Lab 2: Visualizing and Refining Terrain Survey

INTRODUCTION

In this lab assignment we were tasked with visualizing and refining our terrain survey model. Using the same group of three we had during the first lab (myself, Alyssa Krantz and Evan Geurts) we had to go back into the field and recollect our data points using new and better techniques. Before we went out for the second time, we processed our original data. In order to do this we had to take our spreadsheet of data (see lab 1 post) and reformat it into three columns, X, Y, Z. Once the spreadsheet was reformatted to the proper format we were able to upload the document into our geodatabase. The original data had 156 data points. We then performed 5 different data interpolation methods on the data, including IDW, Natural Neighbor, Kriging, Spline, and create a TIN (definitions below). Once the previous tests were run in ArcMap, we were able to import the files into ArcScene and create a 3D model of our terrain. The image below (FIGURE 1) shows the five interpolation tests we ran, I will go into further detail on each of the tests later in the blog when I am talking about the new data we gathered.

Figure 1: The five different data interpolation methods used on our first data set. Each of the five methods will be explained into greater detail later in the lab write-up.

Our next task after processing our first data set was to go back out into the field and redefine our data gathering methods. On Wednesday September 23, the group met at 6pm behind the shed to gather our materials and head down to our study area, which was the same as lab 1 (FIGURE 2).

Figure 2: The study area for this lab was the same as it was for the first lab, under the University of Wisconsin- Eau Claire footbridge on the bank of the Chippewa River

We decided to use one of the 4 foot by 4 foot surveying boxes that was already assembled. We chose this because in our first attempt we spent well over 30 minutes trying to get the box to be leveled. Behind the shed we took the survey box apart so we could easily put it in the back of a car, and then we drove down to the bank of the Chippewa River under the walking bridge. As soon as we parked the car and got it unloaded the rain came and would not let up until we were about an hour into working. Using wing nuts, washers and bolts we assembled the box (FIGURES 3 AND 4).

Figure 3: Alyssa working on assembling the survey box

Figure 4: The box was held together by basic wing nuts, washers and bolts

Once the box was constructed and properly leveled out (FIGURE 5 AND 6) we were ready to start creating our features. We decided to make our features larger because they were barely visible in our first data set when we imported the files into ArcScene to create a 3D model (FIGURES 7 AND 8). We also believed the size of our grid pattern was at fault too, in order to combat that issue we changed our grid pattern from 10cm x 10cm grid boxes to a smaller size of, 5cm x 5cm.

Figure 5: Finished survey box completely assembled

Figure 6: Once the box was assembled we had to make sure it was level with the ground

Figure 7: The finished product with the features 

Figure 8: Our hill and depression were all in one, simulating a volcano with a creator 

The features included in this survey box were the same of those in lab 1. We needed a ridge, hill, depression, plain and a valley. Like previously mentioned, we wanted to make these sand features far larger than our first try. Once the features were all added to the sand box (FIGURE 7) we were ready to begin the next step of collecting data.

For our new method we decided to still use the string to help make the grid, but instead of making an actual grid, we only went one way and were going to use a ruler which we could easily move from tick mark to tick mark as the other axis (FIGURE 9).

Figure 9: A grid was constructed to collect data, one axis was marked using string and the other was marked using a ruler stretched and taped across

We found that using the ruler to measure of the string down to the surface was the best way we could go about getting an accurate elevation, so we stuck with that technique.

At this time it was getting late, the sun had completely set, and we knew it was going to rain tomorrow. So we had no choice but to keep trying to get our measurements in the darkness of night (FIGURE 10). Using flashlights we had brought with we finished collecting our 506 data points and took everything apart. As soon as we finished taking everything apart and brought the box back to my car the rain came and did not let up again until closer to midnight.

Figure 10: Darkness came quickly and we were forced to use flashlights in order to keep working

NEW DATA

Once dry, we typed up the data into a Microsoft Excel spreadsheet in the three column format (FIGURE 11). Again we used an origin in our northern corner of the survey box. This point was classified as (0,0) and every other point along the X and Y axis was 5cm greater in their respected directions. Again, in order to avoid confusion and having to use equations in Excel, we imported all of our Z values in negative numbers, thus not inverting our data.

Figure 11: All the data points were entered into a 3 column Microsoft Excel Spreadsheet 

Once the excel spreadsheet was finished, we imported the file into our ArcMap geodatabase. Setting the X, Y and Z fields to their respected columns we got our 506 data point grid into ArcMap (FIGURE 12). We then could run our five data interpolation tests on our grid, and eventually create a 3D model.

Figure 12: The 506 data points were entered into ArcMap and were ready to have data interpolation tools ran on the set

Below are the definitions and products of the 5 data interpolation tests we ran on our data,

Definitions from webhelp.esri.com

Inverse Distance Weighted (IDW): 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 (Figure 13).

Figure 13: Inverse Distance Weighted data interpolation ran on our set of data. This method uses averages from nearby points in order to fill in the "gaps" between  data points.

Natural Neighbor: Finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas in order to interpolate a value (Sibson, 1981). It is also known as Sibson or “area-stealing” interpolation. Its basic properties are that it's local, using only a subset of samples that surround a query point, and that interpolated heights are guaranteed to be within the range of the samples used (Figure 14).

Figure 14: Using the data interpolation method of Natural Neighbor

Kriging: Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. Kriging fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location (Figure 15).

Figure 15: Kriging Data interpolation method

Spline: The Spline method is an interpolation method that estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points (Figure 16).

Figure 16: Spline data interpolation method

Triangular Irregular Networks (TIN): are a form of vector based digital geographic data and are constructed by triangulating a set of vertices (points). The vertices are connected with a series of edges to form a network of triangles (Figure 17).

Figure 17: Triangular Irregular Networks (TIN) model of our survey area

DISCUSSION

 

Of the five maps created, each of them have their own pros and cons. The first map, Figure 13, used the Inverse Distance Weighted (IDW) data interpolation method. This method averages the value for the in between sections of the points. In this particular project I do not see any pros in using the IDW data interpolation method. As for cons, when looking at the image above it made it look like our ridge was not so much one smooth mountain ridge but instead made it look like it had several peaks. All of the features had a jagged/pointy aspect to it.

The second data interpolation method used, was Natural Neighbor. Natural Neighbor data interpolation is a good tool to use if we are working with smooth data. Natural Neighbor passes through all of our input data points, but the space between the points is treated as a smooth section. Unfortunately, several of our features lost their sharpness to this method. Natural Neighbor worked well on the plain section because we did not have any abrupt changes to the landscape.

The third method I used was Kriging data interpolation. This is my personal favorite one to use. Kriging uses far advanced math equations that I would not begin to know how to explain, but to sum it up, Kriging uses the surrounding points to spatially relate the data points to fill in the gaps. This results in a much smoother, realistic looking land formation. Kriging was the first data interpolation method to be able to slightly pick up our depression we built in the center of our hill.

The fourth method used was Spline. Spline data interpolation works similar to Natural Neighbor. Just like Natural Neighbor, Spline uses the exact data points and then uses mathematical equations to minimize curvature of the features. For the purpose of this lab, minimizing the curvature of the features will create an unrealistic looking landscape, and much like Figure 16 shows, our land features are very pointy and fake looking.

The final method used was Triangular Irregular Networks or TIN. In my opinion, TIN is the best method to use to show the features, and based upon the output, when compared to the rest of the images, the TIN one looks most like the actual sand box. TIN creates a series of triangles from the data points in order to create the land scape. Although this method lacks in the real smooth look as say the Kriging had, it makes up for it in being able to show precise features of the landscape.
 

Overall, this project touched on multiple beginning aspects of surveying and critical thinking. Other areas this lab introduced us to/jog our memories on, was using ArcMap and ArcScene to create 3D models of landscapes. We were able to recollect data in a better fashion and thus create a better 3D map of said data.

During our time working on lab 2 we did come across a couple issues. The first being the three of us all have such busy schedules, it was difficult to find a time we all could meet and work together on the data collection. The one time we could meet, which was when we did this caused many more issues. We got about half way through setting up our grid when it became way too dark to be able to see anything, luckily we brought flashlights with. Then while collecting the rain started, the rain caused our grid strings to become loose and also made it so our tape would not stick any more. Our last major issue we had was using the flashlights on our sand landscape. The shadow the light would create on the landscape made it very difficult to see where the ruler was and if it was touching the ground or not.

CONCLUSION

Although we came across several issues with losing light and Mother Nature, the overall project was a success. We achieved our goals of gathering a new survey data set, taking that data and turning it into an excel file, importing the file into ArcMap, running data interpolation tools on our collected data and lastly creating a 3D model of the data.

Hopefully next time we have to go in the field to collect data it won’t be rainy or pitch black out. 

SOURCES

webhelp.esri.com

Lab 1: Sand Box Terrain Model

Introduction

Welcome to my blog which will be used for the 2015 Fall Semester Geospatial Field Methods course offered at the University of Wisconsin-Eau Claire and taught by Dr. Joseph Hupy of the Geography and Anthropology Department. This blog will serve the purpose of portraying the course work we will be covering in class. Then be able to be show in a portfolio style blog to showcase my skills I have developed over my years at the University.

For more information on myself or other blogs I have put together show casing my geospatial capacity please visit my website at http://drakebortolameolli.weebly.com/

Our first assignment in the Geospatial Field Methods course was to create a Digital Elevation Surface model using a 4 foot by 4 foot box and sand we gathered down on the bank of the Chippewa River in Eau Claire, Wisconsin. Split up into six even groups of three we would have to build ourselves a landscape in the box and with the materials provided figure out how to create a surface model which could then be uploaded into ArcMap at a later time.

The purpose for this assignment was to gain knowledge on basic styles of surveying, be able to work in a team and to be able to think critically as we did not have any written instructions on how to go about doing our survey.

The Environment

Working in a group of three, myself, Alyssa Krantz and Evan Geurts, we worked in pretty perfect weather conditions. It was around 4pm when we started, slightly cloudy and a slight breeze, our area of study was located directly under the foot bridge crossing over the Chippewa River (north side) on the University of Wisconsin-Eau Claire’s campus (image 1).

Image 1: Study area under walking bridge at the University of Wisconsin-Eau Claire (Eau Claire, Wisconsin)

Methods

After transporting all the materials to our area of study, we set up our 4 foot by 4 foot box. None of the sides of the box were connected to the other, so for the first thirty minutes we tried to create a level surface between the boards (will be further addressed in the discussion section below)Image 2. The materials we had to use for this project included; 4 boards to create our box, fishing line, a leveler, marking flags, tape and lots of sand and rocks.

Image 2: Attempting to create an even level between different segments of the sand box

After setting up the box, something that took much longer than expected, we began creating our land features (image 3). Inside the box we were to include a ridge, hill, depression, valley and plain (image 4). Using our hands we dug through the sand creating the required features. We had to remove some rocks from the area because they were interfering with our landscape, while others stayed in the box to give it a more rough style landscape. The sand was difficult to work with, as it was pretty dry, making it very tough to create hills and ridges.

Image 3: Creating the landscape inside our box

Image 4: Inside our box we have several features including a ridge, hill, depression, valley and plain

The next step after creating all our features was to figure out the elevation of our terrain. The way we set up our box nothing was above boards. In order to make it simpler to work with we decided to treat our terrain as if it were the ocean floor, thus creating the board level as sea level.

Next, we had to come up with a way to record multiple points of elevation. We were not allowed to write on the boards so we put a layer of tape all around the box so we could have something to write on. We then measured 10 centimeter increments and placed tick marks along the boards. Since our boards were not a perfect square we ended up with 12 tick marks on one axis and 13 tick marks on the other. Each tick mark corresponded to the origin, in number and letter terms A1 was our origin (images 5-7). We used numbers and letters while writing down our data to make it simpler to record, but when put into an excel file we changed it to distance from the origin. 

Image 5(left): Using the orange string and tape we were able to create a grid across the entire box
Image 6(middle): The completed grid spanned over the entire box
Image 7(right): The B represents the second row of grid lines, the adjacent axis will have numbers

Using bright orange string we created a grid over our area of study, we forgot to bring scissors with us to cut the string so we had to tape the ends and then carefully loop around to the next tick mark. After creating the grid we had 156 crossing locations. Using a meter stick we measured the distance in centimeters down from the string to the ground point and recorded that on to a piece of paper, which would later be entered into an Excel Spread sheet (Image 8).

Image 8: Using a meter stick we recorded the distance from the orange string (sea level) to the top of the sand feature

In order for our data to be correct we had to put negative numbers in front of all our data numbers, otherwise we would have inverted data, meaning our highest points on the surface model would then show up as the lowest points in a digital model (image 9). This data will be used in the next part of the exercise when we import it into Arc and attempt to create a 3D model of our data.

Image 9: The data measurements (cm) from the sand box have been put into an excel file. The negative numbers represent below sea level. The numbers highlighted in green represent distance from the origin (A1). 

Discussion

Overall, this project did not seem to be like a geography class assignment, but instead a real world application on a small scale model. We learned about basic survey techniques, got to visualize how you can turn a sandbox creation into numbers which can be used to create a 3D map. Lastly, this assignment was a great way to not only work on team building but also problem solving and critical thinking.

We did manage to come across a few difficulties while working. One was trying to get our boards to be even with one another. We started off by using a leveler to make sure each individual board was level, but what we did not take into consideration was making sure all the boards were level with one another, so by time we got to the final board we had about a 2 inch height difference. This was definitely the longest section of the assignment.

The only other problem we came across was when we were measuring the distance from the string to the terrain feature. Every once in a while the string would stretch when touched so we would have to compensate or try to pull the strings tight again to continue with the accurate measurements.

Conclusion

The project I would say turned out to be a success. We achieved our goals of creating a terrain model in the sands of the Chippewa River, create a grid over the area, get accurate measurements of the terrain and put all our results into an easy to read Excel file.

Some may say it was not real surveying, but the task was not to use the correct style of surveying but instead be able to come up with our own accurate way using what little tools we had to work with.