Accuracy assessment of lidar‐derived digital elevation models

Abstract Despite the relatively high cost of airborne lidar‐derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal distribution of the derived height differences or errors. This paper proposes a new methodology to assess the vertical accuracy of lidar DEMs using confidence intervals constructed from a finite sample of errors computed at check points. A non‐parametric approach has been tested where no particular error distribution is assumed, making the proposed methodology especially applicable to non‐normal error distributions of the type usually found in DEMs derived from lidar. The performance of the proposed model was experimentally validated using Monte Carlo simulation on 18 vertical error data‐sets. Fifteen of these data‐sets were computed from original lidar data provided by the International Society for Photogrammetry and Remote Sensing Working Group III/3, using their respective filtered reference data as ground truth. The three remaining data‐sets were provided by the Natural Environment Research Council’s Airborne Research and Survey Facility lidar system, together with check points acquired using high precision kinematic GPS. The results proved promising, the proposed models reproducing the statistical behaviour of vertical errors of lidar using a favourable number of check points, even in the cases of data‐sets with non‐normally distributed residuals. This research can therefore be considered as a potentially important step towards improving the quality control of lidar‐derived DEMs.