Framework for assessing wetland blue carbon provision through remote sensing and modelling

This study presents a comprehensive framework for assessing current and future ecosystem services in coastal wetlands, by integrating satellite image classification with simplified predictive modelling. The research was conducted in Ria Formosa, one of Portugal's most important coastal lagoons. Hard classification and soft regression Random Forest algorithms were employed to estimate the fractional coverage of marsh zones in 2025, with the former applied to very high-resolution satellite data (Worldview-3, with metric pixel size) and the latter to high and medium-resolution satellite imagery (PlanetScope and Sentinel-2, with metric to decametric pixel sizes). The results provide valuable insights into the challenges associated with variable satellite sources for automated mapping of ecological succession. Future projections up to 2100, informed by land cover change simulations from the SLAMM model, investigated potential ecological trajectories under sea-level rise (SLR) scenarios. Ecosystem adjustments to SLR were further translated into estimates of future blue ecosystem services (i.e., organic carbon stocks), based on reference values reported in the literature. Under the IPCC SSP5-8.5 pathway, significant transitions were projected, including relevant portions of present-day high marshes converting to low marsh, low marshes transforming into tidal flats, and tidal flats devolving into subtidal bare sediment. The modelling framework suggests that coastal squeeze will lead to a meaningful decline in salt marsh extent. The projected ecogeomorphologic adjustments to SLR allowed for pinpointing vegetated areas of gains and losses in carbon stocks by 2100. Foreseen changes will have key implications for the ecological balance of these wetlands, as the significant loss of high marsh habitat may compromise the ecological succession functioning and potentially lead to a decline in biodiversity within these zones. The integrative approach employed, which combines remote sensing and simplified modelling, offers crucial insights into the dynamics and resilience of wetland ecosystemsunder SLR conditions, supporting informed management and conservation efforts in the face of environmental changes.

Acknowledgements: This study contributes to the projects C-Land (CEXC/4647/2024), SYREN (ALGARVE-FEDER-00853600-SYREN-17135), and DEVISE (2022.06615.PTDC), funded by the Fundação para a Ciência e a Tecnologia, as well as to the CLARKS project (CLARKS - 2024-1-ES01-KA220-SCH-000246633) under ERASMUS+ 2024, and to RestLands (ID 705677) funded by Planet Labs.