| Summary: | Our planet is traversing the age of human-induced climate change and biodiversity loss. Projected global warming of 1.5 ºC above pre-industrial levels and related greenhouse gas emission pathways will bring about detrimental and irreversible impacts on the interconnected natural and human ecosystem. A global warming of 2 ºC could further exacerbate the risks across the sectors of biodiversity, energy, food, and water. Time- and cost-effective solutions and strategies are required for strengthening humanity’s response to the present environmental and societal challenges. Coastal seascape ecosystems including seagrasses, corals, mangrove forests, tidal flats, and salt marshes have been more recently heralded as nature-based solutions for mitigating and adapting to the climate-related impacts. This is due to their ability to absorb and store large quantities of carbon from the atmosphere. Focusing on seagrass habitats, although occupying only 0.2% of the world’s oceans, they can sequestrate up to 10% of the total oceanic carbon pool, all the while providing important food security, biodiversity, and coastal protection. But seagrass ecosystems, as all of their blue carbon seascape neighbors, are losing 1.5% of their extent per year due to anthropogenic activities. This has adverse implications for global carbon stocks, coastal protection, and marine biodiversity. Seagrass and seascape recession necessitates their science and policy-based management, protection, conservation which will ensure that our planet will remain within its sustainable boundaries in the age of climate change. The present PhD Thesis and research aim is to develop algorithms for seagrass mapping and monitoring leveraging the recent emergences in remote sensing technology―new satellite image archives, machine learning frameworks, and cloud computing―with field data from multiple sources. The main PhD findings are the demonstration of the suitability of Sentinel-2, RapidEye, and PlanetScope satellite imagery for regional to large-scale seagrass mapping; the introduction and incorporation of machine learning frameworks in the context of seagrass remote sensing and data analytics; the development of a semi-analytical model to invert the bottom reflectance of seagrasses; the design and implementation of multi-temporal satellite image approaches in coastal aquatic remote sensing; and the introduction, design and application of a scalable cloud-based tool to scale up seagrass mapping across large spatial and temporal dimensions. The approaches of the present PhD cover the gaps of the existing scientific literature of seagrass mapping in terms of the lack of spatial and temporal scalability and adaptability; the infancy in seagrass and seascape-related artificial intelligence endeavours; the restrictions of local server and mono-temporal approaches; and the absence of new methodological developments and applications using new (mainly open) satellite image archives. I anticipate and envisage that the near-future steps after the completion of my PhD will address the scalability of the designed cloud-native, data-driven mapping tool to standardise, automate, commercialise and democratise mapping and monitoring of seagrass and seascape ecosystems globally. The synergy of the developed momentum around the global seascape with the technological potential of Earth Observation can contribute to humanity’s race to adapt to and mitigate the climate change impacts and avoid cross tipping points in climate patterns, and biodiversity and ecosystem functions.
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