The Spatial Sustainability Hub (SSH) produces technical evidence to support EUDR due diligence under Articles 8–11 of Regulation (EU) 2023/1115 — including geolocation checks, post-2020 forest-loss screening, risk indicators, and audit-ready evidence packets — for agricultural commodity supply chains. Every analysis queries a curated set of satellite and remote-sensing datasets to determine whether forest cover was lost within a supplied plot boundary after 31 December 2020 — the cutoff date established by the EU Deforestation Regulation.
SSH provides decision-support evidence. The operator or trader placing the product on the EU market remains responsible for the complete due-diligence assessment under Articles 8–11 and for submitting the due-diligence statement in the EU Information System.
This document describes the exact methodology, datasets, output fields, and known limitations used in every analysis. It is intended for operators, auditors, regulators, suppliers, and the general public. The methodology is version-controlled: every change is documented in the Version History section below and takes effect only after a deliberate, reviewed code deployment.
Cite this methodology:v1.1 · 2026-06-08
SSH analyses use the FAO forest definition as codified in EUDR Article 2(6): land spanning more than 0.5 hectares, with trees capable of reaching more than 5 metres in height, and a tree canopy cover greater than 10%. The reference date is 31 December 2020 — land that was forest on that date is subject to the Regulation.
The fao preset is the default for all analyses (EUDR Art. 2(6)). The preset used for each analysis job is recorded in the job metadata and surfaced in every per-plot evidence packet.
For the fao and eudr_strict presets, land classified as agricultural use or commodity plantation is excluded from the forest baseline (EUDR Art. 2(13)): pixels where FDP commodity probability maps indicate established crop presence, or where ESA WorldCover classifies the area as Cropland or Built-up, are masked out before the forest baseline is computed. This reduces the risk that mature cocoa, palm, rubber, or coffee plantations are misclassified as forest, while preserving an auditable record of the masks and thresholds used.
Four analysis presets are available. The fao preset is the Article 2(6)-aligned default applied to all standard analyses. The hansen_30 preset applies a 30% canopy threshold appropriate for analyses focused on closed-canopy humid tropical forest where open-canopy ecosystem conversion is not the primary risk. Operators may select an alternative preset at submission time.
* Agricultural-use exclusion applies to the fao and eudr_strict presets only — FDP commodity maps (cocoa, palm, coffee, rubber) ∪ WorldCover Cropland/Built-up (EUDR Art. 2(13)).
| Preset | Canopy threshold | Min patch | Agri-exclusion | Use case |
|---|---|---|---|---|
| fao | 10% | 0.5 ha | yes * | EUDR Article 2(6) default |
| hansen_10 | 10% | none | no | Open-canopy commodities |
| hansen_30 | 30% | none | no | Dense humid tropical forest |
| eudr_strict | 30% | 0.5 ha + primary forest mask | yes * | Maximum rigour |
SSH queries 14 datasets. Datasets marked context are shadow signals — they are computed in parallel but do not influence the compliance verdict or appear in sensors_run.
| Dataset | Provider | Measures | Coverage | License |
|---|---|---|---|---|
| Hansen Global Forest Watch | University of Maryland / Google | Annual tree cover loss 2001–2023 | 2000–2023, annual | CC-BY 4.0 |
| JRC Tropical Moist Forests | European Commission JRC | Annual deforestation and degradation in tropical moist forest 1990–2023 | 1990–2023, annual | CC-BY 4.0 |
| SBTN Natural Lands Map v1.1context | WRI / Science Based Targets Network (SBTN) | Global natural lands classification at 2020 baseline | 2020 baseline (static) | CC-BY-SA 4.0 |
| DeDuCE Commodity Attributioncontext | Trase / Chalmers University | Country-level deforestation attributed to traded commodities | 2001–2022, annual | CC-BY 4.0 |
| Forest Data Partnership — Cocoacontext | FDP Consortium | Cocoa suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 |
| Forest Data Partnership — Coffeecontext | FDP Consortium | Coffee suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 |
| Forest Data Partnership — Palm Oilcontext | FDP Consortium | Oil palm suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 |
| Forest Data Partnership — Rubbercontext | FDP Consortium | Rubber suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 |
| JAXA Forest/Non-Forestcontext | JAXA | Annual forest/non-forest classification from PALSAR-2 SAR 2015–2020 | 2015–2020, annual | Free for research use |
| ESA WorldCovercontext | ESA | Global land cover at 10 m resolution, 2020 and 2021 | 2020, 2021 | CC-BY 4.0 |
| GEDI Canopy Heightcontext | NASA | Global canopy height from spaceborne lidar 2019–2023 | 2019–2023 | Public domain (NASA) |
| MapBiomas Alert | MapBiomas Coalition | Near-real-time deforestation alerts in Brazil and other Latin American countries | 2019–present, monthly | CC-BY 4.0 |
| PRODES Deforestation | INPE | Annual primary forest deforestation in the Brazilian Amazon | 1988–present, annual | Public domain (Brazilian federal government) |
| KLHK Peatland Map | Indonesia Ministry of Environment and Forestry (KLHK) | National peatland extent and depth classification | 2019 baseline (static) | Public domain (Indonesian government) |
Extended table including GEE asset IDs, spatial resolution, DOIs, and known coverage gaps:
| Dataset | Provider | Measures | Coverage | License | Resolution | GEE Asset | DOI | Known Gaps |
|---|---|---|---|---|---|---|---|---|
| Hansen Global Forest Watch | University of Maryland / Google | Annual tree cover loss 2001–2023 | 2000–2023, annual | CC-BY 4.0 | 30 m | UMD/hansen/global_forest_change_2023_v1_11 | 10.1126/science.1244693 | Detection lag up to 18 months in persistently cloud-covered regions (Congo Basin, insular SE Asia). Detects tree cover loss only — does not capture open-canopy ecosystem conversion (cerrado, chaco, savanna). |
| JRC Tropical Moist Forests | European Commission JRC | Annual deforestation and degradation in tropical moist forest 1990–2023 | 1990–2023, annual | CC-BY 4.0 | 30 m | projects/JRC/TMF/v1_2023/AnnualChanges | 10.1016/j.scitotenv.2021.145901 | Known cloud shadow artefacts in equatorial Africa. Tropical moist forest biome only — does not cover dry forest, savanna, or peatland. |
| SBTN Natural Lands Map v1.1context | WRI / Science Based Targets Network (SBTN) | Global natural lands classification at 2020 baseline | 2020 baseline (static) | CC-BY-SA 4.0 | 30 m | projects/wri-datalab/assets/sbtn-natural-lands-map/v1-1/naturalLandsMap_v1_1_2020 | 10.46830/writn.23.00120 | Static 2020 baseline only — does not detect post-2020 changes. Best used as context signal for non-forest natural ecosystems (cerrado, chaco, wetlands). |
| DeDuCE Commodity Attributioncontext | Trase / Chalmers University | Country-level deforestation attributed to traded commodities | 2001–2022, annual | CC-BY 4.0 | National (country-level) | — | 10.5281/zenodo.10674962 | Country-level signal only — does not measure deforestation on any specific plot. Attribution derived from national trade statistics. |
| Forest Data Partnership — Cocoacontext | FDP Consortium | Cocoa suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 | 10 m | projects/forestdatapartnership/assets/cocoa/cocoa_2020 | — | Probability score only — threshold interpretation is operator-defined. |
| Forest Data Partnership — Coffeecontext | FDP Consortium | Coffee suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 | 10 m | projects/forestdatapartnership/assets/coffee/coffee_2020 | — | Probability score only — threshold interpretation is operator-defined. |
| Forest Data Partnership — Palm Oilcontext | FDP Consortium | Oil palm suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 | 10 m | projects/forestdatapartnership/assets/palm/palm_2020 | — | Probability score only — threshold interpretation is operator-defined. |
| Forest Data Partnership — Rubbercontext | FDP Consortium | Rubber suitability probability 2020 and 2023 | 2020, 2023 snapshots | CC-BY 4.0 | 10 m | projects/forestdatapartnership/assets/rubber/rubber_2020 | — | Probability score only — threshold interpretation is operator-defined. |
| JAXA Forest/Non-Forestcontext | JAXA | Annual forest/non-forest classification from PALSAR-2 SAR 2015–2020 | 2015–2020, annual | Free for research use | 25 m | JAXA/ALOS/PALSAR/ANNUAL/FNF4 | — | SAR-based — captures structure, not spectral reflectance. May over-classify dense shrublands as forest. |
| ESA WorldCovercontext | ESA | Global land cover at 10 m resolution, 2020 and 2021 | 2020, 2021 | CC-BY 4.0 | 10 m | ESA/WorldCover/v200 | 10.5281/zenodo.5571936 | Two snapshots only (2020, 2021) — not a time series. |
| GEDI Canopy Heightcontext | NASA | Global canopy height from spaceborne lidar 2019–2023 | 2019–2023 | Public domain (NASA) | 1 km (resampled from 25 m footprints) | LARSE/GEDI/GEDI02_A_002_MONTHLY | — | Sparse sampling outside equatorial band (±52° latitude). Footprint gaps filled by spatial interpolation. |
| MapBiomas Alert | MapBiomas Coalition | Near-real-time deforestation alerts in Brazil and other Latin American countries | 2019–present, monthly | CC-BY 4.0 | 30 m | projects/mapbiomas-public/assets/alerts/v2024 | — | Coverage limited to Latin America (Brazil, Bolivia, Colombia, Peru, others). |
| PRODES Deforestation | INPE | Annual primary forest deforestation in the Brazilian Amazon | 1988–present, annual | Public domain (Brazilian federal government) | 30 m | projects/mapbiomas-public/assets/brazil/prodes | — | Brazil Amazon biome only. Annual update cycle — intra-year events captured by MapBiomas Alert. |
| KLHK Peatland Map | Indonesia Ministry of Environment and Forestry (KLHK) | National peatland extent and depth classification | 2019 baseline (static) | Public domain (Indonesian government) | 250 m | — | — | Indonesia only. Static baseline — does not capture drainage or degradation after 2019. |
eudr_compliant = true if no post-cutoff loss is detected above the de minimis threshold across all active verdict sensorseudr_compliant. Accuracy is validated through an independent per-country confusion-matrix assessment against held-out reference plots — not solely by internal reproducibility hashing.null and excluded from the aggregation.sensors_run.sensors_run.Every analysis returns a DeforestationResult object. The six verdict fields below determine compliance status:
eudr_compliantbooleandeforestation_hafloat | nulldeforestation_pctfloat | nullmulti_sensor_agreementboolean | nullcausalitystring | nullsensors_runstring[]dra_signalbooleanShadow context signals — computed for every job but excluded from the compliance verdict and from sensors_run:
sbtn_natural_land_2020float | nullcontext signaldeuce_attribution_hafloat | nullcontext signaldeuce_attribution_yearint | nullcontext signalfdp_cocoa_prob_2020float | nullcontext signalfdp_cocoa_prob_2023float | nullcontext signalfdp_coffee_prob_2020float | nullcontext signalfdp_coffee_prob_2023float | nullcontext signalfdp_palm_prob_2020float | nullcontext signalfdp_palm_prob_2023float | nullcontext signalfdp_rubber_prob_2020float | nullcontext signalfdp_rubber_prob_2023float | nullcontext signalfdp_declared_cropstring | nullcontext signalcarbon_co2e_loss_mgfloat | nullcontext signaljaxa_fnf_2020float | nullcontext signalesa_worldcover_classstring | nullcontext signalgedi_canopy_height_mfloat | nullcontext signaldeuce_attribution_ha and deuce_attribution_year are country-level signals derived from national trade statistics. They do not measure deforestation on the specific plot.