Distance Azimuth Survey

Introduction:

This project includes the class splitting up into groups once again and each taking data from a center points to different types of trees surrounding that point. Each group needs to create a survey plot that is going to prove useful when technology fails and backups are needed. The study area is a dirt trail right in the middle of a forest/swam full of different types of trees. Below is figure 1, which represents the study area of where points were taken by groups through GPS devices and the azimuth distance of each point to the center point is calculated.

Figure 1: Yellow blocks represent where the data was collected,
it is located on a dirt trail behind Davies and Phillips at UWEC

The figure above represents the study areas where the data points are being collected, the study areas selected are located on Putnam trail, directly behind the Davies Center on the University of Wisconsin - Eau Claire campus. This area is very swampy which is a good place for a variety of different types of trees to grow. This study area was selected because of the large variety of trees found in the area to take azimuth directions on. Once the groups arrived to the study area, they split up to three separate center points where the longitude and latitude were taken for each of the center points, next was to take azimuth distances to different species of trees found nearby. The groups were assigned GPS units to record the location for each tree origin in the survey. Along with measuring the distance of each tree there were more attributes collected, the azimuth, tree type, and diameter. Once all of the data was collected the groups combined all of the data into an Excel spreadsheet so than it can be used in ArcMap. Below is figure 2, representing the Excel spreadsheet created containing all of the data.

Figure 2: Excel spreadsheet for the survey containing all of the attribute data for each data point collected

The data collected was the longitude and latitude of each center point, then the distance from that center point to the trees chosen in meters for the survey. Along with the azimuth from the center point in degrees, the diameter of the tree taken at chest height, the tree species, and the sample area number. Once all of the data was collected the groups combined the data then moved to mapping out the collected data using ArcMap.


Methods:

This survey contained many steps, all of which are included below;

Step 1:

Locate the study area. This area must contain a large amount of different tree types and can be easily located using technology such as google maps for data accuracy reasons.

Step 2:

Obtain the GPS devices and measuring tape from professor and identify center point from which all of the data will be obtained from. Making sure that data accuracy is kept in mind when performing these steps.

Step 3:
Select ten different trees for the survey recording all of the data for each attribute.

Step 4:
Use a compass to obtain the azimuth by directing the compass at the tree in question for the survey.

Step 5:
Use the distance device provided by the professor to obtain the distance from the center point to the tree in question.

Step 6:
Figure out what species the tree is by the features of the tree, for instance, leaf shape, color of bark, texture, and other physical characteristics to determine the species.

Step 7:
Use the measuring tape provided to determine the diameter of the tree at chest height.

Step 8:
All members of the group record information collected for each tree for the survey

Step 9:
Transfer all of the data collected from the notebook to an Excel spreadsheet and combine with the other groups to have more data overall.

Step 10:
Convert the Excel Spreadsheet into a table on ArcMap, then perform the 'Bearing Distance to Line Command' tool to draw out the lines from the center point to the trees surveyed.

Step 11:
Use the feature class produced from the last tool used and use the 'Feature Vertices to Points' tool to use points at the end of each line to represent the tree taken from the center point.

Step 12:
Create a map of the end result from the data collected.


Below is figure 3, which represents the final map of the data collected.

Figure 3: Final points from the data collected in the field, representing the trees surveyed
distance from the center point

In figure 3, each tree that was collected in the survey is represented by a green tree, and the distance from the center point are represented by red lines each representing a shorter or longer distance than the last in meters.


Results/Discussion:

Initially there were a few problems that were encountered, nothing went wrong while collecting the data. But at first when the data was added, some of the groups added their data incorrectly which made a portion of the appear in the wrong location as can be seen below in figure 4.

Figure 4: Yellow boxes contain the data points collected from the original data 

Visible in the figure above is the data that was added incorrectly, the longitude and latitude data was added incorrectly for the study areas located most north and the study area at the very bottom of the map. One was initially located no where near the site where the data was collected and the other was located in the parking lot outside of the Davies Center approximately 30 meters in front of where it is supposed to be. This was solved by changing around a few of the numbers in the longitude and latitude so then the data appeared in the correct locations to where the data was collected. These methods are very useful when technology fails, and back ups are needed. As long as the right equipment is in the possession of the surveyor then all is well. A pro for this method is that it is easy to use as long as the data is recorded in an organized fashion, for example in a table format. The technology that has surpassed this method are distance finders and different types of GPS to collect data points. These points if collected through a survey grade GPS can log all of the attribute data collected in the field, and already compatible to transfer onto ArcMap for use. The results taken by group 1 were all in their correct locations it seemed, because all of the data collected was entered into the Excel spreadsheet correctly. But its hard to tell how accurate the locations of the data points are on the map because of how thick the trees are its hard to see through the cover provided by the trees.


Conclusion:

This was overall a good lab, the class including myself was able to learn survey techniques if/when technology fails in the field. The only major error in the survey was the incorrect GPS points logged in the spreadsheet so it is very important to use the correct GPS points. As well as logging all of the attribute information correctly so then there aren't any mistakes in the final product. The accuracy in this project was sub par because of how the tree covers was so bad the GPS locations were hard to pick up. Older equipment can be frustrating to work with in times, but it is a good skill to learn just in case its needed. It would have been more interesting to have collected even more data points so than the different groups could get a better idea of these methods, just in case its the only option in the end.

Field Activity #4: Digital Elevation Surface Part II

Introduction:

In the previous lab the class used a sandbox as their study area which was 114 x 114 cm filled with sand. Each groups first task was to create a variety of different types of terrain such as; a ridge, hill, depression, valley, and plain. Our group created a grid system of 19 x 19, where each point is 6 cm apart from one another. Once the grid was created measurements of each unit was taken where the top of the sandbox is the zero elevation level, after the data points were collected they were then transferred onto an excel spreadsheet and ready for use in ArcMap. Data normalization is used to reduce data redundancy and improve data integrity, this skill was very important in this lab because it allowed the class to easily find any errors in the data and fix them. Once the data is normalized each group has collected accurate and usable data, which will allow them to create 3-D models of their landscape. The data points collected show the difference in elevation from point to point, this allowed each group to create their landscape through the use of interpolation. Interpolation is a tool found in ArcMap, this tool predicts the values for cells or the data points collected in a raster. The different interpolation methods used in part II of this activity are; IDW, Kriging, Natural neighbor, Spline, and the creation of a TIN raster. 

Methods:

The first step for this project was to create a folder specifically for all of the data collected and maps created. Next is to create a geodatabase within the folder that was just created, after the geodatabase is created the excel file with all of the data is imported into our geodatabase making sure that all of the data entered is set as numeric in excel. The excel file containing all X,Y, and Z data is opened up in ArcMap, then the different interpolation methods are implemented. The first is the IDW (Inverse Distance Weighted) method, this specific method averages all of the data within one cell of the grid system. The closer the data points are to the center of the cell in question the larger weight it has on the averaging process of each individual cell. The Kriging method estimates the surface of the landscape within each individual cell and from a scattered set of points with z-values. This procedure produces a prediction of what the surface looks like from the data collected from the study area, while providing a measure of accuracy for each surface prediction given. Natural neighbor is the next method, this method uses the z-value that was provided for each individual cell, finds the closest other data points neighboring the query point and creates a surface where all areas are smooth except for the locations with input values. The Spline method estimates the input values for each individual cell using a mathematical function that minimizes the curve on the surface. Creating a overall smooth surface that passes exactly through the data points collected making the depiction of the study area very accurate. The final method is the TIN (Triangular Irregular Network) method, this method connects all of the data points by triangulating a set of vertices. This forms a series of triangles that are all connected by these vertices creating a surface that displays elevation. Once all of the each of the interpolation methods was used on ArcMap, the data was then moved onto ArcScene where the image was shown in 3-D format. In order to create a map for each of the interpolation methods, each of the 3-D images needed to be saved as a layer file so then they can be opened on ArcMap and the legends from ArcScene can be used in the final maps. Next each 3-D image was exported as a 2-D JPEG, still displaying the image in 3-D. The orientation used helps each viewer see most if not all different types of terrain represented in the study area. The scale is very important because it allows someone to tell how much elevation is present in a certain area. 

Results/Discussion:

The different interpolation methods show multiple advantages and disadvantages with representing the elevation data from the study area. Some of the methods proved to show the data in similar ways, but others were better at representing certain types of terrain. Below is figure 1 which are the results for the IDW interpolation method.

Figure 1: IDW Interpolation of Sandbox Study Area

As one can see from the image above the areas that are at the zero elevation level are represented by the color green, while the areas that are above that level are blue and the areas that are below are yellow. The disadvantages to this method are that in the top right corner, the depressions aren't represented to the correct depth each of them smaller then they are supposed to be. There is the same problem with the valley on the left side of the map, it also doesn't represent it do the correct depth, too shallow. What can be taken from this image is that the IDM interpolation method doesn't represent area's below the zero elevation accurately, while the area's above that elevation level are represented accurately. Below is figure 2, representing the Spline interpolation method of the sandbox study area. 

Figure 2: Kriging Interpolation of Sandbox Study Area

After looking at figure 2 one can see that the Kriging method is probably the worst at representing elevation. The valley and depressions aren't represented correctly when being compared to the study area, they are far too shallow and not deep enough. While the ridge and hill are far too close to the ground and not high enough, once again not representing these features correctly when being compared to their real life features in the study area. Figure 2 is supposed to be representing a 3-D image based on the data collected in this activity but yet it almost looks as if it is still 2-D. The method still allows somebody to be able to see where the features are located because of the colors but it doesn't correctly show the elevation levels that it's supposed to. The map seems to be showing that the valley is the same depth as most of the study area which is not the case because the only other area that should be showing close depth values to the valley would be the depressions. Figure 3 is below, in this figure is the natural neighbors interpolation method.

Figure 3: Natural Neighbor Interpolation for Sandbox Study Area

This method for interpolation does an especially good job at representing the valley and depressions, the valley is to the far left of figure 3 and is colored blue. The three depressions are at the top left of figure 3 and are also colored blue. The main issue with using this method is that all levels that are supposedly below the zero elevation level are all listed at the same depth through this method. This can lead to problems, because of this people can misread figure 3 and misunderstand how deep some of the features are supposed to be and how some aren't as deep as they should be. The next method is represented by figure 4, this is the spline interpolation method.

Figure 4: Spline Interpolation of Sandbox Study Area

This method is the best at representing the landscape that was created in the sandbox, the spline interpolation does the best job at representing the difference between what is supposed to be below the zero elevation level and what is supposed to be above. Also does a very good job at representing a gradual increase or decrease in the landscape for example in the valley, the walls of the valley are changing color as they go down meaning that the depth is increasing. The final method is represented in figure 5, not really a interpolation method but the Triangular Irregular Network or TIN does a very good job at representing different elevation and depth levels for valleys, depressions, hills, and ridges. While looking at figure 5, somebody can easily see which colors are the highest (white, gray, and brown) and which ones represent the lowest points in the landscape (green, beige, and light blue), but in the end the best at representing the entire landscape as a whole is the spline interpolation method.

Figure 5: TIN Raster of Sandbox Study Area

Revisit Survey:

When remaking the survey with the landscape that was created, group 2 went back to the sandbox and created more data points towards the back right of the study area, where the depressions and the ridge are located. In order to do this, the group decided to change their points from 6x6 in that area to 3x6 which will provide more detailed digital imagery of that area. After looking at figure 6 below, one can see that the area where the depressions and ridge are located, there is much more detail then before in figure 4. Not only does it show a more accurate depth for the depressions but more accurate height for the ridge. The colors are continuously changing in those areas now because of the more data points taken to turn that area into a more accurate depiction of the study area.

Figure 6: Spline Interpolation of Revised Sandbox Study Area

Conclusion:

This survey is mainly about data normalization, which is very similar to many other field based surveys that have been performed to obtain data. If somebody were to do this activity without normalizing the data it would be very time consuming because of all the extra data points that would needed to be collected. This specific project is different from any other project because in this project instead of mapping an already existing landscape, the groups were able to design their own and map it themselves. It is not always this easy to create such a detailed grid based survey because this project was based on such a small scale. Compared to other projects where someone might have to collect data for a study area spanning hundreds of acres, this would be much harder to create a grid for something that large. Elevation isn't the only type of data that these interpolation methods can be used on, other types of data such as climate or temperatures. For example by creating a grid system over a large area and taking temperature data from each grid unit, a map of the gradual climate change throughout that study area can be made through the use of interpolation. 

Field Activity #4: Creation of Digital Elevation Surface

Introduction:

For this project the class was given two bins that were filled full of sand, these bins represented our study area. In this activity, the first step was to create a variety of different types of terrain such as; a ridge, hill, depression, valley and plain. In order to map out these types of terrain a sampling method must be set up, but first what is sampling especially in a spatial sense? Sampling is taking a number of points/samples to determine the big picture, in a way its a shortcut method to investigate the variable(s) at question. Each sample point contains data about that specific location and that will allow a map to be created from the area that the sample points are collected from. There are a few different ways to go about sampling this landscape, the first is random sampling, basically this is where points would be randomly placed around the landscape and the data would be taken from each of those points and recorded. Hopefully the random points would be on the terrain features but since the sampling technique is random, there are no guarantees. Systematic sampling is another technique that could be used for this activity, this technique is where you start with a random point then move on from that point at a periodic interval that doesn't change throughout the experiment. This way there will likely be a grid pattern created which will make all of the sample points organized and easy to collect data from. The final sampling method is stratified sampling, this is where the samples are divided up into separate groups of where the groups are based on similar features or data points. The lab objective is to create a landscape in a bin that is provided full of sand, create a grid system to map out the landscape, log the data from the grid system into an excel spreadsheet, and finally use a computer program that will create a digital image of the landscape created.


Methods:

The sampling method used for this activity was stratified sampling because it makes the most sense with the landscape that was created, and it would be the least time consuming method. Another method that would be similar to this one would be the systematic sampling method. But since the stratified method was used, there were fewer points taken, and the overall accuracy will be much higher because of how similar points are being combined together. The materials that are used in this lab includes a sandbox with a wooden frame, copious amounts of beach sand, a meter stick for measuring, push pins to mark important locations for the grid system, and string to create a grid system for the landscape created. The sampling scheme was 6 cm x 6 cm, this is because it's small enough to collect enough data for the activity, but large enough to the point where the amount of data points isn't overwhelming and hard to organize. This sampling scheme was completed by a meter stick being put on the wooden frame of the sandbox, this is where the push pins were put 6 cm apart from one another on every side of the wooden frame. The string was then used to connect the pins that are directly across from each other so then the grid system can be created. Below is an image of the grid system that was created using push pins, lines of string, and a meter stick.

Figure 1: Grid System for sampling method and Topography that was created

Since the data collection started at one of the corners of the sandbox a traditional (x,y) coordinate system was set up, where each point correlates with two points, but in this project there is also a z value for the elevation of each. For the zero elevation level, the top of the wooden frame was used as a reference point for zero elevation. To record all of the data, a table was drawn out in a notebook with the exact same grid layout as the figure above so then the elevation data for each sample point can be correctly recorded and organized.  The data was entered in this fashion starting at the first point; 1-1-Z, 2-1-Z, 3-1-Z, 4-1-Z, and so on. this made it especially easy to transfer the data from a notebook to a excel spreadsheet.


Results/Discussion:

The final number of sample points collected is 361 points. The minimum number that was collected was -14, maximum number was 4, the mean was -4.7, and the standard deviation was 3. The sampling method that was chosen worked perfectly, as discussed by the group each grid unit was one sample point, and this made it much easier to organize all of the data that was collected. One of the major problems that occurred during this activity was that the lines were producing some slack which made it difficult to take the elevation points because the lines were at the same height of the wooden frame which was designated as the zero point for elevation. This was overcome by tightening up the lines to the required height for the zero point for elevation. Also each grid unit was hard to measure not based on the height of the line but the terrain itself because the entire area within each grid unit isn't flat making it hard to average out the elevation for each individual sample point.


Conclusion:

The sampling method that was used in this activity relates to most other sampling methods and the definition of sampling itself. But in this case it especially relates with the systematic sampling method because of how a grid system was created to acquire the points and not just placing random points out there and taking data from them. It is crucial when doing spatial analysis to have a sampling method, a sampling method allows you to generalize some data of a large area into a smaller sample. There is no possible way to collect every single point of data in a large area, but through sampling every point doesn't need to be collected and the same results are achieved. In relation to sampling spatial data for a larger area this is essentially the same concept but at a smaller scale. Once all of the numbers were analyzed the sampling method chosen did a decent job, each of the sampling points came out with an elevation level, all of the land types were accounted for, there are just some sampling points that could be more accurate. To refine the survey to increase the sampling density, a smaller sampling scheme should be used so than even more accurate data can be collected from each grid unit.

Hadleyville Cemetery Final Part

Introduction:

The problem that was faced during this project was to try and recover all of the lost data from Hadleyville cemetery. In order for these issues to be resolved, field work where data will be collected from all the legible headstones is a necessary part of this activity. This will allow a map to be created representing Hadleyville cemetery. To learn more about the problem at hand and the methods that was used to collect the data click here: Intro. and Methods


Methods:

There were two different tools that was used to collect all of the data and imagery, the first tool was a survey grade handheld GPS device and the second was an UAS. The GPS was used because it will allow the data that is collected to be as accurate as possible to it's real life location. Unfortunately the allotted time was not enough to collect all of the grave sites with the handheld GPS device, so a majority of the data was collected by hand and organized in an attribute table styled fashion by pencil in a notebook. The UAS was very helpful because it allowed the study area to be mapped using a high resolution camera that will capture 95% of the study area that needs to be mapped.

Once all of the data was collected and the images captured, the next step was to transfer all of the hard copy data towards use in ArcMap. The project team was split into groups, each group collected a few rows of data, each row contained different amounts of grave sites but all were accounted for. The data was then compiled onto an online excel spreadsheet where the data can be organized in the correct manner. The data was organized by rows and columns, so then once the data is mapped it will be that much easier to find the grave site in question. Below is Figure 1 which is the row and column mapping system that was created.

Figure1: Organized System for Grave Sites created by Marcus Sessler 

The hardest challenge that the class faced when trying to compile all of our data into one was making sure that everyone had collected the same type of attributes. Some groups had collected different types of attributes so there were some problems, but all was resolved when the missing data was found. Other than that the normalization of data was pretty simple because everyone was working together and trying to help one another so it was a smooth process. The map was created and a feature class named "Gravesite" is included, which is all the locations of the grave sites on the map. This feature class contained fields such as Point ID, which makes it really simple to join the table to the digitized points in the map.


Results:

Below is Figure 2, this figure represents all of the fields that were used to organize all of the data that was collected at the study area for each grave site.

Figure 2: Attribute Table for the Grave Locations

In figure 3 the final product is presented, the main image is the cemetery in question and all of the red spots are grave sites. The map below the main image shows the location of the study area within the state of Wisconsin.

Figure 3: Hadleyville Cemetery

If one was to click on one of the grave site locations a window would pop up telling you what row and column the grave is and much more. Figure 4 below presents what would happen if you were to click on a location.

Figure 4: Grave Site Location Attributes

Once one of the grave site locations is selected windows exactly like the ones in figure 4 will pop up. One window will show the picture of the gravestone and the other window will show all of the attributes for that particular headstone.

The data collection methods took the majority of this project when compared to how long it took to create the GIS part of the project. The survey grade GPS device wasn't used very much in the data collection, only for a few headstones. As mentioned before a majority of the data collection was by hand, this was due to the GPS was taking too much time and all of the data needed to be collected. Some sources of error for why the survey grade GPS wasn't used was because of stability, the GPS device was on a stand and it was hard to get it perfectly straight. Possible data entry errors, taking pictures with the GPS device may not be included, certain headstones not being accounted for and much more. A way for this to be solved may have been for a single group to go through the cemetery with the GPS so then they would have more time to collect all of the data rather then taking turns with the GPS device. Another possibility of refining the method was to have a single person take the pictures of all the headstone so then it would be easier to gather information, rather than going to multiple people for different pictures just go to one with all of them. Also creating a grid system of the study area before going there initially would have made the process of collecting all of the data much smoother. But this would have all relied on the information that was provided of the cemetery initially, which was very little.


Conclusion:

Overall all of the methods that were used accomplished the final goal which was to create a map through the use of GIS that would be able to locate grave site locations in Hadleyville Cemetery while providing all of its attributes and an image of the gravestone. The data that was collected came in multiple formats which in the end may decrease the accuracy by a bit but not much. Having all of the data compiled onto an online excel spreadsheet made the project very simple, the only task to be done was a table join which was completed and joined all of the data points or grave site locations with its corresponding GPS location. The final product that came out of this project provides much more information than the original information that was provided, this product shows all headstones that were accounted for and all the attributes for each of them. The survey ended up being a success and this final product will continue to be updated as time goes on and more plots are added, so than all graves will be accounted for.