3.1 At what point in account development to consider global data

The Inventory aims to enable the initial development and pilot testing of national ocean accounts where suitable national data are not available. As outlined in the GOAP Technical Guidance (Chapter 3) and the draft Implementation Strategy for the SEEA EA, key data sources and available data should be identified in the initial strategic planning and implementation preparation phase of the account development process (Figure 2).

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Figure 2: National implementation process presented in the draft implementation strategy for the SEEA EA. The red circle highlights the initial strategic planning and implementation preparation phase. The two yellow boxes indicate activities that the Inventory can support.

The first step in the process is strategic planning of the priorities for the account development. The strategic planning process should begin by convening key stakeholders a) from the organisations that contribute data and expertise to the compilation of ocean asset accounts and b) from the agencies that will use the accounts in planning and decision-making. In consultation with these stakeholders, the planning process should then determine 1) the policy question (or analytical objectives) that the ocean accounts are intended to address, 2) the type of accounts needed, and 3) what account development is feasible with the available resources (including data). One of the central activities is the identification of relevant data sources and the evaluation of availability and quality of the data.

The priority should be to investigate national data sources. Where national data of suitable quality are not available, the Global Ocean Asset Data Inventory can support the strategic planning by providing an overview of possible global data sources. For example, the Inventory can be used as part of diagnostic tools for strategic account planning (see Box 1).

By the end of the strategic planning, it should be clear what policy question (or analytical objective) the ocean accounts are intended to address, what accounts will be developed for this and what national data is available. The next step is building mechanisms for implementation of the accounts (see Figure 2). As part of this, the specific key datasets need to be identified that can be used to build the accounts. National data should be prioritised where available at suitable quality. Where national data are not available or not possible to obtain (e.g. through data sharing options, original fieldwork or socio-economic surveys), the Inventory can be consulted to identify and select suitable global datasets.

At this point, available global datasets in the Inventory should be critically assessed. The filter and search functions in the Inventory allow users to identify datasets that are relevant to the policy priorities and accounts to be developed. If relevant datasets are available, the stepwise approach described in Chapter 4 can help guide the critical assessment of quality and relevance of the data. This should enable an informed decision about what global data to use in the specific national context for ocean accounts determined in the strategic planning process.

3.2 Advantages and limitations of global ocean asset datasets

Whether global data is useful for developing national ocean accounts, and what the limitations are, will depend on the specific national context and ocean accounting priorities and on the global datasets that are relevant to these. Nonetheless, a few general observations can be made on the advantages and limitations of global ocean asset datasets. These observations can be helpful when considering the use of global data.

3.2.1 Advantages

• Consistency over large scales: Global datasets apply consistent methodologies and standards over large scales. This is particularly relevant for large countries with multiple and/or extended coastlines and large ocean areas within their EEZ. While data held in the country might be more accurate, or at higher resolution, it may not always be consistent and comparable across different parts of the national ocean environment. Data that comes from different regional or local monitoring programmes, or individual site-specific studies, might have been produced using different methodologies and standards. This makes it more difficult to aggregate up to national ocean asset accounts.

• Consistency over time: The consistent methodologies underpinning global datasets also serve to ensure temporal (as well as spatial) consistency. In particular, satellite and derived data now offer quality assured, regular time-series global data on the environment. These datasets can provide global information on ecosystem extent, condition (habitat type, area, density, water depth, pollution, etc.) and other ocean assets. This can often be made available at high temporal frequencies, as some data are produced daily or weekly. Some satellite data services have been provided since the 1980s^[5].

• International comparability: Consistent methodologies and standards applied in global datasets also facilitate international comparisons between countries. This is particularly relevant where national ocean accounts are intended to be used for reporting against international targets and commitments such as the Sustainable Development Goals, the post-2020 global biodiversity framework or nationally determined contributions to the Paris Agreement.

• Expert support: Where datasets are actively maintained by scientific or expert organisations, it may be possible to get support and guidance from the data providers. It may also be possible to work with these data providers to improve the useability of global data at the national level.

3.2.2 Limitations and sources of error

When using global data, it is important to know how the datasets were produced in order to understand their limitations and sources of error. There are three main approaches for creating global maps^[6]: 1) by combining different local or regional datasets (e.g. World Atlas of Seagrasses), 2) by using satellite imagery or other remotely sensed data (e.g. Global Mangrove Watch), or 3) by combining remote sensing and in-situ data in a hybrid approach (e.g. Global Distribution of Coral Reefs^[7]). In their paper on ‘Spatial Data Collection for Conservation and Management of Coastal Habitats’, Pruckner et al. (2021) identify a number of limitations and sources of error that these global ocean ecosystem maps have:

• Different combined maps (e.g. for seagrasses) may show contradictory results. It is therefore important to understand the methods used to produce the different maps and what exactly the maps are showing. For example:

  • Are the maps at different spatial and temporal scales?

  • Are habitats classified differently?

• Most global maps only show the presence of ecosystems. They do not provide information about ecosystem health. Moreover, it may not be clear whether blank areas on the map are due to absence of ecosystems or lack of data.

• Most existing global ocean ecosystem maps only provide a static snapshot for a given ecosystem type at one point, or period, of time. Currently, only changes in global extent of mangroves can be tracked, using Global Mangrove Watch. However, efforts are under way to provide regularly updated coral data in the Allen Coral Atlas.

Other limitations and sources of error for global datasets include:

• For maps produced by combining different datasets, it may be difficult or impossible to identify the date and sources of individual datasets used to generate extent. For example, this is the case for the Allen Coral Atlas.

• Remote sensing data requires in-situ validation (ground truthing) to ensure that they are interpreted correctly for different national circumstances. Especially for sub-tidal ecosystems like coral reefs and seagrass beds. This is because artefacts from satellite imagery present a common source of error.

• Predictive modelling can be used to close data gaps, including for satellite images. However, modelled data can only provide a best estimate. For example, potential ecosystem extent based on presence/absence criteria. When using this type of modelled data it is important to understand if they have been trained correctly and ground truthed thoroughly for national circumstances.

• Global data often have low spatial resolution. This may limit their usefulness for areas where granular data is required, e.g. to understand habitat fragmentation.

3.3 Additional global data sources

The Inventory focuses on a specific selected set of datasets relevant to ocean asset accounts. It is not meant to be an exhaustive list of all available datasets that might be relevant to ocean asset accounts. Other data sources may provide additional useful resources to support the initial compilation of national ocean accounts. A growing number of platforms, portals, repositories and initiatives are working to increase and facilitate access to global ocean data. Some of these global ocean data sources were identified during the search for global ocean asset datasets and are listed in a separate tab in the Inventory (see ‘Portals, Repositories’ tab).

Two of these additional global ocean data sources are the UN Biodiversity Lab and Ocean+. The UN Biodiversity Lab is a free, open-source spatial data platform brought together by the United Nations Development Programme, the United Nations Environment Programme (UNEP) and the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC). The platform provides access to over 400 global spatial datasets on nature, climate change and sustainable development. This includes 25 datasets with marine relevance, providing data on:

• Marine and coastal ecosystems,

• Fishing effort,

• Territorial seas, contiguous zones and EEZs,

• Coral reef connectivity and ecosystem service values,

• Global mangrove soil carbon,

• Cumulative ocean impact, marine pollution index,

• Global intertidal change, global surface water,

• Marine ecoregions, pelagic provinces and wilderness.

Ocean+ is UNEP-WCMC’s umbrella initiative for marine biodiversity data and information. One of the products under the initiative is the Ocean+ Library which guides the user to a range of selected, high quality marine datasets and online resources with applicability to marine decision making. This includes both global and regional resources, as well as detailed metadata on each dataset.

Efforts are also on going to support ocean accounting using global and other readily available data via the Group on Earth Observations for Ecosystem Accounting initiative and via the ARIES for SEEA project. These platforms are aiming to provide data and applications for ecosystem accounting in the near future. Within the Group on Earth Observations (GEO), the GEO Blue Planet initiative is also working to develop global ocean data for policy and decision-making.

The GOAP Technical Guidance (section 4.2) highlights two key sources for ocean satellite remote sensing, in-situ and modelling observational data:

• The Committee on Earth Observation Satellites (CEOS) brings together 55 space agencies from around the world to ensure international coordination of satellite Earth observation programmes, facilitate data sharing and disseminate resources to support the access and use of satellite data (‘Data & Tools’).

• The Global Ocean Observing System (GOOS) is an initiative of the Intergovernmental Oceanographic Commission of the United Nations Educational, Scientific and Cultural Organization, co-sponsored by the World Meteorological Organization, the United Nations Environment Programme and the International Science Council. GOOS supports the international ocean observing community in developing tools, technology, information systems, scientific analysis and foretastes for ocean observations. They provide access to ocean observation data through the Ocean OPS dashboard.

For information related to marine activities or produced assets, commercial, sector specific data service providers may be an additional source of data. These data services will generally involve a cost. However, paid services may provide the benefit of getting quality assured data tailored to specific user needs. These services might be worth considering in some cases where a country might have a very specific data need.