TG-4.1 Remote Sensing and Geospatial Data

Prerequisites: TG-0.1 GOAP Fundamentals, TG-0.7 Quality Assurance Framework Enables: TG-3.1 Asset Accounts, TG-3.5 Ecosystem Condition Accounts, TG-6.1 Coral Reef Ecosystem Accounting, TG-6.2 Mangrove and Coastal Wetland Accounting, TG-6.3 Seagrass Ecosystem Accounting, TG-1.2 Marine Spatial Planning

1 Outcome

This Circular provides operational guidance on using remote sensing and geospatial data for compiling ocean accounts, establishing the spatial foundation that underpins ecosystem extent mapping, condition monitoring, and marine spatial planning analysis. Upon implementation, statistical agencies and ocean accounting practitioners will be able to:

a) Select and access appropriate satellite imagery platforms for mapping marine ecosystem extent and monitoring change, with specific attention to optical sensors (Sentinel-2, Landsat) for coastal ecosystem mapping and SAR platforms (Sentinel-1) for all-weather coverage in persistently cloudy regions;

b) Apply remote sensing classification methods and change detection workflows to produce spatially explicit ecosystem extent accounts that feed directly into TG-3.1 Asset Accounts and support ecosystem-specific guidance in TG-6.1 Coral Reef Ecosystem Accounting, TG-6.2 Mangrove and Coastal Wetland Accounting, and TG-6.3 Seagrass Ecosystem Accounting;

c) Integrate bathymetric data and seabed classification products within ocean accounting spatial frameworks, providing the depth-stratified spatial units required for marine ecosystem extent accounts and enabling three-dimensional representation of marine ecosystems as described in SEEA EA Chapter 3;^[1]

d) Delineate Basic Spatial Units (BSUs) appropriate for coastal and marine contexts, recognizing that BSU resolution should reflect spatial heterogeneity and data density--finer resolution (10-30m) in data-rich coastal zones, coarser resolution in offshore areas with sparse observations--while ensuring exhaustive and mutually exclusive coverage across the Ecosystem Accounting Area;^[2]

e) Implement quality assurance procedures for geospatial data products consistent with international geospatial standards (ISO 19157) and the framework established in TG-0.7 Quality Assurance Framework, including accuracy assessment through confusion matrices, positional accuracy documentation, and validation against authoritative reference datasets; and

f) Align geospatial data workflows with the Global Statistical Geospatial Framework (GSGF) Principle 1 (fundamental geospatial infrastructure) and Principle 4 (statistical-geospatial interoperability), supporting coordination between National Statistical Offices and National Geospatial Information Agencies.^[3]

2 Requirements

This Circular requires familiarity with:

• TG-0.1 GOAP Fundamentals -- provides foundational understanding of the Global Ocean Accounts Partnership framework, including institutional arrangements for multi-agency collaboration and the relationship between environmental and economic accounting.

• TG-0.7 Quality Assurance Framework -- establishes the overarching quality framework for ocean accounts, including data quality dimensions, validation procedures, and documentation requirements that apply to geospatial data products described in this Circular.

2.1 Institutional Requirements

Countries implementing this Circular shall:

a) Establish formal data sharing arrangements between the National Statistical Office (NSO), National Geospatial Information Agency (NGIA), and relevant hydrographic authorities;^[4]

b) Designate a technical lead with competency in geospatial data management for ocean accounting purposes;

c) Document access arrangements for satellite imagery and bathymetric data sources, including licensing terms and data sharing agreements;

d) Maintain metadata catalogues for all geospatial datasets used in ocean accounting, following ISO 19115 geographic metadata standards;^[5] and

e) Participate in relevant international coordination mechanisms, including UN-GGIM Working Group on Marine Geospatial Information where applicable.^[6]

2.2 Technical Requirements

Countries shall ensure technical capacity for:

a) Processing and analysing satellite imagery at resolutions appropriate for marine ecosystem delineation (typically 10--30m for extent mapping);

b) Handling gridded bathymetric data in formats consistent with IHO S-100 series standards;^[7]

c) Managing coordinate reference systems appropriate for marine domains, including vertical datums for depth measurements;

d) Integrating vector and raster geospatial data within geographic information systems (GIS); and

e) Producing spatially explicit outputs aligned with the SEEA EA framework for ecosystem extent and condition accounts.^[8]

2.3 Data Quality Requirements

All geospatial data used in ocean accounts shall:

a) Include documented accuracy assessments using confusion matrices or equivalent validation approaches;

b) Specify positional accuracy in horizontal and vertical dimensions;

c) Document temporal coverage and update frequency;

d) Include provenance information traceable to source satellite platforms or survey instruments; and

e) Be validated against authoritative national or regional reference datasets where available.

3 Guidance Material

3.1 Decision Use Cases for Remote Sensing Data in Ocean Accounting

Remote sensing and geospatial data support multiple decision-making applications within ocean accounts. Understanding these use cases helps prioritize data acquisition investments and guides compilation decisions.

3.1.1 Ecosystem Extent Monitoring

Ecosystem extent accounts require spatially explicit delineation of marine ecosystem types, with extent measured in hectares or square kilometers and tracked over accounting periods.^[9] Remote sensing provides the only practical method for mapping marine ecosystems at national scales. Decision applications include:

• Monitoring change in mangrove extent -- supporting SDG indicator 6.6.1 (change in extent of water-related ecosystems) through time-series analysis of mangrove forest area using Landsat and Sentinel-2 imagery at 30m and 10m resolution respectively. The Global Mangrove Watch dataset provides annual mangrove extent maps from 1996 onwards at 25-metre resolution, enabling national compilers to track long-term extent trends.^[10]

• Mapping coral reef ecosystem assets -- delineating reef extent and geomorphic zones (reef flat, reef crest, fore-reef slope) using multispectral satellite imagery and acoustic bathymetry. The Allen Coral Atlas (version 2.0, released 2022) provides standardized reef extent and geomorphic zone mapping at approximately 5-metre resolution for shallow tropical reef areas globally, derived from Planet satellite imagery.^[11]

• Tracking seagrass meadow distribution -- monitoring seagrass extent using high-resolution optical imagery in clear, shallow waters. Seagrass mapping requires water clarity sufficient for optical penetration (typically Secchi depth greater than 2m) and spatial resolution fine enough to detect patchy meadow structure (sub-10m resolution preferred).

Extent accounts compiled using remote sensing data feed directly into TG-3.1 Asset Accounts (Section 3.4 on ecosystem asset accounts), providing the opening stock, additions, reductions, and closing stock entries required by SEEA EA Table 4.1.^[12]

3.1.2 Change Detection and Degradation Assessment

Change detection identifies additions and reductions in ecosystem extent between accounting periods, enabling compilation of the extent change entries required in ecosystem asset accounts. Remote sensing provides consistent, repeatable observations suitable for multi-temporal analysis. Key applications include:

• Detecting conversions -- identifying conversions from mangroves to aquaculture ponds, or from coral reef to rubble following bleaching mortality. Change detection methods include post-classification comparison (classifying images from two dates independently and comparing results) and direct change detection using spectral indices (e.g., differencing NDVI time series to identify vegetation loss).

• Monitoring coastal erosion -- tracking shoreline position changes using optical and SAR imagery. Shoreline change rates inform condition assessments for coastal ecosystems and support valuation of coastal protection services described in TG-6.1 Coral Reef Ecosystem Accounting and TG-6.2 Mangrove and Coastal Wetland Accounting.

• Assessing bleaching impacts -- mapping bleached coral reef areas using multispectral and hyperspectral imagery. NOAA Coral Reef Watch provides satellite-derived bleaching alerts and degree heating week (DHW) products that can be integrated with in-situ mortality surveys to estimate reef area lost through bleaching events.^[13]

These change detection outputs populate the managed and unmanaged additions and reductions recorded in extent accounts, distinguishing changes resulting from deliberate human decisions (managed) from those associated with natural processes including climate-driven impacts (unmanaged), consistent with SEEA EA paragraphs 4.14-4.17.^[14]

3.1.3 Basic Spatial Unit (BSU) Delineation

The SEEA EA introduces the Basic Spatial Unit (BSU) as an operational tool for implementing ecosystem asset delineation within GIS environments. A BSU is "a geometrical construct representing a small spatial area" whose purpose is to provide a fine-level data framework within which ecosystem characteristics can be attributed.^[15] For ocean accounting, remote sensing pixels or national grid cells serve as BSUs, with resolution choices guided by data density and ecosystem heterogeneity:

• Coastal zones -- fine-resolution BSUs (10-30m pixels from Sentinel-2 or Landsat) are appropriate because data availability is high and ecosystems such as mangroves, seagrass meadows, and coral reefs exhibit fine-scale patchiness.

• Offshore zones -- coarser BSU geometries (1km grid cells or depth-contour-defined spatial units) are typically sufficient because environmental gradients are broader and in-situ data is sparser.

BSUs must be exhaustive and mutually exclusive across the Ecosystem Accounting Area, ensuring complete spatial coverage without overlaps or gaps (SEEA EA para. 3.37).^[16] Where ocean accounts adjoin terrestrial or freshwater accounts, the BSU grid should be consistent across domains to enable integrated analysis.

The spatial data outputs from this Circular provide the BSU framework within which TG-3.5 Ecosystem Condition Accounts records condition variables and TG-1.2 Marine Spatial Planning performs spatial allocation analysis.

3.2 Upward Connections: Which Accounts Consume Remote Sensing Data

Remote sensing and geospatial data produced under this Circular feed into multiple downstream account types. Understanding these upward connections helps compilers prioritize data quality investments and ensure that spatial data products meet the requirements of consuming accounts.

3.2.1 Ecosystem Extent Accounts (TG-3.1, TG-3.5)

Ecosystem extent accounts require spatially explicit delineation of ecosystem types, with total area measured at opening and closing accounting periods and additions/reductions tracked between periods. Remote sensing provides:

• Classified imagery -- raster outputs assigning each BSU to an ecosystem type (e.g., mangrove, coral reef, seagrass, sandy substrate). Classification products should include accuracy assessments (overall accuracy, user's accuracy, producer's accuracy) derived from confusion matrix analysis with ground-truth validation data.^[17]

• Change detection products -- identifying pixels that changed ecosystem type between opening and closing dates, distinguishing managed changes (e.g., coastal development) from unmanaged changes (e.g., storm damage, natural succession).

TG-3.1 Asset Accounts Section 3.4 requires opening extent, additions, reductions, and closing extent for each ecosystem type. Remote sensing outputs populate these entries directly.

3.2.2 Ecosystem Condition Accounts (TG-3.5)

Ecosystem condition accounts record the quality of ecosystems using variables organized within the SEEA Ecosystem Condition Typology (ECT).^[18] Remote sensing contributes condition variables across multiple ECT classes:

• Physical state characteristics (Class A1) -- sea surface temperature from MODIS or VIIRS sensors; turbidity from optical imagery using Secchi depth proxies or water color indices.

• Structural state characteristics (Class B2) -- vegetation cover and canopy height for mangroves and coastal wetlands derived from optical imagery (cover) and LiDAR or InSAR (height); coral cover from high-resolution multispectral imagery; above-ground biomass estimated using SAR backscatter and allometric equations.

• Landscape and seascape characteristics (Class C1) -- fragmentation metrics (patch size distribution, edge density) derived from classified imagery; hydrological connectivity assessed by analyzing tidal channel networks; spatial pattern indices quantifying ecosystem asset configuration.

The SEEA EA notes that "condition accounts are most commonly compiled using remote sensing, modelling and other techniques in combination with available direct field measures."^[19] For comprehensive guidance on condition variable selection and indicator derivation, see TG-3.5 Ecosystem Condition Accounts.

3.2.3 Ecosystem-Specific Accounts (TG-6.1, TG-6.2, TG-6.3)

The ecosystem-specific circulars in Section 6 apply extent and condition accounting methods to particular marine ecosystem types:

• TG-6.1 Coral Reef Ecosystem Accounting -- requires reef extent mapping at 5-30m resolution, geomorphic zone classification (reef flat, crest, slope), and condition variables including live coral cover, bleaching prevalence, and rugosity index.

• TG-6.2 Mangrove and Coastal Wetland Accounting -- requires mangrove forest extent mapping, canopy cover estimation, canopy height measurement for above-ground biomass estimation, and change detection for tracking conversions to aquaculture or agricultural uses.

• TG-6.3 Seagrass Ecosystem Accounting -- requires seagrass meadow extent mapping in optically clear waters, shoot density estimation from high-resolution imagery or benthic surveys, and monitoring of meadow fragmentation patterns.

These circulars reference this Circular (TG-4.1) for general remote sensing methods while providing ecosystem-specific technical details tailored to each habitat type.

3.2.4 Marine Spatial Planning Accounts (TG-1.2)

Marine spatial planning requires spatially explicit information on ecosystem distribution, human use patterns, and jurisdictional boundaries to support allocation decisions. Remote sensing data contribute:

• Ecosystem extent layers -- serving as the base map for spatial planning exercises, showing the distribution of benthic habitats (coral reefs, seagrass meadows, sandy substrate, rocky reef) and surface water ecosystem types (coastal waters, open ocean).

• Change detection outputs -- identifying areas of high ecosystem change rates that may warrant protective zoning or management intervention.

• Bathymetric-derived products -- depth contours, slope, and geomorphic features that inform zoning decisions (e.g., depth-based fishery management zones).

TG-1.2 Marine Spatial Planning uses ecosystem extent maps to conduct spatial allocation analysis, including cross-tabulations of ecosystem type by designated use zone (fishery zone, marine protected area, tourism zone, port area).

3.3 Satellite Imagery Sources

Remote sensing provides essential data for ocean accounting, particularly for mapping ecosystem extent, monitoring change, and deriving condition indicators. Countries should consider multiple satellite platforms depending on their specific requirements for spatial resolution, temporal frequency, spectral characteristics, and cost.^[20]

3.3.1 Optical Satellite Platforms

Optical imagery is fundamental for mapping marine and coastal ecosystems. The following platforms are recommended for ocean accounting applications:

Sentinel-2 (Copernicus Programme)

The European Space Agency's Sentinel-2 mission provides multispectral imagery at 10--60m resolution with a 5-day revisit time.^[21] The 13 spectral bands include visible, near-infrared, and short-wave infrared wavelengths suitable for:

• Coastal ecosystem mapping (mangroves, seagrass, coral reef extent)
• Water quality parameters (chlorophyll-a, turbidity, suspended sediments)
• Coastal land cover change detection
• Tidal flat and intertidal zone delineation

Sentinel-2 data is freely accessible through the Copernicus Data Space Ecosystem and Google Earth Engine, making it particularly suitable for Tier 1 and Tier 2 ecosystem accounting approaches.^[22]

Landsat 8/9 (USGS/NASA)

The Landsat programme provides 30m multispectral imagery with approximately 16-day revisit time. The continuous archive extending back to 1972 (Landsat 1) enables long-term change analysis essential for establishing baseline conditions in ecosystem accounts.^[23] Key applications include:

• Historical ecosystem extent reconstruction
• Long-term coastal change analysis
• Calibration and validation of other satellite products
• Time-series analysis for condition accounts

MODIS (Terra/Aqua satellites)

The Moderate Resolution Imaging Spectroradiometer provides daily coverage at 250m--1km resolution, suitable for:

• Ocean colour and primary productivity monitoring
• Large-scale ecosystem extent mapping
• Seasonal and inter-annual variability assessment
• Sea surface temperature monitoring

The SEEA-MODIS land cover product provides land cover area values derived from MODIS data that can be crosswalked to SEEA classification schemes.^[24]

Commercial High-Resolution Platforms

For detailed coastal mapping, commercial platforms may be required:

• WorldView-3/4: 0.31m panchromatic, 1.24m multispectral resolution
• Planet Labs: 3--5m resolution with daily global coverage
• Maxar constellation: Sub-metre resolution for detailed habitat mapping

Countries should evaluate cost-benefit considerations when selecting commercial data sources.^[25]

3.3.2 Synthetic Aperture Radar (SAR)

SAR imagery provides all-weather, day-night imaging capability essential for regions with persistent cloud cover. SAR is particularly valuable for:

Sentinel-1 (Copernicus Programme)

The C-band SAR mission provides:

• Intertidal zone mapping through coherent change detection
• Oil spill detection and marine pollution monitoring
• Ship detection for fishing vessel monitoring
• Coastal flooding and storm surge mapping

RADARSAT Constellation Mission

The Canadian Space Agency's SAR constellation offers enhanced maritime surveillance capabilities including:

• Ice monitoring in polar ocean accounting regions
• Coastal erosion tracking
• Marine vessel monitoring

SAR data requires specialized processing workflows but provides critical complementary information to optical sensors.^[26]

3.3.3 Satellite-Derived Bathymetry

Satellite-derived bathymetry (SDB) techniques use optical multispectral imagery to estimate water depths in optically clear waters, typically to 20--25m depth. Methods include:

• Ratio-based algorithms (e.g., Stumpf method using blue/green band ratios)
• Physics-based radiative transfer models
• Machine learning approaches combining spectral and contextual features

SDB is cost-effective for mapping shallow coastal zones but requires calibration with in-situ depth measurements and is limited by water clarity conditions.^[27]

3.3.4 Geospatial data platforms and global ocean datasets

Cloud-based geospatial analysis platforms have transformed the accessibility and scalability of remote sensing for ocean accounting. Google Earth Engine (GEE) provides a planetary-scale analysis environment with access to multi-petabyte archives of satellite imagery, including the full Sentinel-2, Landsat, and MODIS collections, together with derived products such as the Global Surface Water dataset and JRC Global Forest Change maps.^[28] The Copernicus Data Space Ecosystem (CDSE) at https://dataspace.copernicus.eu/ -- which replaced the earlier Data and Information Access Services in 2023 -- offers direct access to all Copernicus Sentinel data alongside processing tools optimised for European and global applications.^[29] Digital Earth Australia provides a national-scale example of an analysis-ready data cube built on open-source infrastructure, demonstrating how countries can organise time-series satellite imagery for consistent national ecosystem monitoring.^[30]

For ocean accounting specifically, several global reference datasets provide foundational spatial layers. The USGS/ESRI Global Shoreline Vector provides a high-resolution coastline baseline for defining the land-sea interface. The General Bathymetric Chart of the Oceans (GEBCO) -- discussed further in Section 3.5.1 -- supplies global bathymetric grids essential for depth-stratified analyses. The United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) maintains authoritative global datasets on coral reefs, seagrass beds, and marine protected areas that serve as key reference layers for ecosystem extent accounts.^[31] The Allen Coral Atlas provides high-resolution (approximately 3.7m) benthic and geomorphic mapping of the world's shallow coral reef ecosystems, while Global Mangrove Watch offers time-series monitoring of mangrove extent from 1996 to present. Several of these products are also referenced in Section 3.4.2 (Tier 1 global data products); the platform-level context provided here supports countries in understanding where and how to access them for integration into geospatial workflows.

3.4 Compilation Procedure: From Remote Sensing Data to Ecosystem Extent Accounts

This section describes the operational workflow for producing ecosystem extent accounts from satellite imagery, providing step-by-step guidance that compilers can follow to move from raw imagery to populated account tables.

3.4.1 Image Acquisition and Preprocessing

Step 1: Define the Ecosystem Accounting Area (EAA)

The EAA boundary determines the spatial scope of accounts. For ocean accounts, the EAA typically extends from the coastline to the seaward boundary of the Exclusive Economic Zone (EEZ). Countries should define EAA boundaries using authoritative maritime limits from national hydrographic offices or international sources such as the Flanders Marine Institute (VLIZ) Maritime Boundaries Geodatabase.^[32]

Step 2: Identify Imagery Requirements

Based on the ecosystem types to be mapped, select appropriate imagery:

• Mangroves and coastal wetlands: Sentinel-2 (10m) or Landsat (30m) optical imagery
• Coral reefs: High-resolution optical imagery (Planet 3-5m or commercial sub-metre)
• Seagrass meadows: High-resolution optical imagery in clear waters
• Offshore ecosystems: MODIS (250m-1km) for large-scale patterns

Select imagery dates that minimize cloud cover and capture ecosystems during appropriate tidal stages (low tide for intertidal ecosystems).

Step 3: Acquire and Preprocess Imagery

Download imagery from appropriate sources:

• Sentinel-2: Copernicus Data Space Ecosystem (https://dataspace.copernicus.eu/)
• Landsat: USGS Earth Explorer (https://earthexplorer.usgs.gov/)
• Planet: Commercial access through Planet Explorer
• Google Earth Engine: Cloud-based access to multiple archives

Apply standard preprocessing:

• Atmospheric correction (converting top-of-atmosphere to surface reflectance)
• Cloud and cloud shadow masking
• Water surface glint correction for marine areas
• Geometric correction and georeferencing

Many satellite products are now provided as analysis-ready data (ARD) with preprocessing already applied, reducing the burden on compilers.

3.4.2 Classification and Ecosystem Type Mapping

Step 4: Develop Classification Scheme

Align ecosystem type classification with the IUCN Global Ecosystem Typology (GET) or a national classification crosswalked to GET. For coastal and marine ecosystems, relevant GET functional groups include:

• M1.3 Photic coral reefs
• MFT1.2 Intertidal forests and shrublands (mangroves)
• MFT1.3 Coastal saltmarshes and reedbeds
• M1.1 Seagrass meadows
• M1.5 Photo-limited marine animal forests (temperate reef)

Document the correspondence between national ecosystem types and GET functional groups to enable international comparability.

Step 5: Collect Training and Validation Data

Classification requires ground-truth data. Sources include:

• Field surveys with GPS/GNSS positioning
• Expert interpretation of high-resolution imagery
• Existing habitat maps from management agencies
• Citizen science observations (with appropriate quality control)

Split ground-truth data into training (70%) and validation (30%) subsets. Ensure that training data represent all ecosystem types and capture the spectral variability within each type.

Step 6: Perform Classification

Apply supervised classification methods:

• Random forest classifier (recommended for marine habitat mapping)^[33]
• Support vector machine
• Maximum likelihood classification

Random forest classifiers are particularly effective for marine habitat mapping because they can handle complex spectral signatures, nonlinear relationships, and mixed pixels common in coastal environments. Train the classifier using training data, then apply to the full image extent.

Step 7: Post-Classification Refinement

Apply spatial filters to reduce classification noise:

• Majority filter (replaces isolated pixels with surrounding class)
• Minimum mapping unit filter (removes patches below minimum size)
• Manual editing in areas with known errors

Integrate ancillary data where available (e.g., bathymetry for reef mapping, LiDAR for mangrove canopy height).

3.4.3 Accuracy Assessment

Step 8: Generate Confusion Matrix

Using the reserved validation data, construct a confusion matrix comparing classified results to ground-truth observations. Calculate accuracy metrics:^[34]

• Overall accuracy -- proportion of correctly classified samples across all classes
• User's accuracy -- probability that a mapped class represents that class on the ground (commission error)
• Producer's accuracy -- probability that a ground reference class is correctly classified (omission error)
• Kappa coefficient -- classification accuracy accounting for chance agreement

Countries should target minimum overall accuracy of 80% for ecosystem extent mapping, consistent with IPCC good practice guidance for land cover mapping.^[35]

Step 9: Document Accuracy Assessment

Prepare accuracy assessment reports documenting:

• Confusion matrix (full table showing classified vs. reference)
• Accuracy metrics by ecosystem type
• Description of validation data sources and sampling design
• Known limitations and sources of classification error

This documentation supports quality assurance requirements in TG-0.7 Quality Assurance Framework.

3.4.4 Change Detection

Step 10: Repeat Classification for Closing Date

Apply the same classification workflow to imagery from the closing accounting date. Use consistent methods, training data sources (updated for the closing date), and post-processing approaches to ensure comparability.

Step 11: Conduct Change Detection

Compare opening and closing date classifications to identify pixels that changed ecosystem type. Generate change matrices showing transitions between all ecosystem type pairs:

Step 12: Attribute Changes to Managed vs. Unmanaged Categories

Using ancillary information (e.g., coastal development permits, aquaculture licenses, storm event records), classify detected changes as:

• Managed additions -- restoration projects, afforestation
• Unmanaged additions -- natural colonization and succession
• Managed reductions -- coastal development, aquaculture conversion, land reclamation
• Unmanaged reductions -- storm damage, erosion, bleaching mortality, natural succession

This attribution supports the SEEA EA extent account structure requiring distinction between managed and unmanaged changes (SEEA EA paras. 4.14-4.17).^[36]

3.4.5 Populating Extent Accounts

Step 13: Calculate Area Statistics

Using the classified imagery and change detection outputs, calculate area in hectares for:

• Opening extent by ecosystem type
• Additions (managed and unmanaged) by ecosystem type
• Reductions (managed and unmanaged) by ecosystem type
• Closing extent by ecosystem type

Ensure that closing extent = opening extent + additions - reductions for each type.

Step 14: Populate Extent Account Table

Transfer area statistics into the SEEA EA ecosystem extent account structure (Table 4.1):^[37]

Step 15: Document Methods and Metadata

Prepare metadata documentation following ISO 19115 geographic metadata standards, including:

• Data sources (satellite platforms, dates, scenes)
• Classification methods (algorithm, training data, accuracy)
• Change detection methods
• Coordinate reference system and projection
• Known limitations and data quality considerations

This completes the compilation procedure. The populated extent accounts feed into TG-3.1 Asset Accounts and provide the spatial foundation for condition and services accounts in downstream circulars.

3.5 Bathymetry and Seabed Mapping

Bathymetric and seabed data provide the essential three-dimensional framework for ocean accounting, particularly for accounts addressing the water column and seabed ecosystems.^[38]

3.5.1 Bathymetric Data Sources

General Bathymetric Chart of the Oceans (GEBCO)

GEBCO provides global bathymetric grids at 15 arc-second resolution (approximately 450m at the equator), integrating multibeam sonar data, satellite altimetry-derived bathymetry, and other sources.^[39] The Seabed 2030 project aims to map the entire ocean floor by 2030, which will significantly enhance data availability for ocean accounting.

National Hydrographic Office Products

National hydrographic offices produce authoritative bathymetric data for territorial waters:

• Electronic Navigational Charts (ENCs) in S-57/S-100 formats
• Bathymetric attributed grids (BAG files)
• Digital terrain models of the seabed

Countries should establish data sharing arrangements with national hydrographic authorities to access best available bathymetric data.^[40]

Multibeam Echosounder Data

High-resolution multibeam data (typically 1--50m resolution) provides:

• Detailed seabed morphology
• Substrate classification through backscatter analysis
• Identification of seabed features (reefs, sand waves, bedforms)

Processing multibeam data requires specialized software and expertise but yields the highest quality bathymetric products.

3.5.2 IHO S-100 Data Standards

The International Hydrographic Organization S-100 Universal Hydrographic Data Model provides the framework for hydrographic and marine geospatial data products.^[41] Key product specifications relevant to ocean accounting include:

• S-102: Bathymetric Surface -- gridded bathymetric data products
• S-104: Water Level Information -- tidal and non-tidal water levels
• S-111: Surface Currents -- ocean surface current data
• S-121: Maritime Limits and Boundaries -- jurisdictional boundaries

S-100 products are designed for interoperability with GIS systems and align with ISO 19100 geographic information standards. Countries should ensure ocean accounting systems can ingest S-100 compliant data products.^[42]

3.5.3 Seabed Classification

Seabed substrate classification supports ecosystem extent mapping for benthic habitats:

Acoustic Classification Methods

• Multibeam backscatter analysis for substrate hardness
• Sub-bottom profiler data for sediment thickness
• Acoustic ground discrimination systems

Integrated Approaches

Combining acoustic data with:

• Grab sample analysis
• Underwater video surveys
• Environmental DNA sampling

Countries should document seabed classification methods and accuracy assessments in metadata accompanying ocean accounts.^[43]

3.6 Spatial Boundaries for Ocean Accounts

The spatial boundary of an ocean account is defined in the first instance by jurisdictional limits. Consistent with the SEEA Central Framework, the scope of national ecosystem accounting should extend to the boundary of the exclusive economic zone (EEZ), which under the United Nations Convention on the Law of the Sea (UNCLOS) reaches 200 nautical miles from the baseline (SEEA EA, para. 3.27).^[44] Within that jurisdiction, UNCLOS distinguishes several zones -- the territorial sea (12 nautical miles), the contiguous zone (24 nautical miles), and the EEZ -- each carrying different sovereign rights and obligations that may be relevant for organising ocean accounts by institutional sector. Countries may also have continental shelf rights beyond 200 nautical miles where geological conditions permit, and at the global level, ocean accounts could extend into areas beyond national jurisdiction (ABNJ); the 2023 Agreement under UNCLOS on the Conservation and Sustainable Use of Marine Biological Diversity of Areas beyond National Jurisdiction (BBNJ Agreement) provides an emerging governance framework for these areas.^[45]

The ecological boundary between "ocean" and "land" is less straightforward. The Millennium Ecosystem Assessment (2005) proposed a working definition of coastal ecosystems as extending 100km inland and to a seaward depth of 50m.^[46] This definition is one useful ecological framing -- and has been applied in several early ocean accounting pilots -- but it is not the SEEA EA standard. The SEEA EA itself leaves the precise inland coastal boundary as a national implementation decision, to be determined according to analytical requirements and the ecosystem types present. For transitional ecosystems that span the marine-terrestrial-freshwater interface, the IUCN Global Ecosystem Typology provides purpose-built categories: MT1 (Shorelines), MFT1 (Brackish tidal systems including mangroves and saltmarshes), and FM1 (Semi-confined transitional waters such as estuaries and coastal lagoons). Using these classifications can help resolve boundary ambiguities.

Where ocean accounts adjoin terrestrial or freshwater accounts within the same national accounting system, coordination with agencies responsible for those accounts is essential. Estuarine systems, for example, grade continuously from freshwater to saltwater, and clear institutional agreements are needed to avoid both double counting and omission of these transitional areas. Countries are encouraged to establish explicit rules for assigning transitional ecosystem types to either ocean or terrestrial/freshwater accounts, documenting these decisions in the metadata accompanying the accounts.^[47]

3.7 Data Gap Prioritization

Table 3.7.1 provides a prioritized overview of common data gaps in remote sensing and geospatial data for ocean accounting. Countries may use this matrix to guide investment decisions and capacity development planning.

Table 3.7.1: Data gap prioritization matrix for ocean accounting geospatial data

Extent mapping from satellite imagery is the highest-priority gap to address because it combines high policy relevance with high feasibility and moderate cost. Countries beginning ocean accounting programmes are advised to invest first in establishing satellite-based ecosystem extent baselines, which in turn provide the spatial framework for all subsequent accounts compiled under TG-3.1 Asset Accounts and the ecosystem-specific circulars in Section 6.

3.8 Quality Assurance for Geospatial Data

Robust quality assurance is essential for ensuring the fitness-for-purpose of geospatial data in ocean accounts. Quality assurance procedures should be aligned with the framework established in TG-0.7 Quality Assurance Framework and consistent with international geospatial quality standards.^[48]

3.8.1 Accuracy Assessment Framework

All classified geospatial products used in ocean accounts should include documented accuracy assessments:

Confusion Matrix Analysis

The confusion matrix provides the foundation for accuracy assessment of classified imagery:^[49]

Countries should target minimum overall accuracy thresholds appropriate to account requirements, typically 80% or higher for ecosystem extent mapping.

Ground Reference Data

Accuracy assessment requires independent ground reference data:

• Field survey data collected using GPS/GNSS positioning
• Expert interpretation of high-resolution imagery
• Existing authoritative habitat maps
• Citizen science and crowdsourced observations (with appropriate quality control)

Ground reference data should be statistically sampled to represent all ecosystem classes and geographic regions.^[50]

3.8.2 Positional Accuracy

Horizontal and vertical positional accuracy should be documented for all geospatial data:

Horizontal Accuracy

• Satellite imagery: typically 10--50m for medium resolution sensors
• National orthophotography: typically sub-metre
• GPS field surveys: sub-decimetre with appropriate techniques

Vertical Accuracy

• Bathymetric data: varies from decimetre (multibeam) to metres (satellite-derived)
• Digital elevation models: typically 1--10m vertical RMSE
• Tidal corrections: essential for integrating datasets from different tidal states

Countries should ensure vertical datums are clearly documented and consistently applied across ocean accounts.^[51]

3.8.3 Temporal Considerations

Geospatial data quality includes temporal aspects:

• Currency: How recent is the data?
• Update Frequency: How often is the data updated?
• Temporal Resolution: What time period does each observation represent?
• Seasonal Considerations: Are seasonal variations accounted for?

For ocean accounting, temporal alignment is critical when combining datasets from different sources and time periods.

3.8.4 Data Integration and Interoperability

Quality assurance for integrated geospatial data should address:

Coordinate Reference Systems

• Consistent horizontal CRS across all datasets (WGS84 recommended for global consistency)
• Appropriate vertical datum for depth measurements (chart datum, LAT, MSL)
• Documented coordinate transformations where datasets originate in different CRS

Data Format Standards

Countries should adopt open geospatial standards for data exchange:

• OGC Web Services (WMS, WFS, WCS) for web-based data sharing
• GeoPackage or GeoJSON for vector data exchange
• Cloud Optimized GeoTIFF (COG) for raster data
• NetCDF-CF for multidimensional oceanographic data

Metadata Standards

Comprehensive metadata following ISO 19115/19139 should document:

• Data lineage and provenance
• Processing methods and software used
• Quality measures and assessment results
• Use constraints and limitations^[52]

3.8.5 GSGF Alignment

Geospatial data workflows for ocean accounting should align with the five principles of the Global Statistical Geospatial Framework:^[53]

1. Fundamental Geospatial Infrastructure: Leverage national spatial data infrastructure and coordinate with national geospatial agencies
2. Geocoded Unit Record Data: Ensure statistical data can be linked to specific geographic locations
3. Common Geographies: Use consistent geographic units for aggregation and dissemination
4. Statistical and Geospatial Interoperability: Adopt internationally recognized standards
5. Accessible and Usable Geospatially Enabled Statistics: Ensure outputs are discoverable and usable

Countries should assess their readiness against the GSGF self-assessment tool and develop action plans to address capability gaps.^[54]

4 Worked Example: RS-Derived Extent Change Detection for a Coastal Area

This worked example demonstrates the compilation of ecosystem extent accounts using satellite remote sensing data for a hypothetical coastal region in Southeast Asia. The example follows the compilation procedure described in Section 3.4 and illustrates the key steps from image acquisition to populated extent accounts.

4.1 Setting and Scope

Ecosystem Accounting Area (EAA): A 2,500 km² coastal area including mangroves, coral reefs, seagrass meadows, and sandy/muddy coastal substrates. The EAA extends from the coastline (high-water mark) to 12 nautical miles offshore (territorial sea limit).

Accounting Period: Calendar years 2019 (opening) to 2024 (closing), consistent with the 5-year accounting period recommended for initial ecosystem accounts.

Ecosystem Types: Following IUCN GET classification:

• MFT1.2 Intertidal forests and shrublands (mangroves)
• M1.3 Photic coral reefs
• M1.1 Seagrass meadows
• M1.2 Subtidal sandy substrates

4.2 Image Acquisition and Preprocessing (Steps 1-3)

Opening Date Classification (2019)

Selected Sentinel-2 imagery acquired January 2019 (dry season, minimal cloud cover):

• Tile: 48MYU
• Date: 2019-01-15
• Cloud cover: 3%
• Products: Level-2A (surface reflectance, atmospherically corrected)

Closing Date Classification (2024)

Selected Sentinel-2 imagery acquired January 2024 (same season):

• Tile: 48MYU
• Date: 2024-01-12
• Cloud cover: 5%
• Products: Level-2A (surface reflectance, atmospherically corrected)

Preprocessing applied: Cloud masking using Sentinel-2 Scene Classification Layer, water surface glint correction using sun-glint angle, geometric co-registration between dates to ensure pixel-level alignment (RMSE < 0.5 pixel).

4.3 Classification and Ecosystem Type Mapping (Steps 4-7)

Training Data Collection

Collected 850 ground-truth points distributed across ecosystem types:

• Mangroves: 250 points (field GPS surveys)
• Coral reefs: 200 points (expert interpretation of Planet 3m imagery)
• Seagrass: 180 points (combination of field surveys and high-resolution imagery)
• Sandy substrate: 220 points (acoustic ground-truth from bathymetric surveys)

Split: 595 training points (70%), 255 validation points (30%).

Classification Method

Applied random forest classifier with following parameters:

• Number of trees: 500
• Maximum depth: 15
• Minimum samples per leaf: 5
• Features: All 10 Sentinel-2 bands at 10m and 20m resolution

Applied majority filter (3x3 window) to reduce salt-and-pepper noise. Applied minimum mapping unit of 0.25 ha (approximately 28 pixels at 10m resolution).

Classification Results

Opening date (2019) classification:

Closing date (2024) classification:

4.4 Accuracy Assessment (Steps 8-9)

Confusion Matrix (2024 classification)

Overall Accuracy: 86.3% (220 correct / 255 total) Kappa Coefficient: 0.81

Interpretation: Overall accuracy of 86.3% exceeds the 80% target threshold. Seagrass has lower producer's accuracy (80.9%) due to spectral confusion with sandy substrate in optically deep water where seagrass cover is sparse. This is a known limitation documented in metadata.

4.5 Change Detection (Steps 10-12)

Change Matrix

Attribution of Changes

Using ancillary data (aquaculture licensing records, coastal development permits, typhoon track data), changes were attributed:

Mangroves:

• Managed reduction (800 ha): Conversion to aquaculture ponds (verified through aquaculture license database)
• Unmanaged reduction (750 ha): Erosion and storm damage from Typhoon Ketsana (September 2022)
• Managed expansion (600 ha): Government-funded mangrove restoration programme (verified through forestry department records)
• Unmanaged expansion (450 ha): Natural colonization in abandoned aquaculture areas

Coral Reefs:

• Unmanaged reduction (850 ha): Bleaching mortality event (March 2023, documented by NOAA Coral Reef Watch DHW > 8) resulting in conversion to rubble
• Additions: 0 ha (reef recovery occurs over multi-decadal timescales exceeding the 5-year accounting period)

Seagrass Meadows:

• Unmanaged reduction (350 ha): Burial by sediment following coastal construction (erosion from developed areas)
• Managed expansion: 0 ha (no active restoration)
• Unmanaged expansion (100 ha): Natural colonization

4.6 Populating Extent Accounts (Steps 13-15)

Ecosystem Extent Account (2019-2024)

Note: Minor discrepancies between this summary table and the detailed change matrix (Section 4.5) result from rounding and edge effects in spatial processing. The detailed change matrix provides the authoritative pixel-level accounting.

4.7 Interpretation and Policy Relevance

Key Findings:

1. Net loss of 1,600 hectares (2.4% decline) of priority coastal ecosystems over the 5-year period.

2. Mangrove decline (-500 ha, -1.2%) driven by aquaculture conversion (managed reduction) and storm damage (unmanaged reduction), partially offset by restoration efforts. The managed restoration programme (600 ha) demonstrates progress toward SDG Target 6.6 on protecting and restoring water-related ecosystems.

3. Coral reef decline (-850 ha, -5.4%) driven entirely by bleaching-induced mortality. The absence of managed reductions indicates that direct human impacts (e.g., destructive fishing, coastal development) are currently minimal, but climate-driven bleaching represents a growing threat requiring management response.

4. Seagrass decline (-250 ha, -3.0%) driven by sedimentation from coastal development. This suggests the need for improved sediment management in catchments affecting seagrass meadows.

Linkages to Other Accounts:

• These extent changes feed into TG-3.1 Asset Accounts as opening stock, additions, reductions, and closing stock entries.

• Extent losses indicate potential ecosystem degradation requiring condition assessment under TG-3.5 Ecosystem Condition Accounts.

• Reductions in coral reef extent affect coastal protection capacity valued in TG-6.1 Coral Reef Ecosystem Accounting.

• Mangrove extent changes affect blue carbon storage valued in TG-6.2 Mangrove and Coastal Wetland Accounting.

5 Acknowledgements

This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

This Circular draws upon the work of the United Nations Expert Group on the Integration of Statistical and Geospatial Information (EG-ISGI), the UN-GGIM Working Group on Marine Geospatial Information, and the SEEA Ecosystem Accounting Technical Committee.

The guidance incorporates standards developed by the International Hydrographic Organization (IHO), the Open Geospatial Consortium (OGC), and the International Organization for Standardization (ISO TC 211).

The IUCN Commission on Ecosystem Management Red List of Ecosystems Thematic Group provided the foundation for ecosystem classification recommendations.

Authors: [Names and affiliations]

Reviewers: [Names and affiliations]

6 References

International Hydrographic Organization (2023). S-100 Universal Hydrographic Data Model, Edition 5.1.0. Monaco: IHO. Available at: https://iho.int/en/s-100-universal-hydrographic-data-model

Keith, D.A., Ferrer-Paris, J.R., Nicholson, E. and Kingsford, R.T. (eds.) (2020). The IUCN Global Ecosystem Typology 2.0: Descriptive profiles for biomes and ecosystem functional groups. Gland, Switzerland: IUCN. DOI: https://doi.org/10.2305/IUCN.CH.2020.13.en

Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: Current State and Trends, Volume 1, Chapter 19: Coastal Systems. Washington, DC: Island Press.

United Nations (2021). System of Environmental-Economic Accounting -- Ecosystem Accounting. New York: United Nations. Available at: https://seea.un.org/ecosystem-accounting

United Nations (2022). Guidelines on Biophysical Modelling for Ecosystem Accounting. New York: United Nations Department of Economic and Social Affairs, Statistics Division. Available at: https://seea.un.org/ecosystem-accounting/biophysical-modelling

United Nations (2022). Operational Framework for Integrated Marine Geospatial Information Management (IGIF-H). Working Group on Marine Geospatial Information. Available at: https://ggim.un.org/

United Nations (2023). Agreement under the United Nations Convention on the Law of the Sea on the Conservation and Sustainable Use of Marine Biological Diversity of Areas beyond National Jurisdiction. New York: United Nations.

United Nations (2025). The Global Statistical Geospatial Framework, Second Edition. New York: United Nations Statistics Division. Available at: https://ggim.un.org/GSGF/

Footnotes