Lab 13 - Effects of Scale

This lab introduced us to the effects of scale and resolution on spatial data properties. In the first part of the lab, we investigate vector data (hydro features) and in the second part we do this for raster data (elevation models).
Working with the vector data, we were provided with flow lines and water bodies at 3 different scales and at resolutions. After determining the total lengths of the lines at the 3 scales and the information about the water bodies polygons, I determined that basically, the smaller the scale, the more geographic extent is shown, but in less detail. It makes a bit more sense to me to think of it in representative fractions than verbally. 1:1200 means 1 inch on a map represents 1200 inches in the real world. You can get more detail on that than a 1:100000 map with 1 inch representing 100000 inches. I also found it interesting how the lengths, area, and perimeter of our spatial data changed with the change of scale.
In the second part of the lab, we investigated the effects of resolution on raster data. Using the original 1 m LiDAR data, we needed to resample it to create DEMs with different resolutions by changing the cell size. Determining the best resampling technique took some thought. I chose based on the type of data it is and the method I thought would maintain the elevation and slope characteristics the best. I thought the most interesting part of this was starting with the original 1 m LiDAR DEM and resampling the data to change the cell sizes, and watching the DEM have less and less detail as the cell size increased. Using the 90 m LiDAR DEM I created and the SRTM 90 m DEM I projected, I wanted to compare them in terms of their elevation values and derivatives. For this, I mainly followed the example in the S Kienzle (2004) paper, "The effect of DEM raster resolution on first order, second order and compound terrain derivatives." I investigated the difference in elevation between the two DEMs, the slope (first order derivative), the aspect (first order derivative), the profile and plan curvatures (second order derivatives), and the combined curvature (compound derivative, a combination of the second order derivatives). I'm used to the mathematical definition of derivative, which is a slope of a curve (geometric) or a rate of change (physical), and both uses are really seen here. This is why the curvatures made sense to me as the rate of change of the slope (the second derivative).
Before I got to the results, I needed to recall how the LiDAR and the SRTM DEMs were created. SRTM elevation data is created using synthetic aperture radar (SAR). This uses two radar images and uses the phase difference measurements with a small base to height ratio to measure topography. Wavelengths in the cm to m range are very accurate as most of the signal is bounced back. Heavily vegetated areas may not allow the radar to penetrate enough to get accurate elevation values and very smooth surfaces such as water may scatter the radar beam so elevation values can be inaccurate or unobtainable. LiDAR elevation data is created by an aircraft flying overhead transmitting a signal to the ground and determining the time it takes for the signal to be bounced back to the transmitter, which is then used to calculate an elevation value. LiDAR also has problems in heavily vegetated areas, but there are usually enough data points to interpolate pretty accurate elevation values. Additionally, there can be up to 5 returns per pulse, so this handles rougher and heavily vegetated areas better than SRTM does.

I used ArcGIS to create the slope, aspect, and curvature layers, which I then visually compared. Based on the way the slope is flatter where the streams are but how it quickly becomes steep on either side of the stream, it seems to me this could be an area where a stream cuts through a canyon before reaching the outlet. The SRTM DEM tended to have elevation values that were higher than those of the LiDAR DEM in lower elevations (streams) and lower values in higher elevations, so the mean slope of the SRTM DEM was less than that of the LiDAR DEM. I used other images to analyze the data as well, but below is a screenshot of the slope. We can see that the maximum slope of the LiDAR DEM is greater than that of the SRTM DEM.

I wanted to follow the example in the paper further and compare both to original point data to determine the accuracy of the two DEMs, but I did not have known elevation data. I considered using the 1 m resolution DEM as known data (the paper we read did something similar), but was not sure that would work in this case. Based on what I know about how the LiDAR and SRTM data are obtained, I think that the LiDAR data is likely more accurate, as there are more data points with less distance between them than for the SRTM data. Also, LiDAR does not have the problems over water or heavy canopied vegetation that SRTM data does. The big limitation for both is that due to how they collect data, neither can work effectively through clouds.