No abstract

Climate change driven Sea Level Rise (SLR) is creating a major global environmental crisis in coastal ecosystems, however, limited practical solutions are provided to prevent or mitigate the impacts. Here, we propose a novel eco-engineering solution to protect highly valued vegetated intertidal ecosystems. The new ‘Tidal Replicate Method’ involves the creation of a synthetic tidal regime that mimics the desired hydroperiod for intertidal wetlands. This synthetic tidal regime can then be applied via automated tidal control systems, “SmartGates”, at suitable locations. As a proof of concept study, this method was applied at an intertidal wetland with the aim of restabilising saltmarsh vegetation at a location representative of SLR. Results from aerial drone surveys and on-ground vegetation sampling indicated that the Tidal Replicate Method effectively established saltmarsh onsite over a 3-year period of post-restoration, showing the method is able to protect endangered intertidal ecosystems from submersion. If applied globally, this method can protect high value coastal wetlands with similar environmental settings, including over 1,184,000 ha of Ramsar coastal wetlands. This equates to a saving of US$230 billion in ecosystem services per year. This solution can play an important role in the global effort to conserve coastal wetlands under accelerating SLR.

No abstract

In an era of climate and biodiversity crises, ecosystem rehabilitation is critical to the ongoing wellbeing of humans and the environment. Coastal ecosystem rehabilitation is particularly important, as these ecosystems sequester large quantities of carbon (known in marine ecosystems as “blue carbon”) thereby mitigating climate change effects while also providing ecosystem services and biodiversity benefits. The recent formal accreditation of blue carbon services is producing a proliferation of rehabilitation projects, which must be monitored and quantified over time and space to assess on-ground outcomes. Consequently, remote sensing techniques such as drone surveys, and machine learning techniques such as image classification, are increasingly being employed to monitor wetlands. However, few projects, if any, have tracked blue carbon restoration across temporal and spatial scales at an accuracy that could be used to adequately map species establishment with low-cost methods. This study presents an open-source, user-friendly workflow, using object-based image classification and a random forest classifier in Google Earth Engine, to accurately classify 4 years of multispectral and photogrammetrically derived digital elevation model drone data at a saltmarsh rehabilitation site on the east coast of Australia (Hunter River estuary, NSW). High classification accuracies were achieved, with >90% accuracy at 0.1 m resolution. At the study site, saltmarsh colonised most suitable areas, increasing by 142% and resulting in 56 tonnes of carbon sequestered, within a 4-year period, providing insight into blue carbon regeneration trajectories. Saltmarsh growth patterns were species-specific, influenced by species’ reproductive and dispersal strategies. Our findings suggested that biotic factors and interactions were important in influencing species’ distributions and succession trajectories. This work can help improve the efficiency and effectiveness of restoration planning and monitoring at coastal wetlands and similar ecosystems worldwide, with the potential to apply this approach to other types of remote sensing imagery and to calculate other rehabilitation co-benefits. Importantly, the method can be used to calculate blue carbon habitat creation following tidal restoration of coastal wetlands.