For this portion of our experiment, we continued working on our survey that we had conducted previously from sand courtyard garden planter boxes to create an elevation surface of terrain. Using ArcGIS/ArcGIS Pro, we studied our data using various raster interpolation methods to interpret the results from our experiment and find a realistic and "best fit" representation of our survey. Raster interpolation tools are used to create a continuous surface by connecting the points, using sampled z-coordinate values, to create a 3-D visual product of our terrain. The various methods used includes: IDW (Inverse Distance Weighted), Natural Neighbors, Kriging, Spline, and TIN (Triangular Irregular Networks). After experimenting with our previous data using these tools, we observed where data should be added in order to accommodate the interpolation method of our choice, and re-sampled our terrain as a team.
Methods:
September 25th, 2019
Partly Cloudy, about 20 degrees Celsius.
Our first step was to import our first sampled terrain data into ArcMap and create a point feature class from that data in a new geodatabase. Once the data was imported, we started to experiment with our data using multiple different interpolation methods, and different parameters within those methods. We decided to use a contrasting color in order to visually see a difference in the elevation of the data, as seen below. For each of the methods used, we created 2-D imagery in ArcMap before importing the shape file into ArcScene for a 3-D visual representation.
After experimenting with the data using these tools, we observed where data should be collected in order to accommodate the interpolation method our group choose. After re-surveying our terrain, we tested each interpolation method, as shown below using ArcScene, on our second time surveying our miniature terrain.
Inverse distance weighted (IDW) determines the cell values using a linearly weighted combination of a set of sample points. The weight is a function of inverse distance and the surface being interpolated should be that of a locationally depended variable.
Natural neighbor finds the closest subset of input samples to a query point and applies the weights to them based on proportionate areas to interpolate a value.
Kriging is a tool that assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. This tool fits a mathematical function to a specified number or all points to determine the output value of each location.
Spline is a tool that interpolates a raster surface from points using a two-dimensional minimum curvature spline technique. The resulting surface passes through the input points exactly.
Triangular irregular networks is a tool that triangulates a set of vertices. The vertices are then connected with a series of edges to form a network of triangles.
After experimenting with various interpolation methods with our new data, we decided to focus on the Spline method. We decided to add dramatic relief changes - on the valley, hill, and ridge. This involved some conceptualizing, as we had to rethink our data, our survey, and how to accurately represent our new model.
Discussion
Similar to the last exercise, it was very insightful as it displayed the multiple ways to approach looking at a terrain three-dimensionally. It was again challenging for our group, as we were required to decide a precise representation of our model between the various interpolation methods - we chose the Spline method. This method provides a nice, smooth curvature to the areas with low and high relief and still maintains the original values of the data. Previously, there was a larger plain, and less drastic relief, but with this new model, there is more variation in elevation and is ascetically pleasing to the eye - less negative space.
Conclusion:
This exercise explores situations in field analysis where it helps us to find new methods in expanding our research, and revisiting our data. This is something that is extremely important in field work, and when conducting a survey or research project for a large company. Overall, when revisiting data and modeling, it is important to discover error and consider new methodologies that would prove useful in expanding our research.
Conclusion:
This exercise explores situations in field analysis where it helps us to find new methods in expanding our research, and revisiting our data. This is something that is extremely important in field work, and when conducting a survey or research project for a large company. Overall, when revisiting data and modeling, it is important to discover error and consider new methodologies that would prove useful in expanding our research.
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