Visualizing and Refining Terrain Survey

Background:

As we ventured out in the field this week our class was tasked with revising our original field surveys using a different more refined method of surveying. After all groups had completed the first sandbox our class collectively went out to inspect and critically investigate how groups surveyed and overcame any issues with designing and collecting the original data points. We found that most groups had decided to use a uniform grid survey technique. We also found that most groups to counteract the negative numbers had added the width of the board to raise the original base of the box. Both of these techniques is what our group had used to collect the original data points. Once we had discussed the ways we had surveyed the sandboxes we now where tasked to find more precise ways of survey elaborate features to accurately display in 3-D imagery.

Figure 1.1 original formatting for collection of data points did not work for importing to Arc

Figure 1.2: Desired formatting for Arc GIS all columns are same data and each column does not included equations.

Methods:

After all of our data points where collected we needed to display our sandbox in Arc scene 3-d imagery. To do this we needed to import our Microsoft Xcel table to Arc in order to display our points. However, being inexperienced and new to using our own data our group was not aware that a specific format needed to be implemented in Arc to be able to import the data. Our original table was set up to have easy input of each z value so we started our grid in the top left corner of the sandbox and laid the grid a-e from top left to top right and 1-14 from top left to bottom left (figure 1.1). We had thought since this layout was the same as a excel sheet is formatted it would be easier to input the values. What we quickly found out is that we needed to have a table with three columns of like attributes for the input process to work. (Figure 1.2). You can also see in figure 1.2 that we changed our alphabetical labeling to replicate our grid inches to be increments of eight. Although this change now allowed us to import the table and display the data it was not the table we needed. We found when we displayed the data that our features in our sandbox where all inverted. After trouble shooting the issue we found this was due to our original false origin being placed in the top left corner. If we wanted to keep the original false origin we would need negative x-values for our grid which you cannot have in your table so we had to move the false origin of our grid to the lower left corner of our plot. Lastly our group found it to be easier to change the table grid to replicate our centimeter values of our sandbox meaning the table would go 1-120 in increments of eight centimeters. It was this change that allowed us to make it easier to input more defined values in our new stratified survey. We had decided to stratify within our original grid marks which made it possible to get accurate height values on any area with features without having to survey more points in areas not holding significant features.

 

Results:

Once we had brainstormed and resolved all of our complication of our original data we were able to display our sandbox features. For the first set of data points we were asked to generate 3-d images in five different interpolations, consisting of Inverse Distance Weighted (IDW), Natural Neighbors, Kriging, Spline, and Triangulated Irregular Network (TIN). Once displayed we needed to determine which one was the best representation of our survey and why it was the best fit.

IDW: Inverse Distance Weighted is a interpolation is usually used with surveys that has a very localized area with many data points. It uses a range of values to interpolate the true features and is limited for it can only range from the highest and lowest values of your data table. It is because of this limitation that the IDW was not one of the better choices for our sandbox display. The display was very inconsistent and had a lot of pore ridges as seen with the bumpy surface below.

Natural Neighbors: Nat Neighbors was a little better representation for our model for it does not interpolate any ridges, pits or hills that do not show up in the data. It uses a notion of “area-stealing” meaning that it finds an average height from all surrounding data points to infer what the feature looks like. This allows for smoother surface but also leads to shifts and variation from original location of features.

 Kriging:  Was to this point the best method it uses a very complicated statistical method of finding a relationship with the z-values to design a accurate workflow and display of the features. It is because of the relationship statistics that allows one to have greater confidence in the accuracy and predictions of the kriging model. Although it is not the best fit for our sandbox for it is better used when there is a spatial tie to the survey or a directional bias.

Spline: Spline or thin plate interpolation is one of best models to use when you find yourself surveying gently sloping areas and features with gently elevation change as we were with our sandbox features. Once modeled spline minimizes overall curvature which gives a gently surface to the display as you can see in the image. It is the smooth surface and best display that made us pick to use spline for our second survey.

TIN: Triangulated Irregular Network uses raster and our defined z points to design and connect multiple triangular points that are connected to show your features. However, with little data points our TIN became very rough and didn’t fit our field feature very well.

Second survey:

For our second survey we decided to redesign new feature since our original design was destroyed by the weather. However, we keep all our same layout and grid patterns (image 1.3) but with our new knowledge set our false origin in the lower left and input the data with three columns and grid of increments of eight centimeters but stratified in all areas where there where features. The final product was displayed in a spline model for it best represented our features.

Image 1.3 Grid pattern of second sandbox trial with new features. We measure all stratified areas ever 2cm where needed

Conclusion:

 Overall this project was very helpful to our group for it allowed us to see firsthand how critical prep work is and proper formatting when collect data point in the field. We found that if you try and cut corners or take an easy route it can cause you a lot of pain and time in the long run fixing your mistakes. I hope we can continue to get a lot of filed work for this way of learning is pleasing and laid back way to learn.

Discussion: 

The only negative parts to this lab for me was the challenging landscape in which we were given to build our features. I wish we could have had a closer location to build and preserve our features. If this was accessible it would be easier to make more elaborate features knowing you could keep them same if you needed longer time to collect points. 

 

Sources:

 

ArcGIS help online

Exercise 1: generating digital elevation models

Geography 336 Sandbox creation

Creating and surveying points throughout a man made diagram

Dillan Berg

Background: 

Figure 1.1:   Sculpting our sandbox to specifications
 previously determined

The task our group was faced with was to design and survey our own structures within a 114cm x 122 cm sandbox. The parameters that our group was given was that we needed to included a ridge (elongated structure with a high point), valley (depressed that is longer than it is wide), a depression ( inverted hill), hill ( a elevated area that is not as long as it is wide), and a plane ( a flat area that ranges over a long distance).After construction of the features where built we were tasked to come up with our own form of surveying the features accurately and efficiently. Meaning we needed to have enough points to accurately show all the features but not to many to over collect and have faulty data. 

Methods:

We decided to take our total box which was originally 114cm x 122cm and cut off 1 cm from each side so we could accurately measure each grid. The new area of the box is 112 cm x 120cm.We then made grinds on both sides every 8cm, this allowed us to have a total of 210 sections within the total area to measure for elevation. We decided to keep accurate measurements by labeling the top of the grid by letters and the side by numbers. We then made each measurement within the grid in the lower right corner of each square. Since the features where all below the string we collected all negative numbers. To make the analysis easier in future applications we measured the width of the boards and added the width to all collected measurements. This helped by making all of our data points positive numbers but also keep accurate measurements of the true heights. 

Figure 1.2  Demonstrates the grid layout and our collection methods.

By labeling the top in letters and side in numbers it allowed for quick and easy entry into our excel spreed sheet. Once in excel we where able to transform the data by adding 18.4 cm to each measurement resulting in our final collection. 

Figure 1.3  Final data points of sandbox collected in cm

Discussion: 

Overall our collection went well we had very minor complication. We where able to set up design and collect all the points in one day. It was on Tuesday September 15, the weather was partly cloudy with a light wind and about 82 degrees. Since we where able to collect in one day we did not have to worry about our design being altered or destroyed. A few of our concerns with the collection where as follows. First we had some variation in collection since we where switching between two people measuring. Making it possible that one was measuring different than the other and could lead to slight variation in collection. Second we were not allowed to fasten the string to the wood with anything other than tape making it possible for the string to move or sway if you placed your hand on it. This allowed for some of the collection to be in the wrong spot from where we previously picked of lower right corner. Our last concern was that the area was not placed on a level ground which could have lead to altered measurement where we where not collecting at a perpendicular down angle leading to larger lengths than what was the actual measurement. 

Conclusion:

This activity not only interesting but represented a lot of tools that we will need for the rest of the semester. Some of those tools being critical thinking, problem solving, adapting to the surroundings, team work, and precision collection. The task covered all of these areas which we will be faced with for not only the rest of the semester but also into all of our careers. We where given a task with very little direction. I really enjoyed this activity and hope they continue to be similar task given to us. The task was not only challenging but continues to work on key skills that will advance any student to greatness in the workforce.