Field Activity #10 Sand Survey

Introduction

This field activity is of data processed from Field Activity #1. Field Activity #1 was creating a Digital Elevation Surface in a sandbox at the University of Wisconsin-Eau Claire. The basic overall idea was to create an elevation surface within a sandbox and create sample points based on elevation. The terrain had to include: ridge, hill, depression, valley, and a plain. The author of this blog and group members decided to replicate a continental shelf/ ocean floor landscape. In total 361 data points were gathered from an area of 114cm squared grid. This help create the data to be normalized. Normalization is the process of organizing data in a clear and non-redundant manner for use. Since the data points were already measured in x,y,z the data was already normalized by using negative and positive numbers displaying the z-elevation. Figure 1. Shows the sand data points normalized. For the grid sampling a systematic sampling method was used that created an easy way to gather data and allow for the use of interpolation on it. Using different interpolations on the data created 3D models that exhibited how each interpolation interpreted the data with varying rules implied. Using the interpolations of the data will help distinguish the best one to exhibit the sandbox data points. The required interpolations to use were:  IDW, Natural Neighbors, Kriging, Spline, and TIN.

Figure 1. Sand data points example

Methods 
The first step was to import the x,y,z data points in an excel file to ArcMap by going go File--> Add XY Data. Since the data was taken with a cadastral or unknown/none coordinate system it did not have to be projected in any way. Figure 2. Shows the sand data points in the grid format and color coded blue for the ocean in compliance with the elevation difference in a continental shelf/ocean floor. The data came in as shapefile and then was exported as a feature class in a file geodatabase to run tools on and analyze the data easier.

Figure 2. sand points in grid format

The Interpolation tools used were found under the 3D Analyst-Tools-->Raster Interpolation--> IDW, Kriging, Natural Neighbour, and Spline. TIN was found under 3D Analyst Tools-->Data Management--> TIN--> Create TIN.

A description of the tools used are listed as followed from Esri's GIS Dictionary and ArcGIS Pro Tool Reference.

"IDW (Inverse Distance Weighted) tool uses a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. The closer a point is to the center of the cell being estimated, the more influence, or weight, it has in the averaging process" (Esri 2017)

"Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values. More so than other interpolation methods, a thorough investigation of the spatial behavior of the phenomenon represented by the z-values should be done before you select the best estimation method for generating the output surface" (Esri 2017).

"Natural Neighbor interpolation finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas to interpolate a value (Sibson, 1981). It is also known as Sibson or "area-stealing" interpolation" (Esri 2017).

"Spline interpolation method estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points" (Esri 2017).

"TIN (triangulated irregular network) A vector data structure that partitions geographic space into contiguous, non-overlapping triangles. The vertices of each triangle are sample data points with x-, y-, and z-values. These sample points are connected by lines to form Delaunay triangles. TINs are used to store and display surface models" (Esri).

Once all of the Interpolations were created in ArcMap they were then imported into ArcScene to create a 3D modal of the terrain. From there they were exported as a 2D image into Adobe Illustrator along with the interpolations feature classes in ArcMap. Adobe Illustrator was used for its flexibility with creating map components, color resolution, and easy rotation to show model orientation. The 2D images were imported into ArcMap as a reference modal but the colors of the bands showed up too poorly to analyze the terrain so Adobe Illustrator was used in place of.

Results/Discussion

When creating the surface terrain a continental shelf and the ocean floor was thought of two best portray in the sand so the color chosen for portraying the data was blue. Figure 3. Shows the first interpolation tool used, IDW. In this figure and all others that follow the raster file with the interpolation created in ArcMap is on top with a scale, legend, and arrow in orientation, while the 3D modal in ArcScene created and exported into Adobe Illustrator as a 2D is on the bottom with a legend and arrow in orientation. A scale was not assigned to the 2D image for the representation of it being 3D and not fitting correctly with a scale unless measured from one point to another. For the purpose of this examination the author of this blog will be relating to the 2D model on the bottom of each figure. The IDW Interpolation tool did a poor expressing the data points collected, it doesn't represent a continuous modal of the terrain and did a bad job with exhibiting the ridge and valley in the southeast section. It exhibited the depression and plain well given that it uses the average sampling method of that area and it would have minimal difference in elevation. 

Figure 3. IDW interpolation

Figure 4. Shows the Kriging Interpolation tool. Kriging gives a nice continuous terrain modal that creates a realistic terrain. It displays elevation quite well with the hill and ridges in the eastern sector but does not with the valley right next to the ridge that seems nonexistent.

Figure 4. Kriging Interpolation

Figure 5. Shows the Natural Neighbour Interpolation. This interpolation looks very similar to Kriging in Figure 4. The only difference seen is with more detail in the elevation compared to Figure 4. However this still does a poor job with the valley depression and gives off a shadowing effect from the ridge. 

Figure 5. Natural Neighbour Interpolation

Figure 6. Shows the Spline Interpolation tool. This tool compared to Figure 5. Gives a more refined look to the elevation and rounds off the rough edges compared to the Natural Neighborhood Interpolation. The Spline Interpolation provides the best presentation of the valley right of the ridge in the southeast sector. There is a somewhat clear depiction of elevation change with the terrain presenting a more 3D look than any of the other models.

Figure 6. Spline Interpolation

Figure 7. Shows the TIN method/Interpolation, although TIN is not exactly Interpolation it still provides the data to be exhibited in a terrain modal with x,y,z points. This method gives a choppy presentation of the elevation difference in comparison to the real world. It does provide great detail in elevation difference with the contour lines.

Figure 7. TIN Method

Conclusion

The best Interpolation method used to display the data accurately was the Spline Interpolation. The Spline Interpolation tool exhibited all forms of elevation the best compared to any other tool. Since the author of this blog and their group used the systematic sampling approach the Spline Interpolation represented it the best. The Spline Interpolation tool and other other Interpolation tools/methods can be used in trying to display x,y,z data gathered for the intent of 3D modelling. It is easy and quick tool compared to running other processes on data. 

Reference

http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/comparing-interpolation-methods.htm

https://support.esri.com/en/other-resources/gis-dictionary/term/tin