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.

Saltmarshes provide high-value ecological services and play an important role in coastal ecosystems and populations. As the rate of sea level rise accelerates in response to climate change, saltmarshes and tidal environments and the ecosystem services that they provide could be lost in those areas that lack sediment supply for vertical accretion or space for landward migration. Predictive models could play an important role in foreseeing those impacts, and to guide the implementation of suitable management plans that increase the adaptive capacity of these valuable ecosystems. The SLAMM (sea-level affecting marshes model) has been extensively used to evaluate coastal wetland habitat response to sea-level rise. However, uncertainties in predicted response will also reflect the accuracy and quality of primary inputs such as elevation and habitat coverage. Here, we assessed the potential of SLAMM for investigating the response of Atlantic-Mediterranean saltmarshes to future sea-level rise and its application in managerial schemes. Our findings show that SLAMM is sensitive to elevation and habitat maps resolution and that historical sea-level trend and saltmarsh accretion rates are the predominant input parameters that influence uncertainty in predictions of change in saltmarsh habitats. The understanding of the past evolution of the system, as well as the contemporary situation, is crucial to providing accurate uncertainty distributions and thus to set a robust baseline for future predictions.

Erosion is one of the major issues currently facing coastal areas. Some consequences of this process are beach loss and higher flood risk, which will likely be exacerbated given ongoing sea-level rise. With this in mind, those responsible for conservation and management decisions need appropriate tools with which to identify critical coastal areas, as well as to analyse, interpret, and visualise them with the appropriate geomorphological and environmental background. The aim of this work was to present a methodology for improving the analysis and interpretation of coastal erosion rates, as well as to guarantee wide access and dissemination of erosion data. To that end, an approach for the production, management, and dissemination of shoreline erosion data for the Andalusian coast in Southern Spain was developed. This approach enables the analysis and interpretation of the erosion rates in coasts by linking erosion rates with geomorphological and thematic information using a data model. Additionally, this methodology was proven to be a valid and appropriate tool for the design of a web-based viewer, being the best way to represent the erosion rates obtained every 50 m of shore for the entire Andalusian coast, being an exposed coastal front 917 km long. This is particularly useful for integrated coastal zone management schemes, enabling quick and easy access to valuable information.