This week in Special Topics in GIS, we learned about the sources of error involved in creating DEMs, and conducted two analyses on elevation data. In the first analysis, we compared elevation and land cover class data collected by hand in the field. This elevation data is considered to be the more accurate, or reference data. The sample points were divided among five classes of land cover, shown in the table below. We overlaid a LIDAR image on the field sample point map, and compared the LIDAR-obtained elevations at points corresponding with the field-measured points. The table below shows the accuracy results for the five types of ground cover, separately and together.
Bare earth and low grass, predictably, have the lowest land-cover, because there are few obstructions in the LIDAR view. Fully-forested land has the highest error, which is logical because the canopy of trees will interfere with the LIDAR view. The range of error values was also greater for forest. This might be from the variability of the forest cover.
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Another interesting outcome of this analysis was that while the composite error appeared to be fairly unbiased, most of the component land cover data types did show various strong biases. Land cover types a, b, and c (bare earth and low grass, high grass, weeds and crops, and brushland and low trees) all produce LIDAR elevations that are more often higher than the field measured sample elevations.
Although fully-forested land cover has the highest error, it is also shows the least bias in elevation error. Urban land cover has a very strong negative bias in error: LIDAR elevations are nearly always lower than the field-sampled reference elevations. It just so happens that the combination of these 5 types of data produce a more-or-less unbiased composite. This would be very misleading if the results were not closely analyzed by ground-cover type.
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