In the era of blue growth, ocean multi-use is gaining popularity for its potential environmental, economic and societal synergies. Expectations are high for multi-use applications to alleviate marine spatial allocation conflicts amongst users of the sea, and to stimulate innovative ways of sustainably exploiting marine resources. However, a potential barrier to implementing multi-use and co-location is the lack of a well-defined framework to evaluate the impacts of ocean multi-use projects. This paper introduces the Ocean Multi-Use Assessment Framework (OMAF), which builds upon traditional environmental impact assessments but expands to include societal and economic dimensions. In addition to these three pillars of sustainable development, the framework incorporates two critical conditions: technological feasibility and regulatory appropriateness (legal, policy, and governance). The framework promotes the use of scenarios to compare single-use and multi-use approaches in an integrated manner. This approach allows for a comprehensive, holistic evaluation of multi-use projects compared to single-use alternatives, supporting decision-making. Strong stakeholder engagement throughout the process is emphasized. The OMAF has been developed and tested under the EU-funded Horizon 2020 UNITED project, where it was applied to five multi-use pilot projects. Despite challenges related to data availability for emerging marine activities, the framework has proven applicable and effective for most projects.

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

Marine or maritime spatial planning (MSP) works across borders and sectors to ensure human activities at sea take place in an efficient and sustainable way. The ecosystem service (ES) concept links ecosystem functioning to human wellbeing and has emerged as a potential framework supporting MSP, as it can be used to link different sectorial and environmental policies. However, due to the complexity of the marine realm, mapping and assessment of ES is still in its infancy and there remains a need to develop and agree upon the appropriate progress in ES development to support MSP. This contribution highlights research needs and recommendations to advance the operationalization of the ES concept into MSP. We apply a mixed method approach combining literature research and expert knowledge derived from 14 case studies, to address current status and prospects of ES application in MSP. We present nine main needs dealing with (i) improvement and adaptation of existing ES frameworks and classifications to the marine realm and (ii) definition of an indicator pool; (iii) methodological and technical developments to support data availability and accessibility; (iv) advances in mapping and modelling methods; (v) improvements in assessment and valuation approaches; (vi) further use of scenario and trade-off analysis; (vii) taking advantage of supporting Information Technologies (IT); (viii) improvements in communication and engagement with stakeholders; and (ix) further work for the integration of ES knowledge into policies and for supporting management and MSP. The manuscript concludes with a set of recommendations to foster the operationalization of the ES concept into MSP.

To account for progress towards conservation targets, monitoring systems should capture not only information on biodiversity but also knowledge on the dynamics of ecological processes and the related effects on human well-being. Protected areas represent complex social-ecological systems with strong human-nature interactions. They are able to provide relevant information about how global and local scale drivers (e.g., climate change, land use change) impact biodiversity and ecosystem services. Here we develop a framework that uses an ecosystem-focused approach to support managers in identifying essential variables in an integrated and scalable approach. We advocate that this approach can complement current essential variable developments, by allowing conservation managers to draw on system-level knowledge and theory of biodiversity and ecosystems to identify locally important variables that meet the local or sub-global needs for conservation data. This requires the development of system narratives and causal diagrams that pinpoints the social-ecological variables that represent the state and drivers of the different components, and their relationships. We describe a scalable framework that builds on system based narratives to describe all system components, the models used to represent them and the data needed. Considering the global distribution of protected areas, with an investment in standards, transparency, and on active data mobilisation strategies for essential variables, these have the potential to be the backbone of global biodiversity monitoring, benefiting countries, biodiversity observation networks and the global biodiversity community.

The concept of ecosystem services is gaining attention in the context of sustainable resource management. However, it is inherently difficult to account for tangible and intangible services in a combined model. The aim of this study is to extend the definition of ecosystem service trade-offs by using Bayesian Networks to capture the relationship between tangible and intangible ecosystem services. Tested is the potential of creating such a network based on existing literature and enhancement via expert elicitation. This study discusses the significance of expert elicitation to enhance the value of a Bayesian Network in data-restricted case studies, underlines the importance of inclusion of experts’ certainty, and demonstrates how multiple sources of knowledge can be combined into one model accounting for both tangible and intangible ecosystem services. Bayesian Networks appear to be a promising tool in this context, nevertheless, this approach is still in need of further refinement in structure and applicable guidelines for expert involvement and elicitation for a more unified methodology.

Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.