Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data

Abstract Digital terrain model (DTM) has wide-ranging applications in numerous fields, including natural resource management, urban planning, environmental protection, and disaster monitoring. Utilizing LiDAR data to generate DTM is now a mainstream method. In current applications, LiDAR data are still treated as having primarily additive errors; however, studies have shown that it is affected by both additive and multiplicative errors. From the perspective of error theory and surveying adjustment, it is theoretically inappropriate to treat mixed additive and multiplicative errors directly as additive errors, as each error model is based on a distinct theoretical framework. In view of this, we applied the mixed additive and multiplicative error theory to the generation of LiDAR-derived DTM products and validated its accuracy through two real measurement cases and one simulation case. The experimental results demonstrate that the mixed additive and multiplicative errors theory provides higher accuracy than the additive error theory in both DTM fitting and interpolation. This confirms that incorporating the mixed additive and multiplicative error theory into DTM product generation is beneficial.