LIDAR density and linear interpolator effects on elevation estimates

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Abstract
Linear interpolation of irregularly spaced LIDAR elevation data sets is needed to develop realistic spatial models. We evaluated inverse distance weighting (IDW) and ordinary kriging (OK) interpolation techniques and the effects of LIDAR data density on the statistical validity of the linear interpolators. A series of 10 forested 1000‐ha LIDAR tiles on the Lower Coastal Plain of eastern North Carolina was used. An exploratory analysis of the spatial correlation structure of the LIDAR data set was performed. Weighted non‐linear least squares (WNLS) analysis was used to parameterize best‐fit theoretical semivariograms on the empirical data. Tile data were sequentially reduced through random selection of a predetermined percentage of the original LIDAR data set, resulting in data sets with 50%, 25%, 10%, 5% and 1% of their original densities. Cross‐validation and independent validation procedures were used to evaluate root mean square error (RMSE) and kriging standard error (SE) differences between interpolators and across density sequences. Review of errors indicated that LIDAR data sets could withstand substantial data reductions yet maintain adequate accuracy (30 cm RMSE; 50 cm SE) for elevation predictions. The results also indicated that simple interpolation approaches such as IDW could be sufficient for interpolating irregularly spaced LIDAR data sets.