What is deforestation analysis and where does it get hard?
Beyond the Satellite: Why EUDR Deforestation Analysis Requires a Sophisticated Methodology
The deforestation analysis required by the EU Deforestation Regulation (EUDR) sounds straightforward at first glance: products may not be placed on the EU market if they originate on a plot of land that was a forest before December 31,2020. The EU publishes its own forest map (the Joint Research Council's Global Forest Cover, the JRC GFC, now in Version 4). Companies should be able to map their supply chains and determine if products meet the EUDR criteria by comparing it against the JRC GFC. But the law itself requires more than one vote: why?
To begin with, the EU's forest map has a significant margin of error. It overestimates global forest cover by 9%, and in Africa, by 25%. It cannot generally distinguish between planted forests and native forests. And in some areas, it can misinterpret 60-75% of shade-grown agriculture as forest.
Perhaps because of this, the EUDR requires companies to compare multiple sources of data to assess whether a plot of land has been deforested, including radar, crop maps, and imagery validation from multiple satellites or other sensors. Each of these carries its own margin of error. Some of the causes:
Clouds cover the tropics for months at a time, blocking many wavelengths of satellite imagery (but not radar)
Satellite imagery alone can be easily misinterpreted: shade-grown cocoa and complex agroforestry systems can look exactly like untouched forest from above
Planted and sustainably harvested forests operate on a cycle that can easily be mistaken for illegal clearing
Vegetation changes over the course of the years, so changes that might appear to be degradation are actually seasonal
To navigate these complexities without unfairly penalizing farmers or exposing enterprises to compliance risk, companies must move beyond simple visual checks. Enterprise-scale compliance requires an automated, multi-layered risk assessment methodology to determine deforestation risk. And everything - all raw data collection, all quality checks, all third-party validation, must be conducted securely and maintain a fully transparent audit trail, which is no small challenge when supply chains for large companies often rely on hundreds of thousands of individual farms.
The Three Principles of Robust Analysis
To accurately assess risk at scale, Sourcemap’s methodology evaluates each forest or farm plot through an automated series of tests based on three core principles:
Establish forest cover in 2020: Forest-cover maps determine whether a plot is in scope for EUDR.
Corroborate deforestation status: A finding of tree loss requires agreement between sources derived from different satellite sensors.
Confirmation: A confirmed forest loss signal is only downgraded from a deforestation finding if there is positive evidence that the affected area regrew or was not converted to agriculture.
A Layered Testing Logic
Rather than relying on a single map, an effective system must pass plots through a rigorous, sequenced logic, exiting the testing phase only when a definitive result is reached.
Validating Intake: The first step ensures the geometry, coordinate precision, and location are accurate. For example, cattle establishments submitted as single points cannot be screened by satellite because the extent of the grazing land is unknown, prompting a request for the actual land outlines.
Independent Agreement: No single source can flag a plot. Tree loss signals require confirmation from different sensor types, ensuring that radar data confirms optical signals.
Identifying Pre-existing Farmland: The methodology proactively identifies land that was already agricultural at the cut-off—including shaded agroforestry and young plantings—before any plot is flagged.
Confirming End Use: Tree loss is classified as deforestation only where agricultural conversion is evidenced. The system tests whether land became agricultural using crop, land-cover, loss-cause, and regrowth maps.
Moving Beyond Pass/Fail
A sophisticated approach recognizes that supply chain data is rarely binary. Previous iterations of deforestation analysis often resulted in simple "pass" or "fail" grades, which failed to account for data gaps. Sourcemap’s methodology replaces this with five evidence-based results:
Out of scope at cut-off: No forest existed at the end of 2020, or the land was already agricultural.
No deforestation signal detected: Forest existed in 2020, there has been no tree loss since, and satellite coverage is adequate.
Insufficient evidence: A single unconfirmed signal, poor satellite coverage, or weak crop evidence was detected.
Corroborated disturbance, end use unconfirmed: Independent sources confirm tree loss, but agricultural use is not established.
Deforestation signal, non-negligible risk: Independent sources confirm tree loss, and the land became agricultural.
Crucially, plots that return "Insufficient evidence" or "Corroborated disturbance" do not automatically fail; instead, they generate a targeted request to the supplier specifying the exact missing evidence needed to clear the plot. Every result is stored with a versioned evidence record that logs the datasets consulted, their versions, and satellite coverage, ensuring results are fully reproducible.
Real deforestation analysis requires more than drawing polygons on a map and looking at Google Earth. It requires corroborating optical and radar data, understanding the lifecycle of the specific commodity crop, and maintaining an unassailable evidentiary trail. As regulatory guidance and datasets evolve, leveraging a multi-layered, sensor-agnostic methodology is the only way to ensure both full compliance and an equitable supply chain.
To learn more about how to stand up a robust deforestation assessment methodology that stands up to regulatory scrutiny get in touch with the experts at Sourcemap: https://www.sourcemap.com/demo
