The Role of Surface Complexity in Airborne Lidar Product Error Characterization

There is a wide variety of data product characterization methods to describe LiDAR data quality. The most basic methods typically use a measure derived from vertical differences at known checkpoints (surface patches) to obtain the vertical accuracy, and thus, simply ignore the error contributions of the horizontal components. More advanced methods attempt to also characterize the horizontal accuracy of the LiDAR point cloud, by using measurements at LiDAR identifiable targets or other man-made objects that can be distinctly extracted from both horizontal and vertical representation in the LiDAR point cloud. However, there is a relatively limited, or no attention at all, paid to the surface complexity of LiDAR-surveyed area itself. Though, both the surface geometry macroand microstructures and the material characteristics play a role in the error budget besides the sensor measurement errors. The objective of this study is to elaborate only on the requirements for adequate surface representation in combination with the LiDAR error characterization techniques to identify the relation between the two surfaces, the measured and reference (ideal), and thus, to support better LiDAR or in general point cloud error characterization.