Lab 9: Assessment of the accuracy of DEMs

One can determine the quality of a Digital Elevation Model (DEM) using statistics. In this lab I used Microsoft Excel to calculate the percentiles, Root Mean Squared Error (RMSE), and Mean Error (ME).
The percentiles tell you how many points fit within a given range. I used the 68th and 95th percentiles for this lab. This means, for example, that the difference between the DEM and the field report data for the 95th percentile of urban land cover is within 0.384 m 95% of the time.
RMSE tells you how similar one set of values is to another. The lower the number the more accurate the data is. RMSE does not tell you about the distribution of error. I found that bare earth and low grass land covers were the most accurate while fully forested areas were the least accurate.
ME tells you about possible bias in the data. A negative number indicates underestimation while a positive number indicates overestimation.  The urban area was the most biased with a ME of 0.164 while bare earth was the least biased with -0.005. I have attached below a table summarizing the values I arrived at during this lab.

Accuracy Metric	Accuracy (m)
	Bare earth and low grass	High grass, weeds, and crops	Brush land and low trees	Fully forested	Urban	Combined
Sample size	48	55	45	98	41	287
Accuracy 68th (m)	0.098	0.151	0.22	0.222	0.189	0.276
Accuracy 95th (m)	0.163	0.44	0.481	0.463	0.384	0.171
RMSE (m)	0.105	0.181	0.246	0.394	0.2	0.429
ME	-0.005	-0.069	-0.103	0.003	0.164	-0.006