Field Activity 5: Sandbox Survey Part 2, Visualizing and Refining a Terrain Survey

Introduction

                Prior to this lab each group was to create a terrain inside of a sandbox of about one square meter (Figure 1), and then tasked to come up with a sampling scheme and technique for the creation of a digital elevation surface. Due to the small size of the study area, the grid scheme consisted of 6 X 6 cm squares and systematic point sampling was used to gather elevation data from the X, Y intersections of each square. In cases of sharp change in elevation within one square two measurements were taken in one square, giving the X or Y a 0.5 decimal place in the coordinating location. Data was manually recorded on a hand drawn grid matching that of the one created over the sandbox. This method organized the data and assisted in data normalization as it was entered into an excel spreadsheet. Data normalization involves the input of data into a database so that all related data is stored together, without any redundancy. Data for this excel file was set to numeric, double to indicate the decimal values and keep all data in the same format. X, Y, and Z data for each grid was entered beginning at the first X column from bottom to top and so forth, this pattern eliminated redundancy and kept all data inside one excel file. In total; 434 data points were collected in a relatively uniform pattern throughout the entire study area, with some clustering of points in areas with rapid change in elevation. These data points can be imported into ArcGIS or Arc Scene, where interpolation can mathematically create a visual of the entire terrain based on the know values.

Figure 1

Methods

                To bring this process a geodatabase was created and the excel file, which has been set to a numerical data format, is imported into the database, brought into ArcMap as X, Y data, and then converted into a point feature class. After a point feature class has been made the data can be used to create a continuous surface using interpolation methods. Four raster interpolations and one done in a vector environment are experimented with to determine the ideal representation of the terrain. The four raster operations are: Inverse Distance Weighted (IDW), natural neighbors, kriging, and spline. The interpolation done in a vector environment is called TIN.

IDW and spline methods are deterministic methods, they are based directly on a set of surrounding points or mathematical calculation to produce a smooth continuous surface. IDW interpolation averages the values of known points in the area of the cells being calculated to give them values based on proximity to the know values; cells closer to known points are more influenced by the average than those farther away. Influence of known points can be controlled by defining power, a larger power will place more emphasis on near points and result in a more detailed map, while a lower power will place emphasis on distant points as well to produce a smoother map. Produced values can also be varied by manipulation of the search radius to control for distance of points from the cell being calculated. The IDW interpolation method would not be advantageous for random sampling methods, areas with a higher density of samples would be more well represented than areas with fewer points. Spline uses a mathematical function and the resulting surface passes exactly through each sample point, making it ideal an ideal method for large samples. The function used creates a smooth transition from one point to the next and is ideal for interpolation of gradually changing surfaces such as; elevation, rain fall levels, and pollution concentration. Since this method passes through each sample point to create the resulting surface, it is not ideal for sampling techniques that result in clustered areas or fewer sample points in proportion to the entire area, such as random and/or stratified.

Kriging is a multi-step, geostatistical method of interpolation that creates a prediction of a surface based on sample points and also produce a measure of accuracy of those predictions. It assumes a correlation between points based on distance and direction between them. Kriging is similar to IDW in the fact that it weights points in a certain vicinity but it goes beyond IDW and takes spatial arrangement of the sample points into account, in a process called variography. While kriging goes a step beyond IDW to create a more accurate portrayal of a surface, like IDW it is not an advantageous method for sampling techniques that have resulted in clusters of sample points, such as random sampling.

Natural neighbor interpolation utilizes a subdivision of known points closest to the unknown point to interpolate and does not deduce patterns from the subdivision data. Hence if the entered sample does not indicate the pattern of a ridge, valley, etc.; it will not create this feature. Natural neighbor interpolation is advantageous for samples that have been collected using a stratified sampling method due to subdivisions within the study area.  However random sampling may result in poor representation of a specific surface and thus natural neighbor would produce a poor representation of the surface as well.

TIN, or triangulated irregular network, uses an algorithm to select sample points in the form triangles and then creates circles around each triangle throughout the entire surface. The intersections of these are used to create a series of the smallest possible, non-overlapping triangles. The triangle`s edges cause discontinuous slopes throughout the produced surface, which may result in a jagged appearance and TIN is not an advisable interpolation method for areas away from sample points.

Given the comprehensive and uniform collection of sample points in the terrain, each of these methods should produce a relatively accurate portrayal of the terrain. However, the areas with more densely collected data may result in over-representation in those areas in all interpolation methods except spline. Because of the fact that spline interpolation passes the surface though each data point and produces a smoothed surface in-between the points. Further reading in ArcGIS help indicates that spline is also ideal for areas with large numbers of sample points and gradually changing surfaces, like elevation. This indicated it would be the most ideal method for the survey.

Results/Discussion

                Prior to this activity, it was necessary to create a sandbox terrain and spatially sample it for elevation values. The entire terrain was slightly larger than one square meter and a systematic point sampling of the entire terrain was feasible. Systematic sampling done within an X, Y plane consisting of 361, 6² centimeters, quadrates resulted in an excel spreadsheet with 434 data points (Link to excel file). After the excel file was imported into ArcGIS as X, Y data and converted into a point feature class (Figure 2A), it produced a collection of sample points that were uniform throughout most of the terrain, with areas of sharp change in elevation more densely sampled. Each of the five interpolation methods described above were applied to the feature class to create different continuous elevation surfaces and each analyzed for best fit for the survey.

For each method, the elevation from surfaces option was set to floating on a custom surface with the custom surface being set to the corresponding surface being interpolated and each was set to shade areal features relative to the scene`s light position. Furthermore, other than the TIN method, each scene was set to a stretched symbology with the same color ramp. The TIN method only offers elevation symbology and did not offer the same color ramp.  All First, IDW resulted in elevation changes that were relatively smooth and accurate but the image appeared “dimply”, similar to that of a zoomed in view of a golf ball in almost the entire image (Figure 3A). Kriging also accurately represented elevation, but it appeared very geometric. It would most likely appear similar to a fractal if each shape within the image were colored in differently (Figure 3B). The natural neighbor method is smooth in most places, but edges are rough and smaller elevation changes are less pronounced (Figure 3C). TIN interpolation produced a very detailed image, however it is very “blocky” with angular curvature instead of a smooth surface appearance (Figure 3D). Finally, the spline interpolation method produced smooth elevation transitions, with the most pronounced representation of different elevations throughout the terrain. Based on these results, the spline method appeared to be the interpolation most appropriate for the survey taken (Figure 2B).

Before applying the interpolation methods and having only researched sampling techniques and some about the first geography law; it seemed the survey performed may have been slightly excessive. However, on the other hand, a larger survey is ideal and the time taken to perform the survey was not all that long. After experimenting with and reading about each interpolation method, it can be concluded that the sampling technique and grid scheme was not too excessive. The results from each interpolation method captured most details of the original terrain, and it was clear what was being portrayed in each image.

Figure 2

Figure 3

Summary/Conclusions

                Field based surveys collect essential spatial data needed to determine the relative data of unknown areas within proximity to the collected data, in order to establish an acceptable representation of the entire study area. This activity illustrated the basics of the survey process on a much smaller scale than usual. Like this survey, surveyors need to decide which sampling technique and tools are most suitable for the task. However, the tools used in most field-based studies are go beyond string and thumbtacks, some of the tools used include GPS receivers, total stations, 3-D scanners, and UAVs. In larger survey areas, surveyors must also consider temporal aspects. If a survey is to be done as a follow up to a previous one, the surveyors need to consider whether it should be done in the same temporal situation as the previous survey. Alternatively, if a new survey is to be conducted, the surveyors need to determine the best temporal situation for the survey. The detail given to this small scale survey is not always a feasible option, resulting in a different collection of sample points. This is where the other interpolations would yield a more accurate representation of the survey in question.

                Interpolation is a useful tool for visualization of many things beyond elevation. Other gradually changing phenomena’s such as water table levels and rain fall can be interpolated or demographic data, such as a survey of HIV distribution in Africa. Ecological surveys, such as forest type could also be interpolated. Interpolation methods are crucial in many fields for the extraction of important information otherwise extremely difficult or impossible to survey completely.

Field Activity 4:Sandbox Survery;Creation of a Digital Elevation Surface using critical thinking skills and improvised survey techniques

Introduction

Waldo Tobler`s First Law of Geography states that; “Everything is related to everything else, but near things are more related than distant things” (Esri GIS Dictionary). This statement is the basis of sampling; data collection of a representative population or sample area used to investigate a whole population. Well-chosen spatial samples can be used to create an acceptable description of the Earth`s surface. There are many factors that must be taken into consideration in order to create a well-chosen spatial sample. Some of these considerations include sample size; larger samples tend to be more representative of the whole, how to minimize bias when sampling, and which sampling technique is most appropriate for the area being sampled. A common scheme for spatial sampling is based on points within a grid framework and there are three primary sampling techniques; random, systematic and stratified. Each sampling technique has benefits and disadvantages. Both random and systematic sampling can be sub-classified into point, line and area sampling. Point sampling involves data collection at x, y intersections or in the center of the grid correlating to an x, y intersection, line sampling involves data collection at points along a line, and area sampling involves data collection within grid squares. Random sampling removes bias but can result in a poor representative population due to clustering of sample points. Systematic sampling involves evenly distributed points however, bias can lead to over or under-representation of a certain pattern. Some study areas have a know proportion of specific subdivisions, in these cases stratified sampling would be the best technique. Stratified sampling would evenly distribute sample points taken in proportion to each subdivision and the samples taken in each subdivision could be taken randomly or systematically. The goal of this activity is to create a terrain in an approximately one square meter area and then determine the most fitting sampling scheme and technique to obtain sufficient data for the creation of an accurate digital elevation surface of the terrain.

Methods

                It was decided that systematic point sampling, recording elevation (Z) at each  X, Y intersection was the most appropriate for this project; as the whole study area was only slightly larger than a square meter and the entire study area could be sampled efficiently in a relatively short time frame. A similar alternative to this approach would have been to record Z at the center of each grid square, however in some grid squares a sharp change in elevation near the center would make it difficult to determine which measurement to take. The stream box used was 114cm x 114cm, from these measurements it was determined that a grid with 6cm x 6cm squares would be ideal to capture sufficient data points for mapping. These divisions would result in 361 squares measuring 36 cm² each.  Meter sticks were used as guides and thumbtacks were used to mark each 6cm point on all four sides (figure 1) and string was wrapped around them to form a physical grid (figure 2) to ensure accurate location of the Z points recorded. The top of the stream box was made to be sea level, since the string grid was at the same level as the top of the stream box and the grid could also serve as a guide for sea level during data collection. Where the terrain exceeded sea level, the string was pulled into the terrain without damaging the structures so that all string sat at sea level. After the grid system had been completed over the terrain an origin was set at the southeast corner of the stream box. To facilitate data collection a similar grid of smaller proportions was drawn on paper and the Z value for each X, Y coordinate recorded in its corresponding box. In cases where the terrain in an individual grid square changed sharply, two measurements were taken and depending on which plane the change happened along the X or Y value was given a decimal value ending in .5 and then a whole number. The collected data was then entered into an excel spreadsheet, beginning at the origin and working up each Y column along the X axis. 

figure 2

figure 1

Results/Discussion

                After grid squares with sharp contrasts in elevation were split into two separate Z values the final number of data points collected was 434. The Z value`s minimum was -13, maximum was 10, the mean was ~-2.151, and standard deviation was ~4.146. The X and Y values both ranged from 1 to 19. The sampling method chosen at the beginning of this project and ultimately utilized for data collection appears to have been the best method however fewer points could have been collected. The primary issue turned out to be the grid squares with sharp changes in elevation; this was corrected through the collection of two Z values within those grids and depending on which plane the change happened along the X or Y value was given a decimal value ending in .5 and then a whole number. Another issue was the quantity of data points to be collected. The process was expedited through the delegation of tasks; one person held the measuring stick, another read aloud the value, and a third recorded the data.

Conclusions

                The sampling technique used in this project was slightly tedious but a majority of the points taken represent a 36 cm² section of the whole study area and the even distribution of points will give an unbiased, more accurate representation of the entire study area. Sampling is an effective way to create a reasonably accurate portrayal of spatial features on the Earth`s surface because surfaces that are closer together tend to be related to one another and data about a surface can be used to predict the nature of another surface that is close by. The activity of sampling a surface created in a small area provides an experience that would help with spatial sampling on a larger scale; it touches on the types of problems that could be encountered and the methods that can be used to solve these problems. After learning about the First Law of Geography, it seems the resulting dataset from this project was slightly excessive and fewer data points could have been collected in order to recreate the surface accurately. At the same time, the collection of data point was not costly and should result in a more accurate portrayal of the terrain that was created. 

Hadleyville Cemetery Part 3

Introduction

The town of Pleasant Valley would like to preserve some of the town`s history as well as prevent the sale of an already occupied plot via the development of a detailed and accurate map of the Hadleyville cemetery. This project comes with a few obstacles; all original records and maps have been lost, the monuments date back to 1865 making them difficult or impossible to read, and some burial plots are not marked at all. A simple map could be created to indicate location of marked plots within the cemetery, however this would not include any attribute information correlating to the individual plots. A GIS would include such attribute information as well as the spatial data of the cemetery, this would more comprehensively preserve information about the cemetery. A UAS capable of stable, high resolution photography was programmed to take a collection of aerial images of the cemetery in order to produce an optimal base map. The class used notebooks to record all observable attribute data pertaining to each individual burial plot and cellular phones to photograph the same data that was recorded manually to ensure accuracy.  This method of field data collection can be used to create a base map in a GIS and then digitize plots with associated attribute data included.

Study Area

                Pleasant Valley`s Hadleyville cemetery is located in west central Wisconsin, in Eau Claire county. The town of Pleasant Valley is in the southwest corner of the county (figure 1). Data was collected on a warm, sunny afternoon in early fall.

 

Figure 1

Methods

                  A UAS from the Phantom series was used for the aerial imagery. According to www.dji.com , the entire Phantom series contains a gimbal with 3-axis stabilization capabilities and are all created for high-level aerial photography. The class used notebooks to record data and cellular phones to photograph the same data that was recorded manually. The use of notebooks provides a hardcopy backup of the digitally collected data. The data recorded in notebooks is also data gathered by the human eye, which provides a different perception of the data as it is collected and can be used together with photographic images to ensure accurate interpretation. The use of a survey grade GPS receiver was attempted, however a complete list of points would have been extremely time consuming and a signal was not found when under trees. This GPS receiver was selected over a mapping grade receiver because the mapping grade GPS is generally accurate to within a meter, many plots are closer together than this. In order to consolidate all collected data and include all the same attributes for each section of attributes, an excel spreadsheet was created in a public Google doc. A cemetery grid pattern with lettered columns and numbered rows was created overtop a base map to create point IDs for each plot and to ensure accuracy of attribute input into the excel file from each contributing member. A primary attribute to be included was the name of the person occupying the plot and initially the presence of joint tombstones caused the problem of having more than one name attached to one point ID. This was solved by entering each name as a separate point ID and the inclusion of a notes field to denote such cases. When the excel file was complete it was saved as a csv file to be imported into ArcGIS. The GPS accuracy of the UAS enabled it to obtain a clear, centered image of the entire cemetery and a moderate pixel resolution allowed for zoom levels close enough to verify the presence of a difficult to view monument as well as for the optimal placement of two points on one monument. While digitizing points, the point ID attribute was also entered and the completed csv formatted table of attributes was brought into the layer, joined to the table of point IDs associated to the newly digitized points, and then exported as a new layer to make the join permanent (figure 2). From this result a layout was created with symbology representing alphabetical groupings of the last names (figure 3), which would make the location of a loved one easier for visitors.

Results/ Discussion

                The consolidation of data into a communal excel file containing normalized data fields greatly improved the efficiency of the creation of a GIS. This task would have otherwise been much more complicating due to the fact that data collection was completed by numerous groups and in different manners. Had the survey grade GPS been used to obtain a complete list of coordinates for every plot in the cemetery instead of manual data collection based off a basic diagram, this project would have taken much longer still. There is still room for error given the lack of a GPR system to located unmarked burial plots, however visual observation provided confirmation that plots lacking a tombstone tended to have some sort of indication that it was an occupied plot. Data collection could have been improved if a normalized data list had been created and a basic diagram had been drawn up before collection began and teams assigned to specific areas of the diagram for data collection. This would have reduced the initial confusion between groups and reduced the odds of missing data.

Figure 2

Figure 3

Conclusion

                Overall the use of digital and observational field data collection secured a successful final project. Each method of data collection contributed to the collection all data necessary to meet the goal of this project, to create a detailed and accurate map of the Hadleyville Cemetery. None of the data collection formats used could have singularly collected all that was necessary to create the GIS. There were a few broken monuments piled along the edge of the cemetery, missing information associated with a few plots, and there is a slim possibility that time has erased any evidence of an occupied plot. However, prior to this project the town of Pleasant Valley had no maps or records of the Hadleyville cemetery and this project has resulted in the identification of 158 occupants within this one and half acre cemetery. The resulting project can be edited in ArcGIS to digitize in any future additions or update information of existing data; it can also be used to create a web map online for interactive use by the public. 

Hadleyville Cemetery Part 2

·         Introduction

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

§   The town of Pleasant Valley would like a detailed, accurate map of the Hadleyville Cemetery; however, all maps and original records have been lost. The cemetery also has monuments dating as far back as 1865, many of which are likely made of limestone or marble. These materials tend to degrade over time and many of the engravings will be difficult or impossible to read. Burial plots with no remaining monument marking them will also add to the challenge of creating a complete collection of attributes for each plot.

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

§  A GIS will attach the collected attribute data to a specific geographical location within the cemetery; this method will make utilization of the data much more user friendly and preserve the data more extensively. Factors such as weather and vandalism could result in loss or relocation of a monument. A GIS will have the data attached to the location of the plot, making it much easier to replace the monument and to verify occupancy of a particular plot for future additions. In the past cemeteries have used identification numbers for plots and visitors were given the ID number corresponding to their loved one, as well as a general area within the cemetery the plot could be located. This method can make locating a plot somewhat difficult and time consuming, particularly for nonexistent or illegible monuments and/or those who have never visited the cemetery.  If made accessible to the public, a GIS would also make it easier for visitors to locate their loved ones. Users could access a high resolution image of the cemetery and locate the plot with data for their loved one, the predominance of smart phones also adds to the convenience of this method.

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

§  In order to obtain base map imagery a UAS was programmed to execute a series of flights around the cemetery. The attribute data will be collected using a field notebook and photography; in a field notebook a basic layout map will be drawn with each plot numbered and attribute data corresponding to each plot recorded. As the attribute data is collected each monument will also be photographed using a cell phone or digital camera. In order to obtain very precise locations of each plot, their locations will be recorded using a survey grade GPS.

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

§  A detailed image of the cemetery to use for the base map would make it much easier for one to orient themselves and successfully locate a specific plot while in the cemetery itself or from another location, using a UAS with high resolution photography capabilities will make this possible. The basic layout with a numbering system used to designate attribute data to a plot along with photographs will help ensure accuracy of the attribute data collected. The photographs could also be potentially used as a pop-up image when the corresponding plot is clicked on by a user viewing the map, if a web map is created.  

·         Methods

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

§  A UAS from the Phantom series was used for the aerial imagery. According to www.dji.com , the entire Phantom series contains a gimbal with 3-axis stabilization capabilities and are all created for high-level aerial photography. The class used notebooks to record data and cellular phones to photograph the same data that was recorded manually, this will help to ensure accuracy. The photographs will be useful in assessing any un-noted or un-clear data. The use of a survey grade GPS receiver was attempted, however a complete list of points would have been extremely time consuming and a signal was not found when under trees. This GPS receiver was selected over a mapping grade receiver because the mapping grade GPS is generally accurate to within a meter, many plots are closer together than this.

o   What is the accuracy of each piece of equipment you are intending to use?

§  According to an article posted by Mike Tully at http://www.mapps.org/blogpost/726441/237476/Just-How-Accurate-is-Your-Drone , a combination of factors contribute to the overall positional accuracy of the orthophoto produced. Some of these include; the camera`s potential, the stability of the flight, the quality of the GPS data, and the number and quality of ground control points. Based on these factors alone, the Phantom appears capable of producing accurate imagery for the project: The UAS Phantom series boasts high quality and well stabilized cameras, the flights were made on a day with little wind and appeared to be stable, the cemetery itself is large enough that GPS data should be sufficient for locating it, the six ground control points used in the 1.5-acre cemetery were sufficient and well-marked with bright colors. Had the survey grade GPS been successfully used, they are accurate up to a centimeter. This accuracy would avoid overlap in points that could have occurred with a mapping grade GPS.

o   How was the 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?

§  The aerial imagery was recorded digitally using a camera, this was necessary for a detailed base map. The attribute data was recorded manually in a notebook, but also documented using the camera of a cell phone. Data empirically collected in-situ tends to be the most accurate and a hardcopy of attributes helps to ensure the data is not lost.

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

§  Once the base map has been created and properly projected, location points will be added for each plot on the map. An excel table of all attribute data as well as coordinate information designated by the points will then be created, the table will be converted into a csv files, and then imported into a GIS by linking it to the coordinate points.

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

§  There was only one survey grade GPS unit available for use and data collection was taking a very long time. The unit also did not maintain service when under trees. Due to these difficulties the collected coordinates were discarded and the use of the equipment eliminated from the project. The clarity of the aerial imagery combined with the detailed data collected will make completion of this project possible without GPS coordinates for each plot.

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

§  The collection of GPS data could have been successful if at least two were available and they had stronger receivers. There were also some large gaps in between graves as well as some monuments piled along the edges of the cemetery, indicating the presence of unmarked plots. GPR equipment could have most likely shed some light on the locations of unmarked, occupied plots.

·         Conclusion

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

§    The use of a UAS to take aerial imagery will produce a detailed and accurate base map. The collection of as many observable attributes as possible as well as digital photographs of each site where data was observed will enhance the detail of the finished product.

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

§  Each method of data collection contributed to successfully collecting all data necessary to meet the goal of this project, to create a detailed and accurate map of the Hadleyville Cemetery. If only the UAS was used, it would have taken a very long time for someone to manually drive it to each plot site and take an up close photograph, on the other hand no selfie stick would have been long enough to obtain the quality of aerial imagery we were able to obtain using the UAS for the base map. Photographs taken during empirical data collection created a backup for any questionable data collected.

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

§  Before setting out to gather data, the class as a whole did not communicate methods of collection or assign groups to areas. However, after separating and realizing this small groups did communicate and then other small groups communicated further with other groups. This survey method may result in some overlap in data collection and perhaps a few gaps, but it is likely that very close to all plots were observed and accounted for. If there is any overlap, that data would either match or be looked at more closely, both resulting in better accuracy.  

Hadleyville Cemetery Part 1

·      Introduction

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

§  The town of Pleasant Valle would like a detailed, accurate map of the Hadleyville Cemetery; however, all maps and original records have been lost. The cemetery also has monuments dating as far back as 1865, many of which are likely made of limestone or marble. These materials tend to degrade over time and many of the engravings will be difficult or impossible to read. Burial plots without any identification will also add to the challenge of creating a complete collection of attributes for each plot.

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

§  In creating an updated and accurate map for the cemetery the original records and maps would be very useful in identifying unmarked burial plots, which would not only assist in the development of complete records but also help to eliminate the sale of an already occupied plot. A monument that has been left un-replaced most likely indicates the lack of any loved ones left in the area, making identification very difficult.

o   How will GIS provide a solution to this problem?

§  GIS is capable of storing the necessary information associated with the spatial data included within the map, which will fulfill the request of a detailed map. ArcGIS online can then be used to publish the results as a web map with all the associated records for each plot accessible by simply clicking on a plot. The web map can also be shared in various manners, which should greatly reduce the odds of the information being lost again.

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

§  The Town of Pleasant Valley has requested a detailed map. They would not only like an accurate map of burial plot locations but also attribute information for each plot and a GIS is needed to store the attributes associated with each plot.

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

§  A UAV will be used to obtain a current, high resolution aerial image of the cemetery, a survey grade GPS will be used to collect coordinates of each plot, and a field notebook will be used to record a predetermined list of observable attribute information about each point recorded. The predetermined list will include; name, sex, birth date, date of passing, head stone condition, and headstone material. GPR equipment would be useful in determining whether or not any unmarked areas are occupied and in surveying the outskirts of the cemetery to define boundaries and ensure no occupied areas are missed. Paint would be used to outline unmarked plots and flags will be used to label the GPS assigned point number for each unmarked plot.

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

§  The biggest drawback would be lack of information. Given that all original records have been lost, it will be very difficult to obtain attribute information about the burial plots without monuments or illegible monuments. The age of some of the plots could also be a drawback; according to Geomodel, Inc., GPR equipment has more difficulty detecting burials that are older than 50 years. The presence of rocks and tree roots can also impede upon the ability of GPR equipment to detect burial remains. A possible remedy to these issues is that it may be possible in situations where a casket no longer remains for GPR data to be analyzed for trenches or disturbed soil as an indication of burial. Another drawback could be obtaining accurate attribute data for each plot; this however can be remedied with specific, simplified language for information to be collected and sufficient communication concerning method of collection. Despite the lack of original data, the UAV will be able to provide a very accurate image of the current state of the cemetery, the survey grade GPS will be able to generate very accurate coordinates for each plot, careful field observation can help to obtain a complete as possible attribute table, and finally GPR equipment could prove to be of great value in the prevention of the sale of an already occupied plot as well as delimiting the current boundaries of the cemetery.

·      Methods

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

§  Each headstone will be marked by a point on a survey grade GPS unit. The attributes associated with each occupied plot will be recorded in a field notebook. From here the data collected will be transferred into a table, and then linked to the GPS points. A UAV equipped with a high resolution camera will take a photo of the entire cemetery; this photo will be used as a base map for the GPS points and the connected attributes. From here, the hope is to make an interactive map by linking the GPS points and associated attributes to the high resolution base map, so it is usable by the county officials or anyone who does not have any form of training in GIS. The idea is to make the final product as user friendly as is possible by creating an interactive web map for the county to use.

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

§  The GPS unit will be a survey grade unit, which is accurate up to a centimeter, to prevent graves from being inaccurately marked. The UAV will have a very high resolution camera and the ability to collect data from various altitudes in order to obtain an optimal image. The accuracy of humans collecting data will depend on pre-organization and planning as well as the individual`s ability to collect data. The GPR equipment should be able to obtain information from depths of at least 8 feet below ground and accuracy will be affected by soil type and age of the burial.

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

§  In the field, the data will be recorded as points on a GPS unit with the associated attributes being written in a field notebook. Once the necessary data has been collected, the GPS points will be uploaded into a GIS, and the attributes entered into a table. This method has been chosen because it is the most direct method of data collection.

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

§  Each attribute will be entered into an excel table, converted into a csv file, and imported into a GIS by linking it to the GPS points.

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

§  There are several opportunities for human error while collecting the data, but this method is a simple and efficient way of providing the county with the needed data. The chance of human error can also be significantly reduced by the formation of a well thought out, detailed method for attribute data collection and double checking every entry. Because the scale of the project is fairly small, any errors in data should be easily corrected.

·      Conclusion

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

§  The intent is to create an interactive web map that can be used by anyone regardless of the amount of GIS training. The methods outlined here will provide just that. By linking the GPS points to the attributes and then using an accurate base map to show the current condition of the cemetery, any person with an internet connection, and some computer literacy, should be able to click any point on the map to find out if the plot is occupied, and in many cases by whom. This would also help to prevent any future loss of records.