Improving accuracy of LiDAR-derived digital terrain models for saltmarsh management

Accurate digital elevation models of saltmarshes are crucial for both conservation and management goals. Light detection and ranging (LiDAR) is increasingly used for topographic surveys due to the ability to acquire high resolution data over spatially-extensive areas. This capability is ideally suited to saltmarsh environments, which are often vast, inaccessible systems where topographic variations can be very subtle. Derivation of surface (DSMs) (ground elevation plus vegetation) versus terrain (bare ground elevation) models (DTMs) relies on the ability of the LiDAR sensor to accurately record multiple returns. In saltmarshes however, the dense stands of low (< 1 m) vegetation commonly found precludes the acquisition of more than one return, and the resulting DTM is not different to the DSM. Establishing the offset between ground and vegetation surface in order to correct the LiDAR-derived DTM can be challenging due to the spatial variability in saltmarsh habitats. Here we show the development and application of a habitat-specific correction factor (HSCF) for the Odiel Saltmarshes using a combination of habitat object-based classification (82% overall accuracy) and ground control surveys that reduces the DTM error to within that associated with the LiDAR sensor (average error 0.1 m). We also show that the true accuracy of supplied (unmodified) DTMs can be >0.5 m in saltmarshes dominated by dense vegetation such as Spartina densiflora. In particular, global projections of sea-level rise across the next 80 years (0.18-0.59 m) significantly overlaps this accuracy margin, implying that assessments and modelling of sea-level impacts in saltmarsh systems will likely be erroneous if based on Lidar-derived DTMs. Erroneous assumptions and conclusions can result if the real accuracy of DTMs (bare ground) on vegetated saltmarshes is not considered, and the consequences of the propagation of this misinformation through to management decisions should not be over-looked.