Conference Agenda

In support of efforts to protect eelgrass beds, halt biodiversity loss, and promote recovery, the SwedCoast-BlueCarb project applies satellite Earth Observation (EO) to Swedish coastal waters. Funded by the Swedish and UK Space Agencies, the project combines EO and in situ data to assess the impacts of climate-change mitigation in contrasting test areas: the CDOM-dominated Baltic Sea around Kalmar, and the Swedish west coast that's strongly influenced by Baltic outflow.

Initial activities established collaborations with academic partners and monitoring programmes. The EO processing has focused on generating consistent datasets with atmospheric correction methods tested, and a modelling approach developed to retrieve both water optical properties and submerged vegetation from surface reflectance. Copernicus Sentinel-2 imagery (20 m) is used to map eelgrass (Zostera marina) and bladderwrack (Fucus vesiculosus) extent, together with uncertainty estimates, where vegetation occurs within detectable depths. In addition, laboratory analyses support the optical modelling by characterising the absorption and reflectance spectra of submerged vegetation.

Now that the initial modelling approach is working, the ongoing work utilises the benefits of a machine learning model to accelerate the modelling process, allowing for faster processing and, consequently, systematic monitoring across large spatial and temporal scales. Sentinel-3 data (300 m) provide complementary information on water optical status, a key factor in determining light availability and ecosystem health. In parallel, commercial WorldView-2 imagery (2 m) is being evaluated to demonstrate the potential of very high-resolution mapping.

Ultimately, an automated processing chain will deliver EO-based products openly through a GIS-style portal. These products will enable local authorities and conservation groups to track the condition of eelgrass, identify restoration priorities, and assess the effectiveness of management measures. By quantifying vegetation extent and water optical properties, the project supports blue-carbon conservation goals and strengthens the evidence base for climate-change mitigation in coastal ecosystems.

Seagrass ecosystems store a disproportionately large amount of the ocean’s total carbon, but the synergistic impact of climate and anthropogenic interactions has led to severe habitat loss and carbon storage capability of the ecosystem. High dynamicity of the seagrass ecosystem and associated blue carbon stock necessitates timely monitoring of blue carbon stock to determine variability in the source dynamics and budget of these vulnerable ecosystems. Conventional field measurements of seagrass biomass and sediment organic carbon are laborious and economically non-viable, necessitating the need to develop regional algorithms for monitoring seagrass biomass and organic carbon using satellite data. This study assessed the spatio-temporal variability of seagrass biomass and sediment organic carbon stock along the Palk Bay, South-east coast of India, through a combination of in-situ surveys and satellite-based modelling for the period January 2022 -December 2023. Two permanent monitoring sites—Chinnapalam and Mandapam were selected following pilot surveys, with additional sampling conducted from Thondi to Thangachimadam to validate satellite-derived estimates. Field data on seagrass biomass, sediment, and water quality were collected and analysed, while Sentinel-2 imagery (2015–2023) was processed to map annual and seasonal variability in seagrass cover. NDVI- based seagrass above ground biomass was also obtained from Sentinel data, and validated with field observations. Results revealed significant seasonal variability in total seagrass biomass, with higher values during the wet season (778.29 ± 227.06 g dwt m⁻²) compared to the dry season, whereas the sediment organic carbon showed higher concentrations in the dry season (1.03 ± 0.23%) compared to the wet season (0.72 ± 0.06%). Among species, Cymodocea serrulata showed the highest biomass, while Halophila ovalis and Halodule pinifolia had comparatively lower values. Above-ground (AGB) and below-ground biomass (BGB) also exhibited significant species-level and seasonal variability, contributing to seasonal differences in total organic carbon stock. Empirical models linking NDVI with in-situ AGB (R² = 0.80) enabled satellite-based estimation of biomass and carbon stock, based on AGB-carbon stock relationship for the Palk Bay. Validation with independent field data showed strong agreement with seagrass AGB (R² = 0.67) and carbon stock (R² = 0.72). This integrated field–satellite approach provides a robust framework for mapping blue carbon resources at regional scales, reducing economic and human efforts. The outputs were then incorporated in a WebGIS application, that offers valuable decision-support tools for identifying priority areas for seagrass management and restoration to enhance climate mitigation potential.

The ongoing rise in ocean acidification (OA) presents a significant threat to coral reef
ecosystems, particularly affecting the vulnerability of coral species to environmental stressors.
This research focuses on quantifying the impact of OA on coral reefs surrounding Saint Martin
Island, Bangladesh—an area recognized for its coral biodiversity. Through systematic ecological
surveys, water quality analyses, and predictive modeling using machine learning (ML), we aim
to illustrate the changing conditions of coral growth rates and resilience linked to OA over time.
Coral growth is negatively impacted by OA due to reduced skeletal density, making these
ecosystems more susceptible to bioerosion and storm damage, thereby impairing their structural
integrity and resilience.
Historical data shows that live coral cover surrounding Saint Martin Island has decreased
dramatically, from 20–25% in the 1980s to below 10% today. This decline has been exacerbated
by both global climate change and localized stressors, such as coastal pollution and overfishing.
Our analysis will specifically explore how these stressors interact with OA to further decrease
coral resilience, supporting findings that highlight the severe implications of combined threats
faced by coral ecosystems. Implementation of modern ML techniques allows us to predict future
risks and develop proactive management strategies for coral conservation and restoration,
focusing on promoting resilient coral species and effectively managing local impacts.
Furthermore, the findings of this research will be pivotal in guiding stakeholders towards
adaptive solutions, such as the establishment of locally managed marine protected areas and the
restoration of protective habitats like mangroves and seagrass beds, which can help mitigate the
effects of acidification. This comprehensive understanding is crucial for sustaining not only the
coral reefs themselves but also the myriad of ecosystem services they provide, vital for local
communities reliant on fisheries and tourism. This research aims to fortify the resilience of Saint
Martin Island’s coral reefs against the backdrop of ongoing climate change, ensuring the
longevity of their ecological and economic contributions.

The coastal ocean is a region of socio-economic and ecological importance. Yet, this system is under immense pressure from global climate change and other anthropogenic hazards, which threaten ecosystem services and increase the vulnerability of the growing coastal population and infrastructure. Whilst the coastal ocean is under pressure, it can also be part of the solution to manage and adapt to changes, with the phytoplankton ecosystem as an example of this. Primary production by phytoplankton plays an important role in the global carbon cycle through the conversion of inorganic carbon in the water to organic carbon via photosynthesis. This process is not only important for global climate regulation, but also essential for supporting all coastal ecosystems and the services they provide. In this study, we explored changes in phytoplankton primary production in the global coastal ocean from 1998-2022 within Longhurst's ecological provinces. We address three key questions: (1) In which coastal provinces does primary production undergo significant changes? (2) What are the underlying causes of these changes? And (3) Is the aggregation of data into large areas (such as the ecological provinces) suitable for investigating the underlying causes of any observed change in the global coastal ocean?
To address these questions, we have undertaken trend analysis of primary production model outputs based on the Ocean-Color Climate Change Initiative (OC-CCI) data, and applied a linear regression analysis using a seasonal decomposition of the time series and autoregressive integrated moving average (ARIMA) method. We further explored the impact of sea surface temperature (from SST CCI), phytoplankton chlorophyll-a concentration from OC-CCI (while recognising that chlorophyll-a concentration was an input to the primary production calculations, and that, therefore the two are not independent) and upwelling (Bakun Index) on primary production. We also studied the impacts of missing data (with a systematic bias) and of data aggregation methods at individual pixel level on apparent or artificial trends. Results showed that there were five (out of 23) coastal provinces with statistically significant increasing or decreasing linear trends in primary productivity, and that all regions with significant trends are also upwelling regions. Through this work we contribute to developing an understanding of the changes experienced in the global coastal ocean, which is essential knowledge for management of coastal challenges and pressures globally. This is a contribution to the UKRI NERC funded FOCUS Project (NE/X006271/1), the ESA SCOPE Project and the Simons Foundation CBIOMES Project.