Distance Azimuth Survey

Introduction

This lab we collected azimuth.  Azimuth is the angle from your origin point.  This means that some of the points collected had very high azimuth and some had very low azimuth.  This measurement can go up to 360 degrees.  In figure one each of the three origin points have all of the azimuth lines coming from it.  The origin points are all located in the center of the point sets.  Data was collected for ten trees from each of the three origin points. 

Study Area

The area that was surveyed consisted of three different points along Putnam trail.  From these different points, 10 trees were surveyed.  These ten trees we looked at distance and azimuth from our starting point, type of tree, and also the DBH (Diameter at Breast Height).  The group I was with took our points at the same point we started at as a class.  This would be the middle set of points (Figure 1).  This area was located at the bottom of the UWEC stairs. 

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Methods

The data that was collected for each point consisted of DBH, Azimuth, Distance from starting point, and tree type.  This data was measured using a compass tool and lasers.  The lasers measured the azimuth and the distance from where you are standing.  There is a small lens that must be looked through and the crosshairs needed to line up on the tree you wanted.  This laser would then tell how far this tree is from the origin point, while also collecting the azimuth information.  The compass that was used had a small lens to look through to calculate the azimuth.  The students then had to measure the distance to the tree they were trying to plot.  This unit was more up to the student to find a precise measurement.

The data that was collected included X,Y coordinates for the origin, DBH, azimuth, distance, and the tree type.  This data was collected to show what attributes went to which point.  This also makes it easier to tell which tree is being shot.  As the person was shooting the point collecting the azimuth and distance measurements, another student was collecting the DBH data.  This was collected using a tape measure that took the circumference measurement and transferred this into a Diameter value.

Azimuth is very important for sailors and ships.  They use this same kind of data, but they use it in the sky and calculate azimuth from certain stars.  Some say that azimuth is like a horizontal angle measured around a fixed origin point in a clockwise fashion.  The benefits of this method are that not many tools are required.  Although this is true there is technology that can calculate azimuth much more accurately.

In creating the map some issues were encountered.  Two of the origin points were not in the right location.  This was overcome by recalculating the X,Y coordinates to put the origin in the correct spot.  Some of the issues we faced in the field were visibility.  There were times a student wanted to collect data of a certain tree but could not due to the other trees in the way.  To calculate different data points, another origin point would have to be created.               

Figure 1:  Map shows the three locations where the ten points were collected.  This data was gathered along Putnam trail and near the bottom of the campus stairs.  The cars are in the new Davies Center parking lot, while the track is behind McPhee on upper campus

Results

The data that was collected was accurate, except for the X,Y coordinates.  This data had to be played around with to get the points where they needed to be.  The coordinates of these points are not extremely accurate, which then makes the map inaccurate.  The map shows the representative distances to the trees that were reviewed.  If the origin was moved, the distance to these trees is off.  All the data was collected, but in the future we will need to be more careful to have this data be more accurate. 

Conclusion

This tool could be extremely beneficial to someone often sailing the seas.  It is not as important to me.  This is a very interesting way to find data.  This shows a very precise location of where you got the data that was collected.  Depending on the angle, you can tell which direction the data came from.  This method is very interesting, but somewhat impractical to me.

Visualizing Sandbox Survey.

Introduction.

In the previous labs the groups made a topographic model in a sandbox.  Within the sandbox groups took data points to accurately map and model the topography that they created within the sandbox.  The data for each point collected was then put into an excel file.  From here the data was imported into arc map and normalized.  This means that the columns and rows were arranged based on their dependency on one another.  This allows for the integrity and accuracy of the model to be accurate.  Group 6 created their data points by making 19 6cm squares on both the X and Y axis.  These points were then measured and placed on a Z axis giving us 3 dimensional points.  These points were interpolated in arc map, this means that more points were assumed between the actual points.  This allows the software to in a way, fill in the gaps.

Methods.

Five different interpolation methods were used.  These methods were IDW, Kriging, Spline, Natural Neighbors, and TIN.  These methods are all different interpolation methods and do different things.

IDW- Inverse Distance Weighted Interpolation
This form of interpolation says that closer points are more closely related than those that are farther apart.  It uses points that are closer together to account for areas that have no data.  This makes these areas more possible to have wrong values.  Figure 1 shows the features that were created and from the points gathered these features are very rounded around each of the points.  This method could be very smart to use on large areas.



Figure 1:  This image shows the IDW method of interpolation.  As you can see around each point is a slight bulge of data that was filled in by the software.

Kriging
This method is very similar to the IDW method in that it predicts areas that had no measurement.  This method is based on statistical correlations. Kriging uses a mathematical function.  This method would again be a good method if you wanted to map a large piece of land.  The features in this image (2) are similar to those in the previous image, with some slight differences.


Figure 2:  Kriging method of interpolation is similar to IDW with this image being less precise in such a small area.

Spline
The spline method of interpolation makes a surface that passes through as many data points as possible.  This makes for a very flat image that is very precise and accurate.  This made for the best looking map in my opinion. (Figure 3)  This map looks much smoother because there are 400 data points for it to connect to.


Figure 3: This image shows the spline with very rounded and well defined features.  This image best shows the detail.

Natural Neighbor
This method of interpolation uses the interpolation point and overlapping points around it.  This is then given a weight.  When these areas are overlapped it gives more of a weight to areas that are closer.  As a certain point is within a polygon, the software takes other polygons that over lap this point and use the data from it.  This is another way to have an accurate reading on a larger scale piece of land.  This method is nearly as accurate as the spline method. (Figure 4)


Figure 4:  This image shows the Natural neighbor method.  In the valley as well as the ridge there is not as much detail, but this still does a great job of depicting the landscape.

The next of the images made in arcgis was a TIN.  This is a Triangulated Irregular Network.  This takes the amount of points into effect.  These TINs take more points if the landscape is highly variable.  For our group this interpolation method did not work the best, because we took such uniform points. (Figure 5)  The image is still viewable, but is not as good as some of the others.


Figure 5:  This image shows the TIN image that we collected.  The area that this image captured the best was the plateau that was located right in the center of the image.

After all of these maps were created in arcGIS they were transferred into ArcScene.  This allows the maps to be viewed in 3D.  (Figure 6)  The image that I used to transfer into arcScene was the spline method of interpolation.  This image best captured the scene that we were trying to create in the sand.  The final image that I created in arcScene is not north and south.  There was a north arrow placed on the image for origin.  I also took a lower angle view at the map, because I wanted to show the 3D effect of the map.

Figure 6:  This image shows the final map that was created in arc scene.  The lower areas are represented by the purple, while the dark green areas represent high spots.

Discussion
The images that were gathered really had all different kinds of results.  The spline was the most successful in both arc gis and arc scene.  The TIN was the least visually pleasing of the images.  This was due to the fact that the points we took were all uniform.  This is why spline worked the best.  The next best was Natural neighbors method.  These images looked very similar to the spline just without all of the detail.  Kriging method did not look good either.  There may not have been enough difference in the vertical elevation.  This method did not show nearly as much of the detail as any of the other methods.  IDW was right in the middle.  This image showed some of the detail and it shows a great job showing how this software gets this data.  There are small bulges around all of the data points.  This is because again we took our points in such a uniform fashion.

Conclusion
This exercise did a good job in getting the students a slight background in surveying.  This is obviously very small scale, but is very similar to how surveying is done.  Professionals obviously use different equipment from tacks, string, and meter sticks, but this is how surveying is done.  Surveying is calculating elevation differences and creating maps from certain data points.  This is why interpolation can be so important to professionals.  They obviously will not survey every square inch of land.  This means that the interpolation methods that calculate the differences could come in handy, and also save some time.  This type of survey is not realistic especially on a larger scale.  There were many data points taken and this would not happen with a larger area.

Field Activity 4

INTRODUCTION:
Surveying is the act of taking a collection of points to make some kind of reference, map, or image.  Surveying is just a type of sampling that can be done in a number of ways.  The three most common types of sampling are random and systematic.  A random sample method is just that.  The points measured have to be completely random.  This can be done with a random number generator or a coin toss.  Systematic approach is using a pattern.  This would be  These methods can be very useful in getting measurements of data especially in spatial situations.  These sampling points are supposed to be snapshots to give you an idea of what the rest of the landscape would look like.  The objective of this lab was to create a landscape and using a simple surveying or sampling method, collect the elevation changes in this small area.

METHODS:
Group 6 chose to sample every point in the grid pattern.  (Figure 1)  The box is 114cm x 114cm.  This was divided up into 19 6cm squares.  Then elevation values were taken at each point in the grid pattern.  A total of 400 data points were collected.  Sea level was the top of the box.  In this case this land would flood very easily because it was below sea level in most points.  Instead of a systematic approach a measurement was taken at every cross of the grid.  This gave the information needed to make a 3D map (X,Y,Z)  Tacks were used to hold the points and string was strung across the sandbox.  This created the grid pattern.  The 6cm squares allow for very high precision.  The points were collected with a ruler and transferred into an excel file.  This data will allow easy access and creating a feature class.

Figure 1:  This image shows the grid pattern that was used to collect the data points.

Figure one shows that the origin (0,0) point was in the top left of this image.  Moving to the right in the image, the valley is the first feature.  The middle of this area is dominated by a plateau landform while there are two small hills in the bottom left corner.  A ridge runs from the hill and down into a depression on the bottom right of this image.  

RESULTS:

400 sample points X,Y,Z values were recorded.  There was not much difference in the elevation.  The lowest point recorded was -6cm while the highest value recorded was 4cm above sea level.  This makes the range a difference of 10.  The most frequent value recorded was -2cm below sea level.  Since all points were collected, the data gathered should be extremely accurate.  This method should met the objective better than any of the other sampling methods.  This method would not be practical on a much bigger scale.  The group stuck to the original plan.  It did take a while to figure out what kind of plan and direction we wanted to go.  A numbering system would have made collecting the dat points go much faster.

CONCLUSION:

This sampling method is extremely accurate but also very time consuming.  In spatial situations this can be effective for mapping.  These sampling methods can cover larger areas in less time.  This would make the map a little less effective.  The survey did a great job showing the numbers that were needed. 

Assignment 3

Introduction

Hadleyville cemetery lost the majority of their burial records.  They have been experiencing problems in selling burial lots.  Since they have no records, they don’t know which plots are empty and which are not.  A GIS was built for this cemetery to show an image to go along with the points that were plotted.  This gives the information of the headstones, while also showing where the stone is located in the cemetery.  A survey grade GPS unit was used, as well as a UAS drone and visual interpretation skills.  Images were also taken of the headstones.  The drone supplied a locator map while the survey grade GPS unit was used to accurately map the locations of headstones.  This provides 3 pieces of information, the accurate locations, all the information on the stones, and an image of the stone.  Some of the stones were very difficult to read or to even be seen.  This makes it difficult to know if all of the data was received and collected.

Study Area

Hadleyville cemetery is located just outside Eleva Wisconsin.  It is located right on the edge of a cornfield and is a quite old cemetery.  It is located right on county highway HH between Eau Claire and Eleva. (Figure 1) The data was collected in two shifts in early to mid September.

Figure 1.  This image shows the locations of Eleva, Hadleyville, and Eau Claire.  The image shows that Hadleyville is in the center between Eleva and Eau Claire.

Methods

The class used many different tools to conduct the survey.  GPS units, UAS drones, and notebooks were all used to accurately describe and map the data.  These tools were used to get a background on how they work and how exactly they differ in accuracy and capabilities.  The only problem faced was that the survey grade GPS unit used took too long to gather data for all of the points.  The data gathered with this unit, however was extremely accurate.  (Within 1 centimeter)  This method was extremely time consuming and time ran out while we were trying to gather the data for these headstones.  To collect the data the class used notebooks, a survey grade GPS unit, and cameras.  Although a UAS drone was used, this did not help us collect data.  For this project a digital mode of data collection would not be best, because it was so time consuming.  The data collected was recorded into a Microsoft excel file.  From this file the data was transferred into an attribute table.  This data includes whether the stone was readable, the type of stone, the name on the stone, and the birth and death dates.  (Figure 2) The hard copies of the data gathered were put into an excel file in which the labels match that of the headstone labels.  For example, the third headstone in the second row would be labeled B3. (Figure 3) This allowed for a table join based on the grave id field.  The UAS data was used to more easily see the stones against the background.  This data was then used as the background of the map to easily locate the stones and see the attributes.  The accuracy of the GPS placed points nearly exactly where they are located in relation to the other headstones.  The pixel resolution was not clear enough to zoom all the way in to see some of the stones.  This is where the imagery coloring needed to be changed to clearly see the contrasting colors of the stones.

Figure 2.  This is the attribute table showing some of the data that we collected.  The top row shows the category.  The column on the far right shows the letter and number combination to the corresponding stone.

Figure 3.  This figure shows the rows and numbering system used to label each headstone

Results

The attributes that were used can be seen in the attribute table.  (Figure 2)

There was a lot of time spent in putting the data into an excel file.  This data was critical in the final map created.  These attributes were easily transferred into ArcGIS.  This gives us all of the information for who is buried at each point on the map.  (Figure 4)

As a class there should have been more communication on how to go about the process.  It would have greatly improved the data entry.  This could also have saved a large amount of time.  Communication is key in large data collection.  The communication between groups could have also been improved to save time.

Figure 4.  This image shows the information for headstone O2.  In the bottom left of the image, this is the selected point.  On the right side, the corresponding attributes can be seen.

Conclusion

The methods of this project worked well and this answered the questions that needed to be answered for the Hadleyville cemetery.  The mixed forms of data that were collected really added to the overall time spent on the project.  It was very disorganized and the attributes needed were not clearly discussed within the class.  The errors are negligible as everything was figured out.  The errors were worked through and all the data that was possible to be collected was collected.  This map should be a great upgrade for the records of Hadleyville cemetery.  This survey was a success as all of the data possible to read, was recorded.  This GIS can be constantly updated and should be a great form of keeping the cemetery’s records in the future.  Figure 5 shows the final map that was created.

Figure 5.  This image shows the final map along with the locator map that was created to show the area in which the data was collected.  Toward the top of the locator map there is just a small sliver of UWEC campus.  The area that was mapped is very small and located by the callout box.