Automated Assessment of Digital Terrain Models Derived From Airborne Laser Scanning

This paper presents the derivation of surface-related quality indicators describing the confidence-metrics of the final geo-products derived from airborne laser scanning (ALS). We first discuss the number of factors influencing the quality of the digital terrain models (DTM) and review the rigorous derivation of quality metric per each laser target when considering all the elements of direct-georeferencing as well as the scanning geometry. As in the context of DTM creation, however, the laser measurements are rarely used as single values; we extend this approach by considering other factors as classification, sampling density and interpolation. Further, we propose a novel procedure that enables an automated generation of a DTM quality map encapsulating all these factors assuming that the following conditions are fulfilled: i) the accuracy of each ground point involved in DTM generation is known or derived; ii) the DTM is represented as a regular grid where the elevation values are calculated by projecting the grid-cell centre coordinates on the corresponding facet of the TIN-model whose nodes are the irregularly sampled laser-points reflected from the ground. The derived DTM-quality map is thus influenced by the choice of grid resolution with respect to the actual density of the laser point-cloud, as well as the accuracy of individual laser returns. Finally, we present an example that demonstrates surface-quality map computed for an ALS point-cloud where the distribution of automatically classified ground points is very disparate and contains important gaps due to dense vegetation or insufficient surface-reflectance. We conclude with suggestions on possible applications of such quality-maps that can be associated as metadata to the DTM.