Lab 6: Distance Azimuth Survey

Introduction

In this exercise, the class was provided an introduction to distance azimuth surveying.  This method of surveying is no longer commonly used, as it has been replaced by more sophisticated GPS technologies.  Despite this, possessing knowledge about distance azimuth surveying can be very useful in the case of technology failure, since it does not require many tools and can provide reasonably accurate results.  

For this activity, trees in Putnam Park were surveyed by small groups of students usually various equipment such as a compass, measuring tape and TruPulse 200 laser distance finder.  

Study Area

The study area for this activity was Putnam Park, a natural area located behind the University of Wisconsin-Eau Claire.  Trees at three different sites were assessed (Figure 1).  At each site, ten trees were surveyed from the same point.   

Figure 1: Study area for azimuth distance survey

Methods

At the beginning of this exercise, the instructor introduced all of the students to each piece of equipment.  Due to the limited amount of equipment, small teams of students then took turns assessing trees at three different sites.  At each of these sites, the following data was collected for approximately 10 trees: 

• Latitude and longitude of site
• Distance: from control point to tree
• Azimuth: from control point to tree
• Tree Diameter: measured at breast height
• Tree Type (if identification was possible)

These attributes were chosen collectively by the class and instructor before data collection began.  It was determined that these attributes were most important for future analysis and eventual map creation.  After recording all of this data in the field, the data was entered into a spreadsheet (Figure 2).  

Figure 2: Table of data collected

Results & Discussion

The resulting data of this exercise turned out fairly well.  Although the table of attribute data is by no means perfectly accurate, it provides basic information about the trees in Putnam Park.  One major source of error in this lab is the fact that different students assessed each attribute, each with more or less accuracy.  Moreover, the equipment available at each site was different, which likely affected the accuracy of measurements.  For example, all of the measurements completed at Site 2 were done with the TruPulse, while many of the measurements at Site 3 were taken using the compass.  Another problem during this lab was tree identification, since many students had little experience in this area.  As a result, some trees surveyed may have an incorrect "Tree Type" attribute, which could cause problems if anyone else tried to locate these trees in the field.  

Conclusion

While distance azimuth surveying is no longer commonly used by geographers, it is still a very practical skill to know.  Though geographers are increasingly reliant on sophisticated technology, older basic methods are valuable as well, especially in the case of technology failures.  This skill is also valuable if more expensive technology is not accessible.  For example, the instructor shared a story in which his GPS technology was seized at an international airport.  

Lab 5: Visualizing Terrain Survey

Introduction

In Lab #4, a terrain was created in a sandbox and its elevation was assessed using surveying techniques (refer to previous blog post for more details).  

Figure 1: Normalized table

This lab will serve as a follow-up to the survey conducted last week.  Data normalization played a large role in preparing the data that was collected last week for visualization.  According to ESRI, data normalization is "the process of organizing, analyzing and cleaning data to increase efficiency for data use and sharing" (ESRI, 2016).  In this lab, the elevation data that was collected during the survey had to be normalized in Excel before it could be imported to ArcGIS.  This meant making sure all of the columns were properly sorted and changing the data to be set as 'numeric' with proper decimal values.  After this was done, the data could be imported into ArcMap and converted into a point feature class. 

Methods

After importing the data into ArcMap, it was visualized using five different interpolation techniques.  According to ESRI, interpolation is "the estimation of surface values at unsampled points based on known surface of surrounding points" (ESRI, 2016).  Here are the interpolation techniques that were used in this lab, as explained by ArcHelp and ESRI: 

1. Inverse Distance Weighted (IDW)
   Definition: An interpolation technique that determines cell values using a linearly weighted combination of a set of sample points, wherein the weight is a function of inverse distance.
   Advantages: Can control "power parameter" which determines significance of known points; can limit number of points used for interpolation to speed up processing time
   Disadvantages: Quality of interpolation can be limited if points are unevenly distributed
2. Natural Neighbor
   Definition: A deterministic interpolation method for multivariate data in a Delaunay triangulation; value for interpolation point is estimated using weighted values of closest surrounding points in triangulation.
   Advantages: Well-suited for categorical data
   Disadvantages: Interpolated heights are guaranteed to be within range of samples, so it will not display peaks, ridges or valleys that aren't represented by sample points
3. Kriging
   Definition: A geostatistical interpolation technique in which the surrounding measured values are weighted to derive a predicted value for an unmeasured location.
   Advantages:  Can provide insight on precision of predictions; appropriate when there is a spatially correlated distance or directional bias in data; well-suited for soils, geology
   Disadvantages: Z-values must be assessed prior to interpolation to determine if Kriging is appropriate
4. Spline
   Definition: A deterministic interpolation method in which cell values are estimated using a mathematical funcation that minimizes overall surface curvature, resulting in a smooth surface.
   Advantages: Works best for gently varying surfaces
   Disadvantages: Not desirable if smooth surface isn't wanted
5. Triangular Irregular Network (TIN)
   Definition: A vector data structure that is comprised of contiguous, non-overlapping triangles, wherein the vertices of the triangles are sample data points (x, y, z).
   Advantages: Non-redundant data, can use natural features as break lines; can accommodate more or less complex data with varying triangles
   Disadvantages: Less tools available for processing than for raster data

After applying each interpolation method to the data, the resulting surface maps were imported into ArcScene for 3-D visualization.  Then, the resulting 3-D renderings were exported as JPEGs.  

Results & Discussion

The 3-D visualizations of the survey data using all five interpolation techniques are displayed below (Figure 3, Figure 4, Figure 5, Figure 6, Figure 7).  After viewing all of the interpolations somewhat head-on, side views of each were compared as well (Figure 8).  This helped to determine which interpolation was the best fit for the data at hand.  

Figure 2: Sandbox terrain for comparison with 3-D visualizations

IDW:

This 3-D visualization was one of the worst results, owing to its bumpy appearance which distorts the true nature of the terrain elevation (Figure 3).  The terrain was not nearly this uneven in real life.  

Figure 3: 3-D visualization of IDW interpolation

Kriging: In comparison to the other four results, the Kriging visualization turned out to be the best (Figure 4).  This depiction is much smoother than IDW, and shows a more realistic representation of the depressions, ridge and hill in the terrain.  

Figure 4: 3-D visualization of Kriging interpolation

Natural Neighbor: While this visualization provides a rough interpretation of the surveyed terrain, its edges are somewhat rough (Figure 5).  Additionally, the depressions are not as well defined as other representations like Kriging. 

Figure 5: 3-D visualization of Natural Neighbor interpolation

Spline: The Spline visualization offers the smoothest depiction of the terrain, though in places it appears almost too polished (Figure 6).  Despite this, it stands as one of the best representations of the actual terrain.  

Figure 6: 3-D visualization of Spline interpolation

TIN: Although the TIN visualization depicts all of the intracies in elevation data well, it is rather jagged in appearance (Figure 7).  The complexity it portrayed distracts from providing a comprehensive look at the overall terrain.  

Figure 7: 3-D visualization of TIN

Side Views: Comparing the side views of all five representations reaffirms the aforementioned assessments of the results (Figure 8).  Kriging and Spline still appear to be the most effective visualizations for the terrain.  

Figure 8: Side views of all five interpolations

Conclusion

This survey relates to other field based surveys in that it was an exercise in assessing an area based on a sampling of data.  Though the study area for this exercise was small, the same methodology could be applied at a larger scale.  Using surveying techniques can be a way to save time and money, though it is important to use appropriate and effective techniques to survey the area at hand.  For example, in this terrain survey, it made sense to use intervals of 5cm for the survey grid, however, a larger survey area may require larger increments.  After collecting data in the field, a number of interpolation techniques can be applied to assess the study area.  An element of experimentation may be involved in picking the most fitting interpolation method for the data.  For example, in this exercise, the Spline interpolation appeared to be most suitable, but the TIN interpolation might be more suitable for a project in which the surveyors are concerned with expressing the complexity of the elevation changes.  Other terrain surveys may also benefit from supplemental data from remote sensing techniques or drone technology.  While field based surveys are very useful tools, it is always important to remember that such surveys contain elements of error.  It is impossible to survey every single point of a study area, and interpolation and visualization methods also influence results.  

Lab 4: Creation of Digital Elevation Surface

Introduction

The purpose of this exercise was to provide students with experience in designing, creating, surveying and mapping the elevation of a terrain.  This work was completed in groups of three in a square 4 foot by 4 foot sandbox.  For this group, the activity was carried out on Saturday, October 8th from 2:00pm to 6:00pm.  Before carrying out the activity, each team spent time reading about various sampling techniques.  Sampling is way to quickly collect data on a whole population by gathering data from only a portion of the population (Royal Geographical Society).  For field methods, sampling can be a way to save time, money and energy when dealing with large areas.  There are three main varieties of sampling: 

• Random: samples chosen randomly

• Systematic: samples chosen in a regular way

• Stratified: samples chosen in stratified way; used when population is comprised of sub-groups

Methods

The following materials were used in this activity: 

• Sand
• 2 meter sticks
• Thumb tacks
• String
• Straightened metal hanger
• Computer

A systematic sampling approach was used in this lab, as the student team determined it would be the best way to accurately gather data.  A random sampling approach could have been used through utilizing Excel to randomly select squares of the grid to measure, however, the team wanted to ensure all features of the terrain were measured in detail.

After some discussion, it was collectively determined to create the grid in 5 centimeter intervals.  The team also decided to consider the ground (beneath the sand) as zero elevation to avoid dealing with negative numbers and to make measurement easier.

While Andrew placed thumb tacks around the wooden sandbox in 5cm intervals, Jackie and Anneli created the terrain in the sand.  This terrain included a hill, ridge, depression, plain and valley (see Figure 1).  The team then used string to set up the grid (see Figure 2).  Then, the elevation measuring process began, with Andrew using a makeshift straightened hanger to measure elevations at grid intersections (Figure 3).  This measurement was then compared to a meter stick by Anneli and relayed orally to Jackie, who entered the values into Excel on her computer (Figure 4).  This digital data entry method was chosen for speed and to avoid having to transfer manually recorded data into a computer later on.  This process continued for around three hours until all of the terrain was assessed.

Figure 1: Andrew placing thumb tacks in 5cm intervals

Figure 2: Jackie laying the grid with string

Figure 3: Andrew measuring sand elevation with the hanger

Figure 4: Andrew comparing hanger to meter stick

Results

Figure 5: recorded elevations (z) in Excel

Data about 532 points was collected and entered into an Excel spreadsheet (Figure 5).  Here is a look at the data:

• Minimum: 6.6 cm
• Maximum: 23.2 cm
• Mean: 14.6 cm
• Standard Deviation: 2.6

The chosen systematic sampling method worked well for the data at hand.  That being said, the team did change the sampling technique near the end of the data collection process.  Upon encountering the flat "plain" feature of the terrain, it was decided to complete elevation measurement at every other grid intersection since the elevations were very similar.  

Problems encountered during sampling included human error when conducting measurements with the hanger.  Since the sand and ground was a bit wet during the measuring process, it was sometimes hard to tell where the sand ended and soft ground began.  To try to overcome this problem, spots were often measured twice when the first elevation number seemed to be inconsistent with surrounding measurements.  Additionally, the computer used for data entry died partway through data collection.  This problem was solved by simply recording the last two rows of data by hand and later entering them into the computer.

Discussion

This sampling exercise exemplifies the definition of sampling in that data was systematically collected at some points amidst a larger "population" of points.  Sampling is an important tool in spatial situations to gain insight about entire areas without expending huge amounts of time or money.  Though this exercise only dealt with a small area, the same principles can be applied for larger areas.  Being able to gain accurate information about a larger area through sampling can save lots of time and money, while still providing useful information.  

The survey this team completed did perform an adequate job of sampling the terrain at hand.  That being said, it would of course be better to gather more data about the unique areas of the terrain such as the hill and ridge.  If doing this exercise again, multiple measurements could be made in such areas to reflect the relief there. 

Sources

Royal Geographical Society. Retrieved October 5th, 2016 from http://www.rgs.org/OurWork/Schools/Fieldwork+and+local+learning/Fieldwork+techniques/Sampling+techniques.html 

Lab 3: Creating a GIS for Hadleyville Cemetery

Introduction

All previous maps and records of this cemetery were lost, and government officials wish to preserve the community history through the production of a new map.  This project is a GIS project because of the complex nature of the information being collected.  While a simple map could display general plot location and plot names, using GIS allows more specific location information, complex attributes such as the condition of the stone (broken/whole/missing), as well as embedded pictures of the plots.  For this exercise, a drone and survey-grade GPS unit were used.

The purpose of this project is to design an accurate map of the Hadleyville Cemetery that will be useful to government officials.  Information concerning plot location, plot occupancy/vacancy, plot names, plot birth and death dates, and condition of the stone should be accessible and understandable to map readers.

Study Area

The study area for this exercise was Hadleyville Cemetery, a small cemetery located in Eleva, Wisconsin (see Figure 1).  The data was collected during two late afternoons in Fall 2016. 

Figure 1: Locator map showing study area relative to Wisconsin

Methods

Data was recorded both digitally and manually for this activity.  Aerial imagery data was acquired by flying a drone over the study area on two different days.  Data about the graves was collected with the survey-grade GPS unit and recorded digitally by entering attribute data on the handheld device and then attaching a picture of the headstone to this data.  Additionally, data recorded manually was done so by hand in field notebooks.  Some class members took notes regarding headstone condition, inscriptions, number of headstones in the rows, etc. while others took pictures of each stone. A pure digital approach is not always best when considering aspects such as time constraints.  For example, in this activity, the survey-grade GPS took way longer to collect data than the manual method. 

After all of the data was collected, it was transferred into a GIS for future use.  The aerial imagery was transferred using cables and a USB port.  Since the data collected by the survey-grade GPS was incomplete (only data about the first two rows was acquired), the class ended up only using manually collected data about the graves.  This manually collected data was typed by hand into a shared, online Google Docs document.  Before entering all of the data, the class decided upon a set number of attributes such as first name, last name, date of birth, date of death, notes, etc.  The class also agreed upon a system of common identifiers for each grave, dividing the cemetery into rows and columns and assigning each deceased person a unique ID.  

Results & Discussion

After each class member had entered their data into the shared Google Docs document, each student went about creating their own GIS and map of the cemetery.  After downloading the document as an Excel file and making sure all of the data was normalized, creation of the GIS could begin (see normalized table in Figure 2).  This involved setting up a geodatabase, importing the drone imagery, digitizing the graves and attaching attribute data through a table join.  The resulting map can be seen below (Figure 3).  Since the entire table was too large to provide in a single picture, it is accessible online here. 

Figure 2: Attribute table with data about graves; complete table available here

Figure 3: Map illustrating grave locations; full size map here

The data collection process could have been refined by developing a class plan before collecting data.  This may have involved developing a common grid system for note taking, as some of the students did.  Additionally, the GPS unit could have been used in the areas of the cemetery where there weren't any tree coverage problems, leaving the areas with shadows to be documented via manual note taking.  Sources of error include human error when recording data, illegibility of grave markers, and equipment failures.  

Conclusion

The methods transferred fairly well to the overall objectives of the project, enabling the class to more or less effectively gather the data needed to produce an accurate map of the cemetery. The mixed formats of data collection were beneficial in terms of accuracy and expediency of the survey.  After determining that the GPS unit was taking too long to acquire location information, the class was able to utilize other formats of data to fill in the blanks.  While the GPS unit only collected information about the first two rows, the class was able to manually record data about the entire cemetery. Aerial imagery was collected of the entire cemetery as well. 

While the class was able to gather data about many of the graves at Hadleyville Cemetery, there were still many unknowns.  For example, type of stone was not recorded for many plots.  Since each student collected data slightly differently, it was difficult to consolidate the data into a cohesive spreadsheet for all to use.  Despite this, the maps that the students in this Field Methods class created will be useful to government officials in providing basic information about the cemetery.  At the very least, if anyone wanted to created a more detailed and accurate map, they could follow the methodology outlined in this blog to do so.  Furthermore, the city could take this data and incorporate it into an online GIS platform, allowing photos to be integrated more easily.