Field Activity #6: Conducting a distince Azimuth Survey

Introduction

The purpose of the lab was to learn a new method of surveying known as distance azimuth. This method of data collection is a very low technological method. Much of surveying today is highly reliant on technology. the technological advances have had a positive impact but they come with a disadvantage of possibly malfunctioning while on site. If this were to happen methods, such as the distance azimuth survey method, would be beneficial to have experience with. Distance azimuth uses the concept of azimuth. Azimuth is the direction of a object in relation to a point. This is expressed in degrees. To gain experience with the method, the class was tasked with plotting the location of various tree types throughout a small area of Putnam Park located near campus(Figure 1). 

Figure 1: Location of Putnam park in relation to the University of Wisconsin- Eau Claire

Figure 2: The tools used to conduct the distance azimuth survey.
 A) garmin handheld GPs unit, B) Compass, C) laser distance finder 

Methods

Prior to beginning the survey research was done to avoid as much error as possible. The blog's of previous classes were studied. This allowed the class to learn from the mistakes. The tools to be used to conduct the survey included a  GPS unit, compass, and laser distance finder (Figure 2). Manual notes had to be taken as well so a notebook and writing utensil were necessary as well. The uses of each compass will be described in the data collection section.

      Data Collection

The first step necessary during data collection is to determine a point of origin. When dealing with azimuth, there needs to be one point that every other feature is measured from. In this case, the point was determined and marked using the handheld GPS. This will help when the data gets imported into ArcMap and will be georeferenced. The next step was to then determine the various trees in the vicinity, and get the distance and direction in degrees from the point of origin of each one. The information recorded manually on a piece of paper included; Distance,Azimuth, Tree type, and Diameter. There was a total of 30 different data points collected over the course of the lab.    

      

      Data Normalization

The 30 points were composed in an Excel file and put into the class discussion forum for everyone to use.  Here, the data was organized in a manner that would allow the information to be uploaded into ArcMap. This processes is called "normalizing" data. The final result of the excel file was a column for X, Y, Distance, Azimuthm Distance, Tree type, and Point number (Figure 3).

Results

The results form the lab was the following excel table. This table provided the information that has been used in ArcMap to show the location of various trees throughout Putnam Park.

Figure 3: The final normalized excel file with the various data point information. 

Conclusion

The surveying method used throughout the lab, distance azimuth, is a very low tech method. However, it is a very important method to learn because technology is not always reliable or accurate. the results form this lab will be used in a future lab where a map of the various tree types is created.

Field Activity 5: Visualizing and Refining Terrain Data

Introduction

Table 1: Snip from the Excel file used to input
elevation. Shows normalized data

The previous lab involved the creation of a landscape and the collection of the landscape’s elevation data. the purpose of the lab was to gain an understanding of sampling and the various methods of sampling. The data collected was put into an excel file which was normalized to be compatible with ArcMap. This meant there was only a X, Y, and Z column (Table 1).


The data points of the landscape were collected every 5 cm for the areas that had a lot of elevation change (hill, depression, ridge, valley) and every 10 cm for the areas of little change (plain) (Figure 1). These points will be plotted in ArcMap to be interpolated and transferred into a 3D visualizing software, ArcScene. Interpolation is the calculation method used to create new values for points within a known range. This allows for a more detailed data set and therefore high accuracy. The purpose of the lab is to create a DEM model of the landscape and compare the various interpolation methods including inverse distance weighted (IDW), Natural Neighbors, Kriging, Spline, TIN. 

Figure 1: The landscape created in the sandbox during the previous lab.
The needed features have been identified.

Methods

Figure 2: The data points after being added into ArcMap.
        There has been axes and the starting points
added to provide correct orientation. 

Before beginning the processing on the data points a folder was created along with a geodatabase (“sandbox”), both of which were dedicated to this individual lab to keep the files organized. The excel file created in the previous lab (refer back to Table 1) was imported into the sandbox geodatabase. To add the data points into ArcMap, the XY data was added via the imported excel file. The layer was exported as a point feature class (Figure 2).

Interpolation must be done in order create an accurate 3D visualization. In order to do so, the extensions had to be set to 3D Analyst. The feature class was then added as the input file for the first interpolation tool, IDW. This process was repeated with the remaining interpolation methods (Table 2). 

Once the interpolation was finished, the data was then exported to ArcScene for each individual feature class.  The scale had to be adjusted to the calculated range in order to portray the small area as a more accurate representation of the landscape. The orientation of the imagery is with the (0,0) point being in the lower left corner of the landscape (Figure 4). The various results are discussed further in the “Results” section. 

Table 2: The table provides a brief description of the various interpolation methods used to create DEMs of the landscape.

Results/Discussion

The overall results of the interpolation methods used: IDW, Natural Neighbors, Kriging, Spline, TIN; show a range of advantages and disadvantages. Some of the methods resulted in similar representations of the landscape created in the previous lab. Each individual method will be analyzed further.

Inverse Distance Weighted (IDW)

Figure 3: IDW interpolation result

The IDW interpolation result shows the landscape and the features needed (Figure 3). However, when analyzing the image, it is clearly interpolated by creating clusters of data points weighted heavier at certain points. However, if you compare Figure 3 to Figure 1 it is easy to point out the landscape features. A disadvantage of the IDW interpolation method is that it is not a smooth topographic representation of the landscape created in the previous lab. 

Natural Neighbors

Figure 4: Natural Neighbors interpolation result

The results of the Natural Neighbors interpolation method provided an accurate representation of the landscape created (refer to Figure 1) (Figure 4). An advantage of this interpolation method is the fact that creates a smooth surface when compared to the previous method, IDW. The extreme elevations are very clear within the image and therefore the landscape features are easy to identify. The valley leading into the low area surrounding the hill is easy to identify. The area near the starting point (0,0) is higher than the plain, could be referred to as an unintentional plateau. However, in the areas with little elevation change it is difficult to see the detail of the landscape. 

Kriging

Figure 5: Kriging interpolation result

The Kriging method of interpolation resulted in a very similar image to the previous method of Nearest Neighbor. The landscape shows the surface features of the grid (Figure 5). The ridge near the upper left portion of the landscape appears to be very broken up and not of high elevation as expected. The feature is not as defined as it appeared to be in the actual landscape (refer back to Figure 1).  An advantage of the method is a smoothed surface and transition. A disadvantage of this method is that there is not much detail in the image in the plain areas. The plain, though it had little change, still had some elevation change which did not appear in the Kriging method results. 

Spline

Figure 6: spline interpolation result

The Spline method of interpolation had a large effect on the intensity of the elevation change (Figure 6). This is a major advantage of using this interpolation method. One main aspect that is better with this method is the ability to see even the small elevation changed in the areas of the plain and plateau. It also represents the highest elevation well. The transitions from one elevation range to another is very smooth which allows for a more appealing DEM. 

Tin

Figure 7: TIN interpolation result

TIN interpolation was the one method that visually stood apart from the other (Figure 7). An advantage of the TIN method is that it shows the elevation extremes. The highest points, at the peak of the hill and another hill in the lower right area of the grid, are a light grey (almost white) which is easy to identify. The lowest elevation within the depression and valleys are also a lighter color than the higher surrounding landscape. 

Summary/Conclusion

After analyzing the results of the various interpolation methods it is concluded that Spline does the best at representing all aspects of the landscapes topography. The Spline method showed smooth transitions between the elevation ranges but also intensified the little changes in the flatter areas. This method’s results looked most realistic to the original landscape and did the best to represent the sandbox landscape.

 The overall results of the images proved that there had more than enough data points input into ArcMap to accurately represent the landscape that had been created previously. This means that there did not need to be a repeat of the lab to collect more points. The only errors that may have influenced the data is human error while relaying data vocally.

This survey provided experience with the advantages of sampling but also making sure to collect enough data to create a full picture of the survey area. This was the most important aspect of this lab when compared to others thus far. However, it is always not needed to collect many points in certain regions. As seen in this lab, with areas of little change there was not much need to collect many points because they were all similar and could be represented as so with few points. That being stated, areas of high amounts of change results in the need for more points for detail. 

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

Introduction

Sampling is a technique used to investigate a population by gathering data from a small portion of the entirety. Sampling is used to save both time and money and is used for many studies. Sampling provides an overall look at the spatial variations of a phenomena within a study site. There are three sampling methods; 1) random ,2) systematic, 3) stratified. The objective of the lab was to create a landscape containing a wide range of elevation and collect and record the elevation through systematic point sampling. This means that samples were collected evenly throughout the study area. In our case, the samples were in form of centimeters and represent elevation.

Methods 

               Figure 1: The landscape was created
with all of the parameters needed. 

The elevation of the landscape was collected by taking measurements through a systematic point sampling technique. In order to accurately portray the topography of the landscape measurements had to be taken fairly often at a regular interval. The landscape was created in a 45 by 45 in  (114 by 114 cm) sandbox located east of Philips Hill. Designing the landscape involved several parameters. It was required for the landscape to have a hill, ridge, valley, depression, and a plain. Creation of the landscape was done by hand (Figure 1). Materials given to us was string, wall tacs, tape, measuring tape, and a meter stick. Using the wall tacs and meter stick, every 5 cm was marked on all 4 sides of the sandbox. After placement of the tacs, the string was used to create a grid over the top of the landscape (Figure 2). Our sea level or zero elevation was the actual ground. This was chosen to ensure that negative values would not be measured.

         Figure 2: The string was wrapped around the
 tacs to create a grid over the landscape

To collect the elevation points a metal hanger was straightened and stuck into the ground within one of the grid squares (Figure 3). Once ground was met, the hanger was taken out and placed against a meter stick to read the measurement in centimeters (Figure 4).

Figure 3: The grid helped keep the measurements
 organized and at set increments of 5 cm

Figure 4: Elevation collection

The elevation points were placed in an excel file with 3 columns (Figure 5). The grid had a column for X, Y, and Z so that each point could be plotted on a grid with the elevation. This will help in later visualization of the landscape within ArcMap.

Figure 5: Excel table showing set
 up of elevation measurements 

Results

The overall table consisted of over 400 sample points taken of the to show the elevation change (relief) of the landscape we created. The excel table is the most important result of the project. Using the correct formatting that can be read in ArcMap, the goal for future work is to input the table to create a topography model of the landscape through various processing procedures.

After running statistical measurements of the Z column (which holds the elevation in cm) various statistics were found...
- Maximum elevation = 23.3 cm above sea level
- Minimum elevation = 6.6 cm above sea level
- Mode= 14 cm above sea level

These statistics show the wide range of elevation. the areas that were taken for the plain resulted in less sample values because of the extremely low elevation change. The mode shows that many of the areas was relatively high above sea level which could indicate thick lithosphere in the real world.

Conclusion

Gaining an understanding in sampling (in particular the systematic point sample method) both time and money can be saved. this was witnessed first hand during the collection of the data which lasted 4 hours.  To show even more detail, which might be needed, in areas of great elevation change the data points for that area can increase. The more data the more likely it will be accurately portrayed.  

Field Activity #3: Creation of a GIS for Hadleyville Cemetery using field data

Introduction 

The Hadleyville Cemetery is located in Eau Claire County off of County Road HH. This cemetery contains 120 lots with burials dated back to 1865 and is a total of 1.5 acres. The major challenge faced for the county is the lack of original records and maps of the cemetery. Other issues include the chemical weathering that has occurred to multiple gravestones, the removal of gravestones, and destruction to a large portion of multiple stones. Lack of identifying markings of grave sites is a major issue in the cemetery and creates a risk of disturbing a grave during a future burial. The overall goal of this project is to use various geospatial techniques to accurately map out the cemetery, particularly the occupied grave sites and create a detailed spreadsheet of important information regarding each grave.

The reason this will not just be a simple map or spreadsheet is because there is a lot of other information that should be known regarding the graves. Information can be stored in GIS and tied to a specific location and feature (in this case the feature is the grave sites) and used by management of the cemetery. This information will help in record keeping and buying and selling of plots within the Hadleyville Cemetery. As mentioned, the issues with the cemetery are mainly due to the lack of record and information about each grave. Applying attributes, such as name, date, and condition of gravestone will help solve this problem.

Information needed would be a highly accurate survey grade GPS system. The accuracy is important because the scale of the area is not very large and therefore accurate locations are critical to ensure that issues do not arise (such as the buying and selling of occupied graves or the accidental exhumation of a body). GPS combined with the study of aerial imagery of the area will allow the gravestones and obvious graves to be accurately mapped. As mentioned, a major issue is the absence or destruction of headstones.

Overall, the objective of using GPS along with aerial imagery would be to georeference the aerial image and locate the grave sites based on the image. Using the GPS, we would then be able to just upload the points into GIS and not have to manually place the grave site locations.

Study Area

As mentioned, the Hadleyville Cemetery is located in the city of Eleva, just south of Eau Claire, off of County Road HH (Figure 1). Overall, the cemetery has been maintained except the issue of the destruction of gravestones. This allowed for adequate data collection with the UAV imagery. The data was collected in the late summer and therefore the southwest and southeast corner of the cemetery has a large canopy covering several gravestones. This would not have been as much of an issue if the data was collected in the late fall but that was not the case. Initially the data was collected around 3:00pm which led to a large amount of shadow cast by the gravestone and surrounding trees. Data was then collected around noon the second time. This imagery was used in the map-making process. 

Figure 1: Hadleyville Cemetery location

Methods

As a class, we used various geospatial tools to mark the locations of grave sites. These tools include a survey grade GPS to pinpoint the exact location of the grave sites within the cemetery which allows for precise locations. Another tool used was a UAS (unmanned aerial system) drone. With this technology, the entirety of the cemetery was imaged. The accuracy of the GPS unit was very important due to the small area in question. However, the time allotted to data collection was limited and therefore created an issue. To solve this issue, a UAS was used to capture imagery of the cemetery. Other information necessary to create an accurate database was collected as well. Before collecting data in the field, a set of attributes for each grave site was determined. As a class, these attributes were put into a normalized excel sheet (figure2). The data was collected on pen and paper because it was more efficient. Information collected about the cemetery included first and foremost whether or not the grave stone was legible. Depending on the answer, the rest of the information was collect. This data included last and first name along with a middle initial,  year of birth and year of death, if the statue was standing (if left blank it indicates that the stone was flat and in the ground purposefully), type of stone (marker type), whether is was a joint tombstone and number of people, and finally any additional notes. The PointID field was determined based on the layout which is explained further on.  This data was then uploaded into an excel sheet on GoogleDocs. By putting these attributes into an excel a potential table join could be made within ArcMap. This means that all of the class data was formatted the same. While in the field, the cemetery was split up into lines in order to ensure that all areas were collected(Figure 3).

Figure 2: Excel sheet showing the normalized data collected by class

Figure 3: Hadleyville class grid pattern

Using ArcMap, the aerial imagery was manually analyzed and each grave was digitized. To do so, the Hadleyville imagery was uploaded to a geodatabase within ArcMap. The geodatabase, jackiecemetery, was a file geodatabase created for this project only. After the Phantom image (RGB bands 3,2,1) was imported, digitizing of the gravestones had to be done. Creating a new feature class within the geodatabase allowed for a new layer to be created and edited. The feature class, Graves, was added to the geodatabase and given the same projection as the image was uploaded with and editing began. The feature type selected was point. Within each point, two fields were created. One field was the PointID (text type) and the second was Image (raster type). The PointID for each grave had to match the PointID within the excel spreadsheet. By doing so, a table join was created and all of the other information regarding the specific graves were added to the attribute table within ArcMap. This saved time by uploading the information at once rather than each field by each grave. Also seen in the image was ground control points. These had to be digitized as well to ensure no confusion by viewers. The same process was followed.

Results/Discussion

The newly formatted data allowed for a table join which saved a lot of time in the map-making process within ArcMap. Performing a table join resulted in all of the data within the excel sheet to be added as attributes within ArcMap. The table join conducted was done so by the use of the common field "PointID". The extra attribute created titled image portrays that there was an attachment of an raster. This field holds the image of the gravestone embedded in the map. 

Figure 4: Attribute table from ArcMap showing a table join was completed

The PointID attribute is important for both the location and the connected attributes. The following map was created to show the location of each grave stone (Map1). As seen, the map closely resembles figure3 which can be found in the methods.

Map 1: Indicates the PointID for each grave

Another important attribute was the Last Name. this is important to know when dealing with families wanting to be buried together. Map 2 shows the last name of the individual buried at each specific grave site. As seen, many families have already began to be laid to rest in clusters together. 

Map 2: Map showing the last names of the individuals buried at each location

In many cases, the amount of individuals within a certain cemetery is misinterpreted based on the number of tombstones that are visual. However, with high costs for tombstones, it is becoming more popular to share on tombstone for several people. This not only ensures that the individuals are buried near each other but it also saves surface space within the cemetery and leaves room for more family members to be buried nearby as well. Map 3 indicates the 

Map 3: Map showing the location of the joint tombstones

Despite the success of the maps and the table join, there are many possible errors that may have occurred. For starters, human error is often likely when the work is split up. At the beginning of the project the class was at a loss on how to begin, how to split the cemetery into sections and label them. In the end, data was collected for the same graves several times and had to be filtered out. Another possible error occurs where there are tree coverage. The placement of the graves are estimates of the actual location. This can cause issues in the future because of the importance on the accurate locations of all of the graves. Discussing how to enter the project and set up data should have been determined prior to heading into the field which would have saved the step and stress of then figuring out all of the notes later on after familiarity of the project decreased. The issue regarding the survey grade GPS should have been thought of prior to wasting large amounts of the data collection time trying to 1)figure out the system though the experience was useful and 2) triangulate under cover. Several members of the class ended up having to return to the site to collect the remaining needed data.

Conclusion

The methods of this project allow for the completion of an accurate map containing pertinent information regarding the cemetery. through the use of GIS, GPS, and aerial imagery locations of grave sites can be mapped. This work can help future management of the cemetery and provide historic record of the area. Because the class collaborated, the majority of us had a similar format for how to collect data. However, there was a large amount of frustration toward the beginning while deciding the best way to go about note taking and formatting. Overall, the project provided the cemetery management with accurate location and information necessary for the cemetery records. The location of the grave stones are highly accurate and should provide valid coordinates of  inhabited graves.