Lab 9 - Accuracy of DEMs

This week we analyzed the accuracy of DEMs (Digital Elevation Models).  This particular lab has built upon concepts that were covered in Labs 1 - 3, and heralded the return of RMSEs (root mean square error), percentile calculations, and Excel spreadsheets.

Determining the accuracy of elevation data is remarkably similar to determining the accuracy between x, y coordinates.  Essentially, one needs a series of sample points from the original data set (preferably at least 20 per land cover class type) and a set of reference data.  The reference data should be of a higher quality than the source data.  In the case of elevation data this usually would be the elevation data collected from a sub-meter GPS during the initial data capture (such as with LIDAR).  The differences between the source data and the reference data sets are then calculated.  Statistics such as RMSE, 68th percentile, and 95th percentile are then calculated.

To illustrate this, the first portion of our lab compared reference points and source points for LIDAR data taken within North Carolina.  The data was sub-divided into 5 general land cover types; each land cover type had test points well in excess of the recommended 20 sample points.  After calculating the average RMSE, 68th percentile, and 95th percentile statistics for each land cover type tested, the data was then viewed on a scatter plot graph to see at a glance where obvious outliers in the data are, as well as areas of potential bias.

What I had found was that the differences between the reference and source elevations were relatively similar (from between -0.2 to 0.4 m).  There was one obvious outlier, which represented a possible error during data collection.  There was also a slight bias in the DEM, with the DEM underestimating elevation values.  I've included a graph showing this data; the potential bias is visible from -0.3 to -0.7 m on the graph.

Graph of differences between the source data and reference data; difference values are in meters.