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