Development of a Field Navigation Map

Field Activity #3 part 1

    The start of our third field activity deals with learning our pace count, determining what all to place on a map, and the creation of two maps for future parts of this field activity. By combining all three of these in a future part of our lab, we will be able to locate and navigate to waypoints on the maps we created and find them in the woods. 

    A pace count is a means of measuring distance over a period of time. By using the scale on our maps, we should be able to combine that with the knowledge of our pace count to complete the location of the way points. The way we determined what our pace count was was by stretching two tape measures out all the way. The tape measures were each 50 meters long, so we had a total of 100 meters. When walking to learn your pace, you want to count with only one foot. I used my right foot, so every time my right foot touched the ground I counted, 1, 2, 3, and so on. I recorded my pace count twice and each time it was 65 1/2 steps to cover the 100 meters. Now that I had my pace count it was time to create the two maps I would use them with. 

Before I could create my maps, I first needed to look at the data that we would be using. When viewing the data the two major keys I took into factor when choosing was what was the coordinate system and what projection the data was in. The coordinate system for the first map was the NAD83 Wisconsin Transverse Mercator, with a Wisconsin Transverse Mercator for the projection. NAD83 is a highly used coordinate system in North America. The Transverse Mercator projection stems from the Mercator projection. This projection delivers a high accuracy within different zones less than a few degrees apart.  Knowing that this coordinate system and projection will allow me to view my study area, I used the same projection on the second map and changed the coordinate system slightly. I used the NAD83 UTM Zone 15N coordinate system. This systems is smaller and would give a little better real world showing on the second map. 

    The data that was used to create the maps were provided from our professor. The data was from the Priory database that contained pictures, points, contour levels, and research boundaries. The first map I created I decided to use real time color imagery of our research area. Although it's hard to make out different types of trees in the wooded area, I am able to see different stands of vegetation along with fields, buildings, and water. This first map used the NAD83 Wisconsin Transverse Mercator coordinate system. Although this coordinate system is defined to this area, my second map uses a more defined coordinate system. Knowing that this wasn't enough by itself, I decided to overlay the research area with two foot contour lines. This would allow me to know if I am going up or down hill when in the field. I feel that having the contour lines on the map is the most important feature class to use. On the first map, I have also included the research boundary area with a red box outline, have the north arrow, and also have a scale bar. With know my pace count now, I can use the scale bar with a few conversions to know close to the area that I will be at. In figure 1 I have my map 1. 

 Figure 1

    The second map I created was similar to the first map. I used black and white color imagery that used the NAD83 UTM Zone 15N coordinate system. This coordinate system is smaller and finer compared to the first maps coordinate system. Like in the first map, I also included the two foot contours allowing me to show elevation changes. I also included the scale bar, north arrow, and research boundary area again. With these two maps and my pace count, I should be able to locate the waypoints that we will use in a future part of this lab. Figure 2 shows my second map. 

 Figure 2

    Like I have stated before, this is only the first part to this field activity. With the creation of my two maps, along with my partners maps, and my pace count, I should be able to use these to locate the different way points in the future of this field activity. Currently not knowing what my partners has on his maps, I am curious to see what his looks like and to compare. 

Visualizing and Refining Terrain Survey

Field Activity #2

    This field activity is a follow up from the previous weeks field activity of surveying the terrain surface. In the previous field activity we were asked to create a terrain surface in one of the planters boxes in the courtyard. Since it is winter time out, we used snow instead of sand when construction our surface terrain. After construction our surface, it was now time to figure out the best way to survey the area. We used a systematic sampling method, which allowed us to overlay the terrain with a grid so we could take measurements for all of the different types of terrain. Figure one shows the grid system that we used. It was constructed with yarn and thumbtacks.

 Figure 1

While we were getting measurements for our entire terrain model, we were also recording them onto a piece of paper. We got the x the y and the z coordinates for 180 different points in our flower box terrain model. We then were able to transfer all these points into an excel file so we could eventually bring those points to life in ArcMap and ArcScene.

    Now the real fun began of being able to display these points in ArcMap allowing us to render a 3D display of all our points. When we first brought our points into ArcMap and Scene, all that came across were the points in a grid format. We used five different interpolation techniques that would allow for us to show these points in a 3D format. The first interpolation technique that we used was the Inverse Distance Weight technique. IDW determines cell values using a linearly weighted combination of a set of sample points. One reason why I didn't like IDW was because it doesn't take into effect the areas in between two points. For instance, look at the top of the mountain in figure 2. Our terrain model was more of a plateau on top of the mountain, not two different peaks.

Figure 2 shows the Inverse Distance Weight interpolation technique.

The next interpolation technique we used when dealing with the data was the Natural Neighbors method. This method finds the closest subset of input samples and puts them into a query point and applies weights to them based on proportionate areas. This technique works best when there are data clusters in the data set points. Figure 3 shows the natural neighbors interpolation method.

Figure 3 shows the Natural Neighbors interpolation technique. You can see how the surface of the terrain is much smoother in areas compared to the IDW method. It's also worth noting that the top of the hill is also more of a plateau like it actually was in our terrain.

The third interpolation technique we used was the Kriging method. This method is Gaussian process regression that models the points based off of their prior covariance's. The assumption is that there is some type of correlation between the points. Without there being any correlation the points would be lost and would be put randomly onto the map. Figure 4 shows the data in the Kriging interpolation method. This is one of my favorite techniques however when I turned the 3D model onto the side the hill and the ridge ended up being rather pointy, and this is not how it was modeled in the actual terrain.

Figure 4 shows the Kriging interpolation technique.

The fourth interpolation technique we used was the spline method. This method estimates values using a mathematics function that minimizes overall surface curvature, resulting in a smooth surface that passes through exactly the input points. To me this was the most visually please technique that we used.  Figure 5 shows this technique.

Figure 5 is the Spline interpolation technique. You can see that there is no pointy areas rather everything is smooth and blends into one another. This was one of the main reasons why I believe that this one was the most visually pleasing technique we used.

The fifth and final interpolation method was used was the TIN method. A TIN is a Triangular Irregular Network that uses non overlapping triangles allowing to show the surface features. This tends to be very rough and not pleasing to the eye, but it does display surface data. Figure 6 shows the TIN created from the survey grid.

Figure 6 shows the TIN interpolation technique used.

After creating and using these different interpolation techniques, we were asked to look at our data again and to see if there were any areas that we felt would benefit from having more points in that area. Of course there is always room for improvement when looking at data like this so we decided to concentrate on the hill, portions of the valley, and the ridge in the back.

We devised a way from looking at our data that we already have and the area in the planters box outside that we were able to get 16 more points of x,y, and z data for our terrain. After getting this data we put it into another excel sheet and imported it into Arcmap which would allow us to overlay the data onto the previously displayed data points. Figure 7 shows the new point data we collected from our planters boxes.

 Figure 7, With this data being in between other points, it will allow us to give a better representation of the areas that have steep slopes.











Survey of Terrain Surface

Field Activity #1

    We were asked to conduct an elevation of surface terrain in one of the courtyard planters boxes for our first field activity. We were broken into different groups and had to learn on the fly on how to visually make 5 different landforms in the snow since it was winter time. We were asked to create a ridge, valley, hill, depression, and plain in our planters box. Upon completing these different landforms, the fun part of creating a way to sample all these landforms came into play. Sampling is a way to represent an area that you are trying to investigate. While we were sampling in a spatial perspective, this meant that we were looking at an area, our planters box surface terrain, and wanted to figure out the best way to represent our 5 different features that we created. 

    The sampling method our group choose to use was the systematic sampling method. The systematic sampling method seemed to be the right fit for this field activity, because we wanted to make sure we covered the entire surface area and was able to show the different land formations. By using a grid system we were able to conduct and capture points that would allow us to show the different landforms and there placement along with their elevation or lack there of. Figure 1 shows the grid system that we placed over our landscape area. Figure 2 also shows the grid system that we placed over our landscape, but it is from a different angle displaying the elevation better. 

Figure 1

Figure 2

    The planters box we used ended up being 122 cm wide and 88 cm long, with strings being placed across our surface terrain every 8 cm's. With only using string, thumbtacks, rulers, and a marker, our group was able to place an accurate grid over the top of our surface terrain. One problem we encountered came when we were placing the grid string over the top of the hill and ridge. It was difficult to keep the strings straight at these areas, but with a little measuring we were able to keep them in place. Another problem we ran into was dealing with the elevation and were zero elevation was at. We decided to have our zero elevation at the top of the planters box. This would allow us to show elevation above and below zero. Since the top of our planters box was absolute zero elevation, most of the starting and ending points are zero, but there were times that it was below the zero elevation when it came to the valley and backside of the ridge. The easiest way we found to record our points was to have our points spaced 8 cm apart along the string as well. With the string being zero elevation we were able to record our x and y points along with our z point, or elevation. We entered all our points into a table and then transferred them into an excel spreadsheet. This would eventually allow us to import these points into ArcMap, allowing us to configure our landscape. The string was only considered zero elevation when there was no interference from the landscape that we built. If there was interference, for instance on the hill, we then used our rulers and a small level allowing us to measure from the edge of the planters box up to the top of the hill while maintaing a level ruler. In figures 5 and 6 this technique of measuring is displayed. This may have not been the most accurate way, considering it was freezing outside and one of the rulers could have been off, but with watching the level we were able to get the points as accurate as possible. Figures 3 and 4 are what our landscape looked like before placing the grid over the top of our landscape. 

Figure 3

Figure 4

Figure 5

Figure 6

    After getting the measurements for our entire grid we came out with 180 different points. Some of these points we positive values, some of the points were negative values, and some of the points were at zero elevation. Obviously the positive points were related to the hill and the ridge in our landscape, but since no terrain is flat, there were also areas were there were small raised areas. This also had to do with that we were dealing with snow and not a hard surface to measure from. Since the hill and ridge was from the positive points, the negative points came from the depressions and valley in our landscape. Looking back at figure 4 one can see just how uneven the landscape really was when building it with snow. Our greatest negative elevation we recorded was negative 8.5 cm's. This was the lowest part of the valley that we created. Our highest point was on top of the hill we created with a point recording of positive 19.5 cm's. We did come across two points on top of the hill that were both positive 19.5 cm's, this lead us to believe that the top of the hill was relatively flat for at least 8 cm's. 

    We used the systematic sampling method from start to finish when conducting our sampling technique. Once we got through the first few grid strings, we found our groove and were able to bust out the rest of the grid with relative ease. The only issue we came across was freezing fingers and toes. We found it best that we would do three or four strings at a time then go inside to warm up before taking on more strings from the grid. This would allow the accuracy of our measurements not to be faulted from the shaking of cold hands and feet. 

    The systematic sampling method that we used relates with the sampling definition. Sampling takes only pieces from the entire area of interest and allows those pieces used in the sample to paint a relative picture of what the entire area would look like. Now of course there is no way to plot every single dip and ridge throughout our AOI, but with the systematic sampling technique we choose, we let the points do the talking. By moving the points closer from 8 cm's, to say 5 cm's, yes we would have shown more points and had a better picture. However the question when it would come to a large scale AOI would be, does the time, money, and effort justify the means. For this first field activity I would say no. Sampling is just another way to gather data over an AOI that you are looking at. It is also a very effective way to gather that data when used properly.