Abstract
M.Sc.
Light Detection and Ranging (LiDAR) provide decidedly accurate datasets with high data
densities, in a very short time-span. However, the high volumes of data associated with
LiDAR often require some form of data reduction to increase the data handling efficiency of
these datasets, of which the latter could affect the feasibility of Digital Elevation Models
(DEMs). Critically, when DEM processing times are reduced, the resultant DEM should still
represent the terrain adequately. This study investigated three different data reduction
techniques, (1) random point reduction, (2) grid resolution reduction, and (3) combined data
reduction, in order to assess their effects on the accuracy, as well as the data handling
efficiency of derived DEMs. A series of point densities of 1 %, 10 %, 25 %, 50 % and 75 %
were interpolated along a range of horizontal grid resolutions (1-, 2-, 3-, 4-, 5-, 10- and 30-
m).
Results show that, irrespective of terrain complexity, data points can be randomly reduced up
to 25 % of the data points in the original dataset, with minimal effects on the remaining
dataset. However, when these datasets are interpolated, data points can only be reduced to 50
% of the original data points, before showing large deviations from the original DEM. A
reduction of the grid resolution of DEMs showed that the grid resolution could be lowered to
4 metres before showing significant deviations. When combining point density reduction
with grid resolution reduction, results indicate that DEMs can be derived from 75 % of the
data points, at a grid resolution of 3 metres, without sacrificing more than 15 percent of the
accuracy of the original DEM. Ultimately, data reduction should result in accurate DEMs that
reduce the processing time. When considering the effect on the accuracy, as well as the
processing times of the data reduction techniques, results indicate that resolution reduction is
the most effective data reduction technique. When reducing the grid resolution to 4 metres,
data handling efficiencies improved by 94 %, while only sacrificing 10 % of the data
accuracy. Furthermore, this study investigated data reduction on a variety of terrain
complexities and found that the reduction thresholds established by this study were
applicable to both complex and non-complex terrain.