In the second part of the terrain survey exercise, we were asked to conduct three different steps to complete the analysis of the terrain that the group formed in part 1. First was the importation of the X, Y, Z coordinates and creating a feature class. Next, 3-D analysis tools were used to visualize the terrain in various ways. Lastly, the group was able to resurvey areas of the terrain to help improve weak areas on the visualization and choose the best analysis tool for the given terrain. To visualize and analyze the terrain, we used the following tools in ArcMap:
- IDW
- Kringing
- Spline
- TIN
- Natural Neighbors
Methods
The second part of this lab exercise started with us preparing our spreadsheet into a format that could be easily imported into ArcGIS and be created into a feature class. The spreadsheet was put into an Z,Y,Z format and then formed into a new feature class in ArcCatalog. The feature class formatted the 276 data points into a formation similar to the grid used on the sandbox during the survey (Fig 1).
Figure 1. Point formation formed by ArcMap after the importation of the X, Y, and Z values to a new feature class. This formation is similar to the grid used for the survey.
The next step was to use the 3-D analysis tools in the ArcMap toolbox to begin the visualization process. The raster interpolation tools were used to visualize the date. Interpolation is the method of estimating surface values at unsampled points based on known surface values of surrounding points The tools used included IDW, kringing, natural neighbors, spline, and TIN. Once the 3-D tool had been preformed on the feature class, then the data was saved to a layer and opened up in ArcScene. ArcScene takes the layer information and projects the data into a 3-D version.
IDW is a 3-D analysis tool that interpolates a raster surface from points using an inverse distance weighed technique. This technique has its disadvantages because it is limited on the range of values used and is weak when the highest or lowest extremes have not been already sampled. But it has advantages when the sample points are dense, but is limited to a maximum of 45 million input points. Figure 2 shows the IDW method in ArcSecne.
Figure 2. 3-D view of the IDW interpolation method in ArcScene of the terrain surveyed.
Kringing is the interpolation method that uses a processor-intensive process. This may be a down fall becasuse the time of processing depends on the number of points and size of the search window. One advantage is that an optional output of prediction variable can be calculated to evaluate the need for more data points. Figure 3 shows the terrain using the kringing method showed in ArcScene.
Figure 3. 3-D view of the kringing interpolation method in ArcScene of the terrain surveyed.
Natural neighbors is the interpolation method that imploys the use of a natural neighbor technique. Disadvantages of this technique is that it has a limit of 15 million data points, and it is reccomended to study the area in sections rather than one large area. It is also recommended that the data be in a projected coordinate system than a geographic coordinate system. Figure 4 views the natural neighbor method in ArcScene.
Figure 4. 3-D view of the natural neighbor interpolation method in ArcScene of the terrain surveyed.
Spline is the method that interpolates a raster surface using a 2-D minimum curvature spline technique. A advantage of this method is that the smooth surface passes exactly through the impout points and with a larger number of points, a smoother surface can be formed. Figure 5 shows the spline method in ArcScene. We determined this to be the best method for our surveyed terrain sample.
Figure 5. 3-D view of the spline interpolation technique method in ArcScene of the terrain surveyed.
A TIN creates an image using a triangular irregular network. The tool used was Create a TIN in the 3-D analysis tools and must be used with a projected coordinate system. It is also recoomended to limit data points for display performace. Figure 6 shows the 3-D TIN in ArcScene.
Figure 6. 3-D view of the TIN interpolation method in ArcScene of the terrain surveyed.
After all interpolation methods were performed, the group analyized each one to decide on the one with the best fit for our surveyed terrain. As mentioned above, the group decided that the spline method was the best fit due to the smooth visualiztion fitting our hills and ridges well. There was one area that was seen on all of the 3-D visuals that did not fit the terrain well. The valley/ridge was shown as a tall point on the map, rather than a long, narrow spot. The group resurveyed the terrain in the 60 x 50 cm area, and to increase the visualization, twice as many points were collected in 5cm intervals, rather than the original 10cm intervals and inputed for that area. The new grid is shown in figure 7.
Figure 7. Point formation formed by ArcMap after the importation of the X, Y, and Z values. The condensed area of points is the area that was re-surveyed after the inital visualizations.
After the new X, Y, and Z points were inputed and a new feature class was processed, the spline method was used again and viewed in ArcScene (Fig 8).
Figure 8. Refine of spline method after additional data points were collected. The image is viewed in ArcScene.
Discussion
The 3-D visualizations seemed to be pretty accurate for the terrain that we created and surveyed. It is definitely accurate to say that a symmetrical grid is always needed. Areas with large terrain shape change, like a hill or valley need more input points, while flat areas need less. Also, the more points collected, the more accurate overall. Although I think we did a pretty good job on the number of points collected in most areas, but some areas of large terrain change could still probably use some resampling to obtain more data points.
Some implications of the exercise could include the measuring of our grid system since precise instruments were not used, but also the values in the spreadsheet. It is very easy for a person to type a wrong value in or miss hit a key on the computer. A large implication of the resampling process is that the resample was done one week after the original survey. Snow was used to build the terrain, so depending on the weather, the terrain could have changed in height over the week.
Conclusions
This was a great exercise overall. It was definitely interesting to be able to use and see how the software works from our own creation. Although all visualizations worked well, some worked better then others. The spline was best for our terrain, but I think the largest question is looking at the type of data you are using and how many points were collected.
No comments:
Post a Comment