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Box 1. Using the Inventory as part of diagnostic tools for strategic account planning Ecosystem accounting experiences and ocean accounts pilots have shown that account development should be a collaborative process involving account producers, data providers and account users. To facilitate the collaboration, diagnostics tools such as the SEEA Diagnostic Tool[125] or the Diagnostic Tool for Strategic Planning from the UN ESCAP ocean accounts pilot studies, can be used in the strategic account planning step. One of the areas covered by the diagnostic tools is ‘Knowledge’, i.e. the identification of national data sources and availability. Where the diagnostic tool reveals gaps in national data that would prevent moving forward with the account development, the Inventory can be reviewed as part of the consultation exercise. This would provide an initial overview of available global data. |
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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.
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Country experience: In creating an initial pilot for Canada’s ocean accounts, key condition indicators were sea surface temperature and salinity. Although Canadian data exist, the datasets that were located were not considered suitable for the accounts. Data were either point data with limited spatial coverage or raw spatial data that would have required a significant amount of compilation work. Through using the World Ocean Atlas[14] data, which has a common granularity across all of Canada’s EEZ, a first estimate of sea surface temperature and salinity change could be made. This allowed conditions to be compared across the Atlantic, Arctic and Pacific Ocean regions as data had the same time period and spatial granularity. |
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[155].
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.
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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[166]: 1) by combining different local or regional datasets (e.g. World Atlas of Seagrasses[17]), 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[187]). 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[19].
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.
[14] World Ocean Atlas: https://www.ncei.noaa.gov/products/world-ocean-atlas
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[17] World Atlas of Seagrasses: https://archive.org/details/worldatlasofseag03gree
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[19] Allen Coral Atlas: https://allencoralatlas.org/
3.3 Additional global data sources
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Two of these additional global ocean data sources are the UN Biodiversity Lab and Ocean+. The UN Biodiversity Lab[20] 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+[21]isUNEP-WCMC’s umbrella initiative for marine biodiversity data and information. One of the products under the initiative is the Ocean+ Library[22] 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[23] initiative and via the ARIES for SEEA project[24]. 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.[25]
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)[26] 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 ‘Data & Tools’[27]).
The Global Ocean Observing System (GOOS)[28] 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[29] 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.
[20] UN Biodiversity Lab: https://unbiodiversitylab.org/
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[22] Ocean+ Library: https://library.oceanplus.org/
[23] Group on Earth Observations for Ecosystem Accounting initiative: https://www.eo4ea.org/
[24] ARIES stands for Artificial Intelligence for Environment and Sustainability. ARIES for SEEA project: https://seea.un.org/content/aries-for-seea
[25] GEO Blue Planet: https://geoblueplanet.org/
[26] Committee on Earth Observation Satellites (CEOS): https://ceos.org/
[27] CEOS Data & Tools: https://ceos.org/data-tools/
[28] Global Ocean Observing System (GOOS): https://www.goosocean.org/index.php?option=com_content&view=article&id=272&Itemid=411
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