By Saurav Shrivastava, Head of Product, Farmforce
The forest data behind EUDR compliance
Many agribusinesses are asking the same question: which forest datasets can we rely on for compliance?
At the heart of EUDR is a demanding yet straightforward requirement: proving that commodities such as cocoa, coffee, palm oil, and rubber did not originate from recently deforested land or overlap with known protected areas. Yet behind that simplicity lies a complex technical challenge: choosing datasets that correctly identify what is—and is not—a forest according to the EU’s definition.
As authorities prepare for stricter audits and automated checks, the quality of your forest data has become a material compliance risk — not just a technical detail.
Global Forest Watch Pro (GFW Pro), developed by the World Resources Institute, offers one of the most robust and scientifically validated approaches to this question. At Farmforce, we integrate and leverage complementary tools, such as GFW Pro, to strengthen our customers’ EUDR readiness and build confidence in the data that underpins their traceability systems.
Meeting the EUDR forest definition
The EUDR defines “forest” through three key criteria:
- Canopy cover of at least 10%
- Tree height of at least 5 meters
- Minimum mapping unit of 0.5 hectares
GFW Pro aligns closely with this definition through the combined use of JRC Global Forest Cover v2 (GFC v2) and the Science-Based Targets Network (SBTN) Natural Lands Map, both benchmark datasets for EUDR-relevant monitoring.
The JRC GFC v2 dataset, produced by the European Commission’s Joint Research Centre, is a global forest classification built specifically for regulatory and monitoring purposes. The SBTN Natural Lands Map further refines this by distinguishing natural from non-natural forests using the Spatial Database of Planted Trees (SDPT) — essential for separating plantations or agroforestry from natural ecosystems.
Together, these layers provide a scientifically grounded, policy-aligned foundation for deforestation risk assessment.
Why accuracy matters more than resolution
High-resolution imagery can be seductive. Crisp pixels give a comforting illusion of precision. But EUDR checks are not aesthetic contests; they are classification challenges.
When a dataset has not been rigorously validated, higher resolution increases the number of confidently wrong pixels.
Independent evaluations have shown that the Hansen/GLAD Tree Cover Loss (TCL) dataset — used within GFW Pro — remains one of the most accurate benchmarks for detecting forest loss. While some datasets may offer sharper pixels, Hansen/GLAD’s advantage lies in its consistent methodology, long-term validation record, and minimal bias in both over- and underestimation of forest area.
In other words, for EUDR compliance, accuracy beats aesthetics. The credibility of your due diligence statement depends more on the scientific reliability of your forest data than on visual resolution.
EUDR doesn’t ask “How sharp is your imagery?” It asks, “Can you prove that your classification is correct?”
To further minimize uncertainty, GFW Pro integrates additional global datasets and refines them with regional data, such as MapBiomas in Brazil, ensuring that local realities are accurately represented at the worldwide scale.
Cross-validation: building confidence through multiple perspectives
One of GFW Pro’s strongest features is its ability to cross-validate between datasets. Rather than relying on a single forest definition or loss layer, it compares results across JRC GFC v2, SBTN Natural Lands, and Hansen/GLAD TCL.
This approach:
- Distinguishes non-natural forest areas, including plantations and agroforestry systems;
- Improves confidence in findings by identifying agreement across datasets; and
- Flags high-priority cases when multiple layers confirm deforestation within the same area.
For selected geographies such as Argentina, Brazil, Colombia, and Indonesia, GFW Pro also integrates country-specific forest classification and loss datasets, further reducing uncertainty in EUDR compliance checks. This layered validation is crucial: EUDR demands defensible, evidence-based assessments, not assumptions.
In practice, cross-validation acts as a brake against false positives and false negatives — both of which can trigger supply chain disruptions under EUDR.
How does this align with the Thünen Institute’s findings
In 2024, the Thünen Institute released one of the most comprehensive studies assessing the suitability of global forest maps for EUDR-compliant monitoring. Its findings reinforced the need for scientifically validated, multi-source approaches to balance accuracy and coverage.
The study highlighted the University of Maryland’s GLAD and GFW/Hansen datasets as among the most accurate globally, with minimal over- or underestimation of forest area. While Hansen/GLAD falls short on one EUDR criterion — the minimum mapping unit of 0.5 ha — its integration with compliant layers, such as JRC GFC v2 and SBTN Natural Lands Map, effectively addresses that gap.
In short, GFW Pro’s architecture aligns closely with the Thünen Institute’s recommendations: combining global and regional datasets, enabling cross-validation, and refining global data with national sources.
Integrating trusted datasets in traceability workflows
For Farmforce, forest data is more than a compliance checkbox — it’s the foundation for trustworthy traceability. By integrating validated datasets like those used in GFW Pro, Farmforce enables our customers to:
- Verify non-deforestation claims with confidence.
- Cross-check farm boundaries against EUDR forest definitions; and
- Document geolocation proof within Due Diligence Statements (DDS) in a format auditable by authorities or buyers.
This scientific rigor underpins our broader mission: empowering agribusinesses to make sustainability and compliance operational, not optional. Whether the data originates from a global model like JRC GFC v2 or a national system like MapBiomas, Farmforce’s platform ensures it can be integrated, validated, and acted upon — from the first mile to the export declaration.
This layered approach is essential because no single dataset meets all EUDR criteria in isolation.
A DDS isn’t merely uploaded; it must be defensible. Farmforce ensures that every geolocation point, polygon, and forest assessment is traceable back to validated, audit-ready sources.
Conclusion: choosing the proper data foundation
As EUDR implementation accelerates, one lesson is becoming clear: not all forest maps are created equal. The real differentiator lies in data integrity — combining the best available science, local context, and transparent validation methods. GFW Pro’s use of JRC GFC v2, SBTN Natural Lands Map, and Hansen/GLAD Tree Cover Loss exemplifies that principle. By prioritizing accuracy and cross-validation over pure resolution, it sets a data standard that helps companies navigate EUDR compliance with confidence.
At Farmforce, we continue to leverage these validated datasets as part of our deforestation risk monitoring workflows — giving our customers a robust, scientifically sound foundation for demonstrating compliance and protecting the forests that sustain our agricultural systems.
In the race toward EUDR readiness, companies don’t need the sharpest imagery. They need the most trustworthy data foundation — one built on validation, consistency, and scientific rigor.
