Withdrawn Paper

There are several data product characterization methods to describe LiDAR data quality. Typically based on guidelines developedby government or professional societies, these techniques require the statistical analysis of vertical differences at known checkpoints(surface patches) to obtain a measure of the vertical accuracy. More advanced methods attempt to also characterize the horizontalaccuracy of the LiDAR point cloud, using measurements at LiDAR-specific targets or other man-made objects that can be distinctlyextracted from both horizontal and vertical representation in the LiDAR point cloud. There are two concerns with these methods.First, the number of check points/features is relatively small with respect to the point cloud size that is typically measured, at least,in millions. Second, these locations are usually selected in relatively benign areas, such as hard flat surfaces at easily accessiblelocations. The problem with this characterization is that it is not likely that a statistically representative analysis can be obtainedfrom a limited number of points at locations that may not properly represent the overall object space composition. There is anongoing effort to address these issues, and some of the newer methods to characterize LiDAR data include an average points spacingmeasure, computed from the LiDAR point cloud. Clearly, it is an important step forward but it ignores the surface complexity. Theobjective of this study is to elaborate only on the requirements for adequate surface representation in combination with the LiDARerror characterization techniques to identify the relation between the two surfaces, the measured and reference (ideal), and thus, tosupport better LiDAR or, in general, point cloud error characterization