Field Activity #2: Visualizing Our Terrain Survey

Visualizations of Our Terrain Survey Data

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. 

Field Activity #1: Collecting Data

Elevation Surface Model Creation

For this experiment, a topographic model was constructed from sand in courtyard garden planter boxes to create an elevation surface of terrain in order to practice collecting, analyze, and interpret geographical information. This simulation explores situations in the field where there are multiple ways to approach conducting a terrain analysis and challenges our critical thinking skills. Using grid lines created by string and pins, measurements were taken using a meter stick and a clipboard for recorded measurements. Through the data collected, a map will later be developed showing the topography of the surface created using a selected coordinate system in later exercises.

Methods:

September 18^th, 2019

Sunny, about 21 degrees Celsius.

Firstly, the class was divided into two groups and discussed ideas of how to create a possible terrain. Afterwards, we headed out to the courtyard garden planter boxes filled with sand to build our terrain. Using spoons and our hands, we created a variety of terrain including a ridge, hill, depression, valley, and plain.

After our terrain was complete, we constructed a coordinate system using string and thumb tacks. The thumb tacks were displaced every 6cm around the rim of the box. We then stretched the yarn around the pins to successfully create our coordinate system.

Next, one group member collected samples in centimeters using a meter stick within each 6cm x 6cm box, producing a grand total of 200 samples. Lastly, after cleaning up from our experiment, the member who recorded the measurements recited them aloud while another typed the data into an excel document, which was later saved as a .csv for later experiments.

Discussion:

The simulation created during this exercise was very insightful, because it showed that there were many different ways to conduct this experiment. It helped develop critical thinking and teamwork skills, something that is parallel to full scale field experiments. We also faced some challenges, such as multiple people recording measurements. Consistency is important in professional experiments and is something to take note of in our error analysis. To decrease the risk of error in our experiment and in future experiments, we found that one member of the group should be taking the measurements the whole time.

Conclusion:

This exercise explores situations in the field where there are multiple ways to approach conducting a terrain analysis. This experiment overall challenges our critical thinking and teamwork skills. Personally, I found that when conducting an experiment in the field, it is important to plan out the methodology of the experiment before heading into the field.