walk in a straight line. When this happens, it is important to consult the compass to check the bearing, as that is a very good indicator of where one is going. Due to this, it becomes easy to get lost (Figure 3). For Group 1, they thought they were lost a couple times due to losing their pace count, only to be right where they should have been a couple times.

As one can see, many of the track logs weave through the study area, which indicates the difficulties experienced through the activity. Group 1's track log is different due to using a different GPS unit, which collected a line .shp file to log the track. Looking at the image, one can see many of the groups crossed each others paths, although not at the same time. For Group 1, some of the weave backs can be attributed to being ~.5 of a degree off on their bearing on one navigation to a point. This caused them to walk past their 3rd point, and eventually brought them to their fourth. From there, they walked back to the third point, which is the northern most part of their track, and then navigated to their fifth point after that. This shows that preciseness is extremely important when using any instrument to navigate, as even a tiny discrepancy can cause huge errors when the errors are expanded to much larger areas. Technology also had difficulties when importing the .txt file of the points studied. The points are imported, and transferred to xy data and they show up far off of the study area.

The UTM side of the map is extremely helpful when completing this activity. All of the points are set up in UTM, and the GPS is too, so being able to look at the three of those can be extremely helpful when way off track. The degrees decimal map was far too course to use on this course, as navigation becomes difficult when attempting to navigate that big of a grid. Next time, Group 1 would add a polygon of the starting area, as that was difficult to spot on the topographic map to start off on.

Problems

• Group 1 members did not send the pictures of the points visited.
• When the point .txt file was imported, the points showed up in ArcMap far south of the track logs.

Conclusion

As one can see, the construction of precise maps is very important when navigating using any instrument. Correct bearings are also extremely important, as small discrepancies can cause huge errors when magnified to much larger areas.  An important lesson one can take away from Filed Activity 8 is to always be prepared whenever they go out in the field, as it can be an immense help to have all of the details right.

Field Activity 7: Developing a Field Navigation Map

Introduction

Field Activity 7 is designed to facilitate the creation of field navigation maps. The class will be utilizing these field maps to conduct a survey at the Priory on the UW -Eau Claire land south of the campus the following week. The maps will be extremely important to navigate around the area, as the Priory is very wooded, so having topographic maps of the area is a very valuable resource when walking around the study area. Two field navigation maps are created for this activity, one being done with a UTM coordinate system, and one being done with a Geographic Coordinate System of Decimal Degrees. The coordinate system is very important, especially in conducting surveys, because one must have a reference to a global scale while conducting local surveys. Both maps will have grids overlaying them. The UTM coordinate system is based upon meters, so it's map has a meter based grid. The Geographic Coordinate System of Decimal Degrees is base upon degrees, so the map has a grid based upon decimal degrees. 

Methods

The first method is to open a blank map in ArcMap. The data is saved in the TEMP folder in the Q drive, so the Geodatabase for the Priory is copied over into individual student folders. The navigationboundary feature class and the priory_2ftcountours feature class are then added to the map. After this, the navigation feature class is changed to a hollow box, allowing the internal contents to be seen.The navigation maps are now ready to be created.

UTM Map

The contour must be projected to Transverse Mercator to properly line up with the navigation boundary for the UTM map. This allows a meter-based grid to be overlayed to help create a navigation map. The map is then changed from data view to layout view. This allows a grid and other map elements to be inserted into the map. Before anything else is done, the page setup must be changed to 11x17 inches to create a map that can be printed. The data frame is then fit to the new view. To create a new grid, one must right-click on the current data frame and got to properties. From there, Grids is selected. New Grid is selected to create an overlay. For UTM, Measured Grid is selected. Leave Grid and labels selected, and in the intervals, change the X Axis and Y Axis values to smaller values such as 50 and 50. Leave the next page as the defaults and click finish. Next re-open the Grid page within the data frame properties. Selected the measured grid just created and select properties from the right menu. Go to labels and make sure the Label Style Format is in Mixed Font. Change the font size to 6. From there, go to Additional Properties and select "Group by decimal point" and change the font color to light grey. Click okay on all boxes to exit out back to the map. After this, add other map elements such as: North Arrow, scale bar and reference scale, the projection, coordinate system, data source, watermark with the makers name, and a title.

Decimal Degrees Map

The decimal degrees map does not need to be projected, as the grid will be based upon the coordinate system. Change the map from data view to layout view, and change the page size to 11x17 within the page and print setup. Refit the data frame to the new size to begin. To create a new grid, one must right-click on the current data frame and got to properties. From there, Grids is selected. New Grid is selected to create an overlay. To create the Decimal Degrees map, Graticule is selected, as the grid will be based upon latitude and longitude. To create a navigation map for an area as small as the Priory, change the X Axis and Y Axis intervals to 5" each. Leave the next pages as the default and click finish on the last page. From there, select the graticule grid just created, and select properties. Select Labels, and ensure the Label Style Format is in Degrees Minutes Seconds. Once that is done, change the font size to 6. Then select Additional Properties and make sure the Label Type is Standard. Click okay to close out of all the boxes back to the map. After this, add other map elements such as: North Arrow, scale bar and reference scale, the projection, coordinate system, data source, watermark with the makers name, and a title.

Results

Figure 1: UTM based map.

Figure 2: Decimal Degrees map.

Field Activity 6: Conducting a Distance Azimuth Survey

Introduction

The point of Field Activity 5 is to create a field survey using azimuth angles and distance between oneself and the object being surveyed. This is extremely important when one has no other form of survey techniques available, such as a survey GPS. The field activity's main purpose is to collect a tree survey of Putnam Park in UW -Eau Claire. It is done without a survey GPS, and completed with just basic technologies. Data normalization is incredibly important when working with different groups of surveyors, so the process for creating fields in Field Activity is as follows: x, y, distance, azimuth, diameter at breast height (DBH), tree type, and the point number. This facilitates the correct data entry among different groups of people at different locations. The "X" field is longitude, the "Y" field is latitude, the azimuth is the angle to the tree being surveyed form the surveyor, distance is the distance in meters to the tree, DBH is diameter in cm, tree type identifies the type of tree, and the point number is the different locations a survey is collected.

Figure 1: Black box indicates study area of tree survey

The first point of study by the Geog. 336 class is important because it provides a base point for the rest of the study. The other two points should be collected, and reviewed to make sure that the latitude and longitude make sense spatially.

Methods

Materials

• Hand Held GPS locator
• Rangefinder
• Tree Diameter Tape
• Compass
• Field Notebook

Figure 2: Collecting the azimuth (right) and the distance (left).

The first step is to work as a class to collect the first set of ten trees at Point 1, where the class starts. The GPS point is collected and used to enter all of the data for the ten trees being collected. Someone then uses the compass to find the azimuth angle to a certain tree. Once that is completed, someone uses the rangefinder to shoot the distance to the tree being surveyed to collect the distance in meters (Figure 2). Another person then uses the diameter tape to collect the diameter at breast height, which is important because the tree could have abnormalities lower down, so breast height is a good normalization technique (Figure 3). Once this is done, the tree is identified and logged in the data notebook. The notebook is important because in an azimuth distance survey, there is a good chance that there will be no technology connection to satellites to log the data. The point number is also logged for each tree as 1. Once this is done, different groups go to two other points away from the base point to collect more data for ten trees at each point. 

Figure 3: Collecting the DBH.

The next step is to enter all of the data into an Excel spreadsheet in the appropriate fields. This is then imported into ArcMap by creating a geodatabase for the survey. Once this is done, right click on the GDB and select import Table(single). Select the table to import and enter in the appropriate fields. The next step is to use Bearing Distance to Line command in Data Management->Features. This is used to input the table into lines from each point. The feature class is then input into the Feature Vertices to Points command within the same folder of geoprocesses. This creates the points of each tree. Once these feature classes are added to a blank map, a base map is added.

Results/ Discussion

Figure 4: Final Survey Map 

The data is created to show the different trees that are surveyed (Figure 4). The points all show the correct distances and azimuths to each tree. There are, however, difficulties that happen in this survey. The points are collected beneath a ridge by the UW -Eau Claire campus, so all of the GPS points are off in the initial table. 

Figure 5: Initial map created from data.

The intial points are all off, so the identifier tool has to be used to collect the right lat and long of the actual points. The table is edited to reflect this create the final map (Figure 4). The hand written data works exceptionally well with the other technologies at hand. Although the data was wrong at first in the x and y field, this did not create too much of a problem for creating the final map. This method is effective if no other option is available.

Conclusion

This was a very effective distance azimuth survey. Although some of the data was wrong initially, identifying the correct lat and long is not a big issue. It is different from a survey that would be completed with a survey GPS, as all of the measurement is done by hand, including the distance and azimuth of each tree from each point. For simple surveys such as tree collection, the distance azimuth survey is effective.

Field Activity 5: Visualizing Survey Data

Introduction

In the last field activity, group 1 set out to create a digital elevation model (DEM) by collecting data points within a 1 m x 1 m sandbox. The group collected elevation points using a stratified sampling system that also had systematic sampling techniques applied as well. Once the survey was completed, the data was entered into a spreadsheet to be imported into ArcGIS to be mainpulated into the DEM. An important aspect to complete field activity 5 was data normalization. Data normalization was important for group 1 because in order to create a DEM, the data has to be organized properly. This organization allows groups to reduce data redundancy and helps to improve the integrity of the data. For group 1, data normalization was accomplished by creating three columns in a spreadsheet with the field names: "X_Cell", "Y_Cell", and "Z_Value". The X cell corresponds to the x value of the sandbox grid. The Y Cell corresponds to the y value of the sandbox, and the Z Value corresponds to the elevation points collected at each point. This organization allows the data to be imported  fairly easy, with the names already correlating to geoprocessing techniques within ArcGIS. The data points group 1 collects are used to facilitate interpolated processes that allows ArcGIS to create a DEM very efficiently. The interpolation procedure takes in the collected data points and creates new points around the known points through a variety of different methods.

Methods

The first step group 1 took to complete the activity was tom import the Excel file with all of the data points into a Geodatabase for the sandbox. The fields were double checked to be numeric, as this allows the points to be converted into data usable for map creation. The data is then added to a blank map in ArcMap as "XY data'. This allows one to convert the points brought in to a feature class that is usable for interpolation later. The next step the group took was to utilize the 'Raster Interpolation' within the 3D Analyst Geoprocessing tools to create maps that replicate the intended terrain.

Figure 1: Actual Sandbox Terrain with Amanda adding detail to sand.

The first interpolation utilized by group 1 is Inverse Distance Weighted (IDW). This method averages points in each neighborhood of a cell, and then weights out the priority of veraging to determine the value each point is given (Figure 2). This method was very successful for the group, as many of the data points were collected in the center of cells, so the output image brought a very realistic image of the terrain to a DEM. The 

Figure 2: IDW Interpolation Result

The next method employed by the group was the Natural Neighbor interpolation procedure. It is also known as the "area-stealing" interpolation, as it applies only local values, and establishes a weight that creates a value for each point around the surface. The natural neighbor interpolation provided perhaps the most realistic model of the terrain in the sandbox (Figure 3).

Figure 3: Natural Neighbor Interpolation Result

The third method employed by group 1 in interpolation was the Kriging interpolation process. It uses regionality to establish Z-value patterns throughout the surface, without employing any other method. It assumes the surface is homogeneous, and the same pattern of values are observable at all locations on the terrain. It did not due to well to describe the data collected by group 1 (Figure 4). It centered too much weight around the plateau in the lower right corner of the terrain, and did not do a good job at describing the mountain ridges.

Figure 4: Kriging Interpolation Result

The fourth method employed by group 1 was the Spline interpolation (Figure 5). It utilizes a mathematical function to minimize surface curvature, which in turn maximizes the smooth surface of a surface. It assesses all sample points, and fins the curvature between them. The spline interpolation method did not work well for the data, as it focused very heavily on the data points that were clustered, and formed terrains that were over-exaggerated.

Figure 5: Spline Interpolation Result

The final interpolation method that group 1 employed was the TIN surface. This method employs triangular networks set up between the data points collected. It connects all of the lines to create triangles, which create a network of shapes that resemble the surface. The TIN method worked very well for the data group 1 collected (Figure 6). It portrays all of the various terrains very effectively.

Figure 6: TIN Interpolation Result

The data was manipulated to bring into ArcMap, as the 3D visuals are not apparent in anything but ArcScene and ArcGlobe. To create the scene in ArcScene, the data was interpolated using the various methods. The properties of each individual file is then changed to set the elevation from surfaces to "Floating on a custom surface" to create the 3D effect. Once this is done, one can change the data frame properties of each map to "Calculate From Extent" selection of the vertical exaggeration. This creates the proper scene for analyzing the data. Once the scene is set, the shapefile can be saved as a layer file, and captured as an image. This allows one to open up the two in conjunction with each other in ArcMap to overlay a legend and map properties upon the image. The image was chosen to be landscape orientation, as it displays images such as these the best way.

Results/ Discussion

The best interpolation method for group 1 was the natural neighbor system (Figure 3). It created very accurate local points that portrayed an image that resembled the original sandbox terrain quite accurately. There were very few unaccurate points of the survey. They will be discussed in this section.

Overall, the IDW interpolation provided a very good image of what the sandbox studied looked like. I captured every feature of the box quite well, however the spacing between points portrayed a skewed data set. It did not display the sandbox as a continuous terrain. This could probably be solved by collecting more data points to create a more fluid picture of the terrain. The natural neighbor provided an excellent DEM. It was able to portray the terrains of the different regions with clarity and quality. The only way to make the interpolation more effective would have been to collect more points in different areas. The Kriging interpolation was very poor to represent the groups data. It did not account for the mountain ridge in the sandbox, and created the plateau as a very high hill. This is most likely because the plateau had a key chain size llama on it that the group also collected (which appeared in different interpolations). The spline interpolation was also poor, most likely due to the high concentration of points near the llama shrine again. It provided a good image of the regional if one were to "read between the lines" and see the spots that the reduced curvature removed. The TIN provided an excellent interpolation for the data. The method allowed the data to create well-done triangles that portrayed the terrain as it is. Like the Natural Neighbor interpolation, the TIN scene could have been much better if many more data points were collected to create a continuous data set. Overall, each of the interpolations could have been successful to view the data as a DEM if necessary. The features alone create a very interesting method to collect data. This field activity allowed group 1 to create an effective DEM off of the last field activity in which they collected the data.

Conclusion

This survey was a very effective stratified sampling system. This survey is like other stratified sampling systems in that it collected the elevations of locations, be it for engineering or otherwise. It is different because it was collected using a meter stick rather than a survey GPS. It is not always realistic to perform a grid based system to collect data, as it could intrude upon private land, or buildings one can not enter. A stratified system with a grid influence is probably the best bet for most collection, as it can account for other influences that don't always come up in planning.

Field Activity 4: Digital Elevation Surveying

Introduction

Sampling is a scientific collection method that involves precise measurement of small portions of a large study. It is very important to sample, especially when studying areas of the earth. Sampling allows a study to be completed faster, more efficiently, and saves many resources in the process of creating a valid sample of the entire study area. There are three types of sampling used; random, systematic, and stratified. Random sampling use completely random location collecting. It is completely unbiased, where everything within the study has an equal chance to be selected. It can also be a bad choice to sample a study randomly if large areas within the study are not being sampled. Systematic sampling uses a regular, evenly distributed interval of the study to collect samples. Things such as a grid, or lines of study can be used to achieve a systematic sampling system. It is good to do very straight forward data collection, but can be biased in collection. Stratified sampling is the third sampling method. It uses known groups of a study, and collects data points according to the size of the groups in the study. It can be done in concurrence with the other sampling techniques, and can generate good data that represents the entire study. It can be bad when the groups of the data are not known, as this generates skewed data.

The lab's objective is to accurately construct an elevation surface of a terrain. The study will be completed using a 1 m x 1 m sandbox that students will construct a landscape within using sand to create a unique terrain. This terrain will then be sampled using the sampling method of choice by the students and entered into a spreadsheet to be used for creating a DEM in ArcGIS.

Methods

The sampling method chosen by group 1 was to create a sample using a stratified sampling technique, with a systematic sampling grid to create an easier sample collection. This became effective when the group ran into time constraints, and needed to collect data in a timely matter. This is similar to a systematic sampling method, but involves the shape of the terrain within the study area mapped out as "groups" within the larger area. The sample plot is located near the University of Wisconsin -Eau Claire's Phillips Building. It is directly across the East side of Roosevelt St in a yard of a local resident. The sample plot studied by the group was the western box of the two (Figure 1).

Figure 1: Completed sandbox set-up that includes a forest to the east, prairie lands to the west, and various terrains in between.

Materials Used

• Meter Sticks
• String
• Thumb Tacks
• Data Collection Notebook
• Pencil
• Samsung Galaxy S6 Edge

The sampling scheme was set up using a grid with roughly 10 cm spaces between each reference point on the x and y axis. The sea level was decided to be at the top of the box. Strings were then set up as a spatial reference point at each x and y point to draw over the grid the rough drawing of the elevation model. This allowed group 1 to create a stratified system of equal groups within the model that streamlined the data collection (Figure 2).

Figure 2: Creating a Sampling technique.

Once this was set up, elevation points were collected using a meter stick to collect to data of elevation compared to the sea level (0 cm point). It was entered directly onto the drawn sampling grid (Figure 3). This created a good system for the group to complete the task at hand.

Figure 3: Drawn out sampling method.

Results/ Discussion

In total, there were 178 data points collected using the stratified method. They ranged from very low in the deepest part of the depression to very high on the highest peak. The prairie lands have similar values throughout much of the western portion of the sample.

Minimum: -16 cm
Maximum: 12 cm
Mean Elevation: -4.71 cm
Standard Deviation: 4.64 cm

Figure 4: An example of the collected data.

The sampling method was effective for the group. It became more effective when the group decided to use the string to print lines across the sand box to easily view the cells. The biggest problem for group 1 was the fact that the group only included 2 students, so moving the data points, collecting the data, and writing it was difficult to do between 2 people.

Conclusion

The sampling method deployed by the group utilized a good stratified system that allowed the students to involve a systematic sampling approach to the data collection. One must always consider sampling spatial situations as the time and resources saved become very valuable when a sample can be as efficient as possible. This same sampling technique could be deployed in a much large area to collect data, when the time used to collect the data is amplified many times over. This survey did a very good job of collecting data and the DEM created on ArcGIS will show if the methods were successful. One could add the strings across the whole plot to view the data much more clearly when collecting.

Field Activity 3: Creating a GIS for Hadleyville Cemetery

Introduction

Field activity 3 is designed to facilitate the creation of a GIS for Hadleyville Cemetery. In previous avtivities, students collected data from Hadleyville Cemetery using handwritten notes and a UAS survey drone. Activity 3 is designed to bring all of the data together to create a visual GIS for the county of Eau Claire to distribute for the cemetery. This activity is important because the cemetery does not currently hold all of the records for the burial plots anymore. Thus, a map with all of the data is very important for them to know the current status of the cemetery. Each burial plot point on the map created holds all of the known information of the grave site such as name, year of birth, year of death, condition, and any other attributes available from the headstone. 

Study Area

Hadleyville Cemetery is located in the town of Pleasant Valley. The official address of it is County Road HH, Section 04, Twn 25N, Range 9W. It is on the south side of the road, and is about 1 block west of S Lowes Creek Rd. Figure 1 shows the location within the County of Eau Claire.

Figure 1: Location of cemetery within Eau Claire County. (1)

Methods

The class used a combination of hand-written notes and aerial data collection to create a GIS for this project. Dr. Hupy flew the UAS over the cemetery to get a good aerial picture of the parcel for mapping. 5 bands of em were collected to help view the headstones in different light. The mm accuracy of the drone allowed people to drop a point directly onto the burial plots within the cemetery. Once this was done, the class created a Google spreadsheet o all of the data collected to eventually save as an excel file. This allowed the class to collaborate on data collection, and create a hard copy of the information so that it could be reviewed by all before it was published. After the spreadsheet had been made, the class made GIS' of the images with points on each grave corresponding to the knowledge of the hand-written data. This was then table joined to the excel table in arcmap (see Figure 2). This process was very quick and allowed the class to streamline the process to create a GIS. Once this was completed, an aesthetically pleasing map was created of the results (see Figure 3).

Figure 2: Portion of Grave attribute table/

Results/Discussion

Figure 3: Resultant map with graves plotted out.

Overall the method was very efficient. The class originally planned on taking a survey GPS out into the field to collect all of the data points, but due to bad tree cover and time spent creating the points, the hard copy notes, and heads-up digitizing proved to be the most efficient method of creating a successful GIS. One way the class could have refined the method would have been to count the headstones in each row to get a definitive number before collecting the data. Assigning the correct number of headstones was a difficult task for the class on the spreadsheet. This would have allowed the class to plan out how the graves would have been divied up, and would have provided a much smoother data collection.

Conclusion

The methods transferred well to the overall objectives. The mixed format of data collection allowed the class to expedite the project very quickly and accurately, and provided a means for collaboration. The potential errors should be reviewed, but are most likely negligible to the overall final product. The GIS created provides a clean, accurate source of data for the county and is a successful product. If all goes well, this GIS should be able to be kept in use for as long as the county can keep tack of it.

  Citations

1) Wikipedia Page on Hadleyville Cemetery https://upload.wikimedia.org/wikipedia/en/f/f5/Hadleyville%2C_Wisconsin.png

Field Activity 2: Developing a Proposal for Mapping the Cemetery

Introduction

o Provide background to the problem at hand. What are the problems and challenges facing Hadelyville cemetery?

*From Activity 1*
Hadleyville cemetery is currently without any maps or records of the plots on their land. This poses potential problems for the visitors to the lot who don't have information on where their family members or friends are buried. This could also pose problems to the county of Eau Claire as they could sell the same lot to multiple people without any records on the plots.

o Why is building a GIS of this project better than a simple map and/or spreadsheet?

A GIS is better than a simple map for this project because visitors to the cemetery will be able to use the online GIS to search through the information and find the burial plot of the one they are looking for before even going. It will also allow them to view the status of the headstone before visiting to find the plot much easier.

o What equipment are you going to use to gather the data needed to construct the GIS; ie what is the overall approach?

A survey GPS unit will be used to plot out each grave stone so that the information will all be stored online. A survey drone will also be used to collect land data and allow the points to be added to a visual map. Digital cameras will also be used to take pictures of the headstone for each burial plot. Pen and paper will also be used to collect data.

o What are the overall objectives of the method being employed to gather the data.

The objective to this activity are to create an easy to use GIS service that will alow visitors of the cemetery to plan out a visit with ease.

Methods

o What combination of geospatial tools did the class to use in order to conduct the survey? Why?

The class used a combination of hand collected data, aerial UAV data, and GPS collected data. This allowed all of the data to be added to the UAS map to be viewed with utmost accuracy.

o What is the accuracy of the equipment you are intending to use? (Be sure to cover each piece of equipment)

The survey GPS is accurate to the cm. The UAV is within mms of accuracy, and the hand collected data is accurate to the visual eye.

o How was data recorded? List the different methods and state why a pure digital approach is not always best. What media types are being used for data collection? Formats?

Figure 1: Jeff collecting hand-written data.

The data was first collected with hand written notes. It was also partially collected with a survey GPS, and an aerial imae was taken with a UAV. This was very important, because as the study discovered, it was very difficult to do all of the burial plots with a survey GPS unit in the time allotted, and because the UAV took such a high resolution image, the data collected by hand was able to be digitized into the aerial map to create a GIS.

o How will you transfer the data you gather into a GIS

The data will be transferred into point vector data onto the aerial map to create a GIS.

o What equipment failures occurred if any? What was done to remedy the situation?

There was no equipment failures outright, but the trees over some of the burial plots blocked out the signal for the GPS and made collecting become a very slow task. The process described earlier to create a GIS was the solution to the time difficulties experienced in the field.

o What might have been done to facilitate data collection in terms of equipment and refining the method?

If the survey would have known the GPS unit would pose so much difficulty, the data needed for the GIS could have collected with more accuracy as well as collecting more of a quantity of grave stones.

Conclusion

o How did the methods transfer to the overall objectives of the project?

The methods transferred pretty well. The grid system allowed groups to assign themselves different rows, and the whole class was able to collect the data with ease. With the time difficulties of the GPS unit, we were able to collect enough data with hand written notes that we will be able to create a GIS with the information we ave on the aerial.

o How did the mixed formats of data collection relate to the accuracy and expediency of the survey?

The drone survey created such an accurate map that it allowed us to bypass the difficulties of collecting each burial plot, and the time it would have taken.

o Describe the overall success of the survey, and speculate on the outcome of the data.

The survey seemed to be fairly successful. The overall outcome should be pretty good once it is created.

Field Activity 1: Hadleyville Cemetery Mapping

Introduction

o Provide background to the problem at hand. What are the problems and challenges facing Hadleyville cemetery?

Hadleyville cemetery is currently without any maps or records of the plots on their land. This poses potential problems for the visitors to the lot who don't have information on where their family members or friends are buried. This could also pose problems to the county of Eau Claire as they could sell the same lot to multiple people without any records on the plots.

o Why is the loss of original maps and records a particular challenge for this project.

Without the the original maps and records, there is no basis of knowledge to collect the headstone's available information. This also makes all records of dire importance to record everything collected as best as possible.

o How will GIS provide a solution to this problem?

GIS will provide a solution through providing the clearest information possible to the client. It will provide both a map and the information recorded to the county.

o What makes this a GIS project, and not a simple map?

This is a GIS project through the type of information being collected. Spatial data would be best stored on a GIS format, so the result of this will provide a clear result.

o What equipment are you going to use to gather the data needed to construct the GIS?

A GPS unit will be used to complete the project.

o What are the overall objectives of your proposal?

The overall objectives are to provide an easy to use map-service to the county of Eau Claire. This will allow the cemetery to give visitors the option to look online for the exact location of their friend or family member's burial plot.

Methods

o What is the sampling technique you chose to use? Why?

The sampling technique chosen was to plot out the cemetery as a grid. Each row will be given a letter and each column will be given a number.  Each plot will be assigned a number and a letter on this grid. The technique will allow for the visitor to find the burial plot much easier than it currently is.

Figure 1: Picture of Hadleyville Cemetery with a grid overlay. 

o What is the accuracy of the equipment you are intending to use? (Be sure to cover each piece of equipment)

o How was the data entered/recorded? Why did you choose this data entry method?

The data will be recorded on a GPS hand-held unit. This data entry method will allow the precise location of points.

o How will you transfer the data you gather into a GIS?

The GPS will allow the data points to be transferred over to GIS very easily.

o What drawbacks are there to the method you propose? How to the pros outweigh the cons of this method?

The biggest potential drawback is that the burial plots won't be evenly line up and the grid won't work as well as hoped. The pros outweight the cons because the grid will be even enough to work so that a visitor can walk down the row and count out the letters to find their plot with ease.

Conclusion

o How do your methods transfer to the overall objectives of your proposal?

The methods will transfer to the overall objectives because in a GIS map-service a grid will allow the user to view the location of the burial plot with ease compared to walking around with no clue as to where to go.