Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM

Terrain analysis of digital elevation models (DEM) has become an important technique to assess landscape and watershed scale hydrologic and pedologic processes and the spatial variability of soil and ecologic properties. Light detecting and ranging (LIDAR) elevation data sets provide the flexibility needed to produce multiple horizontal resolutions of DEM from the same data source. A series of 61 LIDAR tiles (100 ha) were collected from the North Carolina Flood Mapping Program covering the spatial extent of the Hofmann Forest in the Lower Coastal Plain of Eastern North Carolina. The LIDAR data set was reduced to 50%, 25%, 10%, 5%, and 1% of the original density. We created 5-, 10-, and 30-m DEM with 0.1 m vertical precision for each density level and used paired t-test to determine if the true mean of their differences were equal to zero. Differences indicated that for the 30-m DEM, LIDAR data sets could be reduced to 10% of their original data density without statistically altering the produced DEM. However, the 10-m DEM could only be reduced to 25% of the original data set before statistically altering the DEM. Data reduction was more limited for the 5-m DEM with possible reduction only to 50% of their original density without producing statistically different DEM. Our evaluation provides some indication as to the minimum required LIDAR data density to produce a DEM of a given horizontal resolution. However, evaluation of additional horizontal resolutions and additional density reduction is required to provide a clearer understanding of the effect of LIDAR data density.