Development of a Field Navigation Map and Learning distance/bearing Navigation

Introduction

Later this semester, our class will be going out to a place called the Priory, where we will practice navigation. The Priory is a peice of land owned by the University of Wisconsin Eau Claire that is located a couple of miles away from the main campus.  As we did not actually go to the priory yet, I will leave a more complete and detailed description of its location for another blog post.

The main objective of this lab, will be to produce maps that our groups will be able to use during that navigation activity.  The maps that we are producing are navigation maps.  All maps are designed to convey some meaningul information, the information displayed on this map will ideally be useful in navigating.  For this reason, I will be careful in this lab, to not put information on the map that is not helpful in navigation.  Any information that is on the map that does not help with navigating is not only useless, but decreases the usefulness of the map by making it harder to read.  The information that we are required to have on the map for the assignemnt are as followed:

Ø  A north Arrow

Ø  A scale bar

Ø  Name of Projection

Ø  Name of coordinate system

Ø  Properly labeled grid in place

Ø  A background

Ø  List of data sources

Ø  Name of cartographer

I also wanted to add elevation to the map, because I saw blogs from a previous section of this class including it.  I think it is valuable information, and could be useful in navigation.  Another feature that I believe will be helpful to add is my pace count.

A pace count is the number of steps that it takes to travel a distance of 100 meters.  It is another objective of this lab, for us to determine our pace counts.  If you know what your pace count is, then when you are out in the field you can have a resonable estimate about the distance you have traveled based on your pace count, as long as you count your steps along the way.  Having this knowledge will be useful when we go to the Priory for our navigation activity.

The main point of this lab, is to get useful information that can we can use at the priory, and also apply critical thinking skills to determine what infomation is useful. We will have data given to us in the priory geodatabase that isn't reasonable to use in this particular map. It will be important to keep a focus on the purpose of the map when determing what information is pertinent to include.



Methods

The first step of the activity was to determine our pace step.  We did this together as a class.  We went to the parking sidewalk behind Phillips hall, where we used a laser range finder to measure out a distance of 100 feet.  We walked 100 feet, counting every time that our right foot went to the ground.  We did the same thing walking back, and then we averaged the two numbers to get our pace step.  The first 100 feet I got 67, and the next 100 feet I got 65.  My pace number was 66.  The numbers that I got were pretty close to one another, so I think I have a fairly accurate number for what my pace count is. If the numbers were further apart, I might be concerned that my pace was too variable during the measurement, and that this affects how accurate the pace count can be.

In the next part of the activity we worked on making navigation maps. We were provided with a geodatabase that contained data that could be used when making our maps.  The first step for me was to go through and determine which layers I thought were useful, and which ones I thought should not be used.  The main criteria for determining usablity, was whether I thought the information was peritinent to navigation, or if I thought it cluttered the map.

The first layer I looked at was a polygon entitled Navigationboundary.  There was no summary description in the item description, but presumably this is the area that we will be in during the navigation activity that these maps will be used in.  When looking at past blogs, people included some amount of space around the navigation boundary.  I decided this would be a good idea, in case we can get reference from features that are located outside of the navigation boundary, and that the space could be used for other information as well.

Figure 1.  Navigation boundary for Priory navigation activity

The next layer that I looked at was a Digital Raster Graphic that was a scanned image of  a US Geological Survey standard series topographic map. I decided not to use this for several reasons.  It was very cluttered, and the resolution was relatively poor.  Also, the item description did not include a date for when the map was made.  The map does label St. Bedes Academy which was established about 54 years ago, so I know it must have been made after 1961, but I do not have a more precise time frame than that and I do not know if the information is still relevant. 


Figure 2. DRG Scanned image of USGS topographic map, provided by the Natural Resources Conservation Service

I decided that I would use an Esri Imagery base map instead of the DRG scanned image USGS topographic map.  I chose this because I think that with less labels and lines, and being more current,  will be more useful for making navigation maps.

Figure 3. Esri imagery basemap shown with navigation boundary

The next layer I looked at was entitled priory_2ftcountours.  This shows contour lines for the areas, at every 2 foot difference in elevation.  Contour lines show a continuous line for points of equal elevation.  It can be a good way to show a three dimensional characteristic like elevation on a two dimensional surface. There was no summary or description given in the item description.  Although I think that there could be some usefulness in showing changes in elevation, I think that this would cause a problem with visibility, as it blocks out a large amount of the background imagery.

Figure 4. 2 ft contour lines at the priory, shown with Esri imagery base map

The next layer was entitled No_Shooting_Zone.  It consists of three polygons that indicate what I assume are places you are not allowed to shoot.  There was no summary or description given in the item description.  The no shooting zone was not mentioned as being part of the navigation activity, so I will not include it on my maps.

Figure 5.  Layer shows No Shooting Zone at the priory

The next layer was a raster entitled grdn45w092_13.  There was no description or summary in the item description, however I assume that it is a DEM or a digital elevation model.  I assume this because it looks similar to DEMs shown in past blog posts, and it shows values that would be expected when compared with the contour lines.  It is a good way to demonstrate changes in elevation.  I decided I would make it transparent, and increase the contrast, and add this layer to my final maps.

Figure 6. Digital Elevation Model of the Priory

The next layer that I looked at was contour lines for every 5 foot difference in elevation, clipped to within the navigation boundary.  These contour lines give useful information, but allow better visibility compared to the 2ft contour lines.  I decided that I would use these in my final maps

Figure 7.  Contour lines for every 5 feet inside the navigation boundary at the priory
 

The next consideration was to put in grids for each of the maps.  One of the maps would have a UTM (Universal Transeverse Mercator) grid, and the other would have a grid based on decimal degrees.  In the UTM coordinate system the world is divided into sixty zones, and each zone has a secant transverse mercator projection.  The properties that make the transverse mercator projection useful in navigation are as followed:

Shape and Area- Small shapes are represented accurately, and larger shapes are represented with minimum distortion.  Area is also kept with minimum distortion.

Distance and Direction- Local angles are kept accurate, and distortion for distance is minimized.

Figure 8.  Map showing 60 UTM zones

For the final maps, I decided that I would used background imagery from esri, The clipped 5ft contour lines found in the priory geodatabase, and also the DEM raster (with transparency and contrast both increased to make it more usable).  The final maps also contain grids in either UTM meters or in Decimal Degrees.  This grid should be useful in navigation.


Figure 9. Map with grid based on UTM

Figure 10.  Map with grid based on Decimal Degrees.

Discussion

When looking at other blog posts for this activity I noticed that their wasn't too much variation between the navigation maps.  Most people decided to use the 5ft contour lines, and some people also decided to include the DEM raster.  Also, most people decided to use the esri imagery for their background.  Most of the variation that I saw was differences in cartographic style.  The content of all of the maps is extremely similar.  I think that a reason that they look similar, is that this map needed to be simple.  The more that you may try to add, the less readable and the less useful the map becomes.

Most of the groups seem to be happy with the maps they produced, and when looking at their discussion during the field part of the activity, I saw the maps that were similar to mine were rated as useful.  The discussion that I saw focused on trying to figure out if the grid size they used was good enough, and if the elevation was clear to read.  As always, I think that any mistakes that I have made will be more apparent after the field activity, but for now I am confident that my maps can convey useful information for the navigation activity.

One concern that I had in making these maps, was the data that I used.  If the data in the geodatabse had not been given to me by my instructor, I would have been more cautious about using it, and I would have had to look for more information about where it actually came from.  Most of the features had little or no description about where they came from, what they were, or what time they were collected at.  Since it was given to me by my professor, I am confident that the data is reliable, but had this been a different situation, I would have spent more time trying to find metadata.

The biggest issue that I had in actually making the map, was remembering everything that I had learned about using ArcMap.  I felt I was working relatively slowly for the whole activity, which might have something to do with trying to remember how to perform all of the functions.  After the activity though, I think I remeber more of what I have already learned.


Conclusion

The objectives of the activity were completed through the making of a navigation map for the priory.  I learned several things from this activity. I learned what a pace count is and how to calculate it.  I also learned some basics for what features are useful to include in a navigation map.  In making the map, I think I learned a few lessons that are applicable to map making in general.  Since we will be using these maps in the future, it was important to me to make the map as clear as possible.  I think that if you keep the objective of whatever map you make in mind during the process of making it, you will have a better end result.  If you can imagine yourself using the map for the intended purpose, then you can better at understanding how to convey useful information.


Sources:
For Priory Information: WQOW
UTM zone map:  University of Deleware

Field Activity 2, Visualizing and Refining terrain.

Introduction

The objective of this excercise was to take the data that we had collected in our terrain survey, and represent the terrain in a model.  This was done by importing the data into ArcGIS.  To make a continuous three dimensional model out of the XYZ coordinates that we had, a method of interpolation had to be used.  In this lab we experimented with the following methods of interpolation:
 

§  IDW
§  Natural Neighbors
§  Kriging
§  Spline
§  TIN

After a model was made it was apparent that it would be necessary to resample a few areas in the terrain to get a better representative model.  Our group collected more survey points from the areas that we felt were somewhat misrepresented in the model and then we updated our XYZ coordinates and again performed the interpolation methods.

In this lab we will be determining how much information it takes for interpolations to render an accurate depiction of the surveyed terrain.  What we should away from this activity will be how to make accurate models, and how to update surveys.  This activity is very practical and accurate if we ever need to take a survey of some kind of terrain, and then need to model it.

Figure1.  Terrain to be modeled in this activity

Methods
After the XYZ coordinates from our survey were recorded in an Excel spreadsheet, the data was imported into ArcGIS and saved as a feature class in a geodatabase that had been created and titled to store features from this activity.

After this was done, the  interpolation methods were performed.  When this was done, the rasters (and TIN) were viewed in ArcScene.  To preface this, it is important to have a rudimentary understanding of what interpolation is.  Interpolation is a way to use coordinates or data points to produce a continuous raster.  Interpolation can be utilized when there is an assumption that the data that is closer to your collected points will be similar in value, and that they can be used to help estimate what values the raster cells should have.  ArcHelp has a good example of how interpolation can be used to make an elevation raster.  A picture of this is shown below.




Figure 2.  Screenshot of ArcHelp example of Interpolation being utilized to produce a raster for elevation surfaces.

The first method of interpolation used was IDW.  IDW stands for the Inverse Distance Weighted technique.  This technique uses the distance from the raster cell to the supplied coordinate to determine how much influence the coordinate value will have.  The method assumes that the coordinates have less influence as distance increases, which can be somewhat problematic when trying to successfully graph ridges.



Figure 3.  3D model of terrain produced using IDW.  Important aspect to note is the bumpy quality of the image, explained by the methods assumtion that distance away from a coordinate point will decrease the influence the coordinate has on the raster cell value.
 

The next method of interpolation used was Kriging.  In some respects Kriging is similar to the IDW technique in that it uses inverse distance weighting as part of its prediction for the values to fill the raster cells.  It is unique however, in that it also takes into account the overall spatial arrangements of the supplied coordinate and can use this to improve the quality of its predicition.  When comparing Kriging to IDW, you can see that the Kriging method does a better job at recognizing the features in the terrain.

Figure 4.  3D model of terrain produced using Kriging.  Important aspect to notice is the relative smoothness of the features when compared to the IDW technique.
 

The next method of interpolation used was the Nearest Neighbor technique.  The Nearest Neighbor Technique is arguably one of the most simple and straightforward methods.  It doesn't make too many assumptions about the data, and will only approximate raster cell values based on the values of the nearest coordinate points.  For this reason, it can avoid some problems made by assumptions of other methods, but it works best for extremely detailed data because it will not predict trends or patterns.

Figure 5.  3D model of the terrain using Nearest Neighbor.  Important aspect to notice is the relatie roughness around some of the features due to the fact that nearest neighbor does not predict patterns. 

The next method of interpolation used was Spline.  The Spline technique estimates values using a mathmatical function that results in a smooth surface that passes through the coordinate points.  This technique would work best for models of terrain that are relatively smooth.

Figure 6.  3D model of the terrain using Spline.  Important aspect to notice is the relative smoothness of the features in this renering compared to some of the other methods.

The next method of interpolation used was creating a TIN.  A TIN is a triangular intergrated network.  It is different from the other methods because this does not produce a method.  The technique uses the coordinates to produce triangulated vertices which can then be used to produce a model.  While this technique can produce a useful model, it is limited by the fact that it lacks a certain level of realism.

Figure 7.  3D model of the terrain created by making a TIN.  Important aspect to notice is that it is composed of triangles, and it is not a raster like the other renditions.

Our group decided that we thought Spline best represented our actual terrain, but that the model could be improved in a few areas.  We saw that that specifically the ridges and depression of the model looked slightly different from the terrain that we had produced.  For this reason we decided that we could improve our model by taking additional elevation measurements from these areas and then use the updated coordinates to produce a 3D model.

During our orignial survey (see original survey) we had made a grid, and then taken an elevation measurment in the center of each grid cell.  To update the survey, we made the same grid, and then took measurents on the four corners of each grid cell (only in the areas that we had determined needed better representation).  The areas that we had determined needed better representation were the hill, ridges, and depression.  Important conditions to note during our survey revision is the fact that there had been a snowfall, which had the potential slightly alter the elevation of our terrain in certain areas.  We tried to minimize the effects of this by lightly brushing away the recent snow, and by trying to take elevation measurements where the snow was dense and not fluffy.

When we went to add elevation measurements to the coordinates we already had, we ran into a few problems.  This was due to how we set up our coordinate system when entering the data from our original survey.  Instead of putting the centimenter measurements for the X and Y coordinates, every five centimeters was essentially counted as one unit. when updating the coordinates, we had to look at what grid cell it corisponded to, and then determine if we should add or subtract .25 from the X and Y values based on which direction from the center of the grid cell the new measuremnt was taken at.  The main problem we had with this, was that it was difficult to conceptualize and tedious to enter the data, but we eventually were able to successfully update the coordinates, so that we would have better representation for the areas that we were concerned about.

Figure 8.  Original survey points shown on the left, updated survey points shown on the right.

When the Spline interpolation technique was performed using the updated coordinates, the results were not what we had hoped for.  The Spline interpolation from our original data had been relatively smooth, and seemed to match our terrain relatively closely.  The Spline interpolation using the updated coordinates seemed to actually be worse when compared to several of the other techniques, including IDW which had been our least accurate model so far.

Figure 8.  3D model made using updated survey coordinates and the Spline interpolation technique.  Important aspect to notice is that it almost looks like an IDW model, and the resulting model looks less like the terrain than the non updated spline model.

Discussion
One of the most important questions to answer is how the model made using the updated coordinates turned out the way it did, and what errors would have had to occur to get us a 3D model that looks so different from the terrain.  Initially there was a question of whether the XY coordinates were correct because we were having troubles conceptualizing how to correctly enter it.  however looking at the updated XYZ coordinates in ArcMap, the points were in the correct XY location.

Figure 9.  Coordinates were entered with the correct XY coordinates.

The error therefor must have come from the Z coordinate, or the elevation measurements.  ArcScene was useful in looking at how the XYZ coordinates related to the Spline interpolation model.  The following pictures show either the original or updated spline models and how they compare with the original and updated XYZ coordinates.


Figure 10.  Original Spline model shown with original XYZ coordinates



Figure 11. Updated Spline model shown with original XYZ coordinates

Figure 12.  Updated Spline model shown with updated XYZ coordinates

Figure 13.  Original Spline model shown with updated coordinates

Figure 14.  Magnification of the disparity seen between the original spline model and the updated XYZ coordinates.

The way that spline works is by using a mathematical function to create a smooth surface that runs through all of the coordinate points.  In order for the spline model to pass through the coordinate points, it needed to create the sloping abrupt peaks that we see in the updated model.  These peaks are not seen in our terrain, but they are represented in the coordinates.  This suggests that the error came from some part of our methodology when collecting the elevation measurements in our second survey.

Most likely, when we tried to minimize the effect of the snowfall, we overcorrected and brushed away snow that was part of the original terrain we had made.  This would explain why we have coordinates that exist below where they would be expected.  Essentially, we changed the terrain in a significant way, so that when we took new elevation measurements to add to our existing data, the model lost accuracy because it was basically mapping coordinates from two different terrains.  This is of course, discouraging news given the amount of time that our group spent on updating the survey, but it does encourage us to be more aware in the future to avoid similar problems. 

There were probably several factors that kept us from realizing that our data was inconsistent.  We we started collecting around 5:00pm, and it was getting colder and darker as we were out there.  We wanted to finish as quickly as possible, and because of this we weren't stopping to ask ourselves whether the elevation data we were collecting made sense when we compared it to the original survey points.  Additionally, when we were entering the coordinates into an excel spreadsheet, we were so concerned with making sure our X and Y coordinates were correct we didn't take time to really look at the elevation data. 

Conclusion
If we were to do this project again we probably would have done it differently.  When addressing the issue of the snowfall and how it affected the terrain, it might have been better to either leave it be, or to take an entire new survey to account for it.  Each of these solutions would have had its own issues.  If we left the new snowfall, we might see a similar disparity between the original and updated coordinates.  If we were to start an entire new survey, it would have taken us much longer to complete.  In the end, our revisionary survey did not work out, and the most accurate model we have was obtained using the Spline interpolation method with the data collected from the original survey.


Figure 15.  Spline was chosen as the best interpolation technique to represent our data.

The original objective of the excerisise was ultimately completed.  We have a 3D model of the terrain that fits well with what the actual terrain looks like.  We also have a better knowledge of the various interpolation techniques that can be done in ArcMap.  If we were to do the project over again, I think that we would have been more careful to make sure that the elevation points that we were taking when we were adding coordinates to our survey made sense when compared to the coordinates that we already had.

Field Activity 1, Terrain Creation and Survey

Introduction

In this field activity, we were divided into groups to accomplish two main objectives.

• Create a three dimensional terrain using garden planters.
• Survey this terrain to collect information on elevation that will be used for mapping.

Additional requirements in completing this activity were

• The terrain should include a hill, valley, ridge, depression, and plain.
• The survey should be recorded into an excel document with X,Y, and Z coordinates to be later imported into ArcGIS.

We had a fair amount of flexibility in this activity, being able to decide when, and how to build our terrain and complete our survey.  A large purpose of this activity was to work on critical thinking skills, and practice working together as a team.


Methods

Our methodology was largely based of the work of previous groups who did this activity.  We read over some of their blog posts and made several decisions about how we would go about this activity including

• The border of the garden planter would be the baseline, all of our terrain would exist below this line so that all of our Z coordinates would be negative, and easier to record.
• We would make a grid using thumbtacks and yarn, and the grid would be 10cm x 10cm (we saw groups that did smaller grid cell sizes report that it made the survey very tedious). 

Our group met on Friday January 30th at 11:00 AM to create the terrain and take the survey.  Weather conditions were overcast, -9°C, with southwest winds at 3mph.  We used snow to create the terrain, making sure to keep all of the features below the edge of the garden, so that all of the elevation data we collected would be negative.



Photo 1.  Creating the terrain




Photo 2. Completed terrain

  Our next step was to create a grid with a coordinate system to survey the elevation.  We placed thumbtacks on the edge of the garden planter every ten centimeters, we decided we would disregard the last row of cells on the edge of the planter that were less that 10 centimeters so that we could keep our surveying consistent for all of the cells.  Our next step was to use the yarn to make a visible grid out of the thumbtacks.  Unfortunately we ran out of yarn, so we improvised, using a combination of yarn and measuring sticks in order to see the grids that we were surveying.  This method was not ideal, but it worked with what we had available at the time.  For each grid cell we took an elevation measurement at the center of the cell.  We also had a diagram of the planter with the grid drawn in, so that we could reference the X and Y location of surveyed point as well.



Photo 3.  Improvised survey methods, for collecting data on elevation

We wrote down the Z coordinates on a piece of paper with a grid.  A picture of the original document is shown below.

Photo 4. Copy of field notes

 
The XYZ coordinates are shown in the photo below




Photo 5.  XYZ coordinates

Discussion

Despite some minor setbacks in the actual creation of a grid, our group was able to complete an elevation survey of the terrain we had made within approximately an hour.  Our group was successful in communicating with each other, and working towards a solution for the problems we faced.  Being able to critically think and work together was essential in collecting the data.

There were a couple of concerns that I had when collecting the data

• Whether the 10cmX10cm grid cell will give detailed enough information for the next activity.
• Whether our improvised methods will lower the quality of our data.

For each of the concerns, viewing other blog posts increased the confidence that I have that our collected data will be accurate and detailed enough for the next activity.  Our methodology matched closely with the methodology of other groups.  The 10X10 cm grid cell seemed to be a decent grid size that other groups chose.  A 5x5 grid cell would have given us more information, but it would have also been tedious work surveying all of the points and transferring it into an excel document. Other groups also dealt with an incomplete physical grid due to lack of yarn, and mention any concerns with the quality of their data.

We could have gotten more detailed information, and taken more precise measurements (rounding to the nearest half centimeter instead of the nearest whole centimeter), but for the most part I think that we did collect good enough data, and that we did so in an efficient manner.  The best way to evaluate our data will be in the next exercise when we create the DEM.  However, I think that our data will be accurate enough to make a good model.

Conclusion

The activity gave as practice with collaborative data collection, and critical thinking while solving problems.  The ability to choose our own methodologies made the project more challenging, but also gave us flexibility.  Our group was able to successfully work together to accomplish the objectives given in the assignment.  We produced a terrain with the necessary features, and we took a survey of this terrain to give us coordinates that can be used in the next exercise.  I found the lab useful, and thought that it was a good way to demonstrate what issues to be concerned with while collecting data for a project.