Lab 9 - Accuracy of DEMs

The objective of this assignment was to calculate and analyze the vertical accuracy of a DEM, comparing it to known reference points. We also analyzed the effects of interpolation methods on DEM accuracy.

First, we are given a high resolution DEM obtained through LIDAR and reference elevation points collected on the ground. Field data was collected for 5 land cover types: a) bare earth and low grass, b) high grass, weeds, and crops, c) brush lands and low trees, d) fully forested, and e) urban areas. Cluster sampling was used, which are selected by a random or systematic method with a cluster of samples around each center. The main way to spot cluster sampling is that the distances between samples are smaller than the distance between clusters.

One of the best ways to determine the quality of a DEM is to take the DEM data and compare it to some form of reference data. Here we used elevation points obtained from the field. Using that data, we can determine the root mean square error (RMSE), the 68^th percentile accuracy and the 95^th percentile accuracy. Low values there generally means that the DEM data matches up well with the reference data and the values are accurate. You can also calculate the mean error to help determine the vertical bias. The reason RMSE does not work for this is that the elevation difference between the DEM and reference values are squared within the calculation, so the result is always positive; you cannot determine bias from RMSE. From my results, this is a very accurate DEM, with an overall RMSE of 0.276 meters and a 95% confidence level of 0.43 meters. It seems to be the most accurate over bare earth and low grass and less accurate over shrubland and low trees (perhaps it’s easier to distinguish between taller trees and the ground than shrubs and the ground). The vertical bias is also very small, with a combined vertical bias of 0.006 meters. The greatest vertical bias is a 0.16 m negative bias over urban areas and a positive 0.10 m bias over brush land and low trees. The LIDAR data seems to be an excellent model of elevation here, and it does exceptionally well over flatter (less “rough”) areas, such as bare earth or low grass.

Next we are provided a data layer contained 95% of the total points, which are used to create DEMs using 3 interpolation methods: IDW, Spline, and Kriging. The remaining 5% of the points are used as reference points. After creating the 3 DEMs, I exported the reference data to an Excel file and created new columns for the DEM data. After inputting the data to the Excel file, I performed the RMSE, 68th and 95th percentile, and mean error calculations for the three DEMs. Based on these calculations and assuming that I want the interpolation elevation values to be the closest to the reference points, the best interpolation technique for this particular data set is the IDW interpolation.

Statistic	IDW	Spline	Kriging
RMSE (m)	11.78366	17.78546	12.11856
95th	21.72756	20.04868	22.60116
68th	12.18899	9.888262	12.41265
ME (m)	1.73996	0.854330	2.121384

Overall, I learned a lot about the process of determining the vertical accuracy of DEMs using reference points. I initially had some difficulty figuring out how to determine the 68th and 95th percentile accuracies, but going back to a previous lab helped with that.