> ## Documentation Index > Fetch the complete documentation index at: https://help.polygon-one.com/llms.txt > Use this file to discover all available pages before exploring further. # Geo Analysis in Detail > How the satellite analysis of a plot reaches its result: the checks, forest types, risk levels and what to do when a plot is “Invalid” This page explains step by step how Polygon One checks a plot. It is written for compliance officers. You do not need any background in satellite data. For an overview of all data sources and risk flags, see [Risk Assessment](https://help.polygon-one.com/en/eudr/datensammlung/risikoanalyse.md). ## What the analysis answers The analysis answers one question: **Was forest removed or damaged on this land after 31 December 2020?** That is the EUDR cut-off date (Art. 2(13)). The regulation distinguishes two cases: * **Deforestation** (Art. 2(3)): forest is converted to agricultural use, for example cropland, pasture or an oil palm plantation. * **Forest degradation** (Art. 2(7)): primary forest or naturally regenerating forest is converted into plantation forest or other wooded land. The analysis checks this question for **wood** only. What happened before the cut-off date does not matter for the EUDR. Land that was not forest in 2020 cannot be deforested under the regulation. > [!NOTE] > > The analysis produces a **risk level**, not a legal assessment. It shows you where to look more closely. Whether a risk has been resolved is decided on the basis of evidence. ## The checks in order The checks always run in the same order. As soon as one step settles the result, the analysis stops. ### 1. Is the geometry plausible? First, the system checks whether the area can be a production area at all. It uses the **ESA WorldCover** land-cover map. The plot fails if: * more than **10 %** of the area is built up (buildings, roads, settlement), * more than **10 %** of the area is water, or * there is **no land-cover data** for the geometry, usually because it lies in the sea. If the plot fails, the result is **“Invalid”**. Details and solutions are under [When the geometry is not plausible](#when-the-geometry-is-not-plausible). ### 2. Was there forest on 31.12.2020? Next, the system reads the **JRC Global Forest Cover 2020** map (on the map: **“JRC 2020 forest baseline”**). It shows where forest stood on the cut-off date. * If forest covers at most **1 %** of the plot, there was no forest at the cut-off date. The result is **“Low Risk”**. * **Exception:** if other datasets still show clear tree-cover loss after 2020 (Hansen: more than 1 % of the plot) or a high-confidence disturbance alert after 2020 (GFW), the data disagree. The result is then **“Moderate Risk”** with the flag **“Datasets disagree about forest presence at the EUDR cutoff”**. This never leads to high risk. ### 3. Which forest type was there? (wood only) For wood, the system also reads the **JRC Global Forest Types 2020** map (GFT). It splits the 2020 forest into **Primary Forest**, **Natural Forest** and **Planted Forest**. This decides whether a later harvest counts as degradation. The forest types are [explained below](#forest-types). For other commodities (for example cocoa, coffee, soy), the forest type does not matter. Only the conversion of forest into agriculture counts there. ### 4. Did anything change after 2020? Now the system looks for changes after the cut-off date. It uses several independent datasets: * **Hansen Global Forest Change:** tree-cover loss measured each year. * **JRC TMF (Tropical Moist Forest):** deforestation and degradation in tropical moist forests, with a time series. * **GFW disturbance alerts (GFW/DIST):** near-real-time alerts about vegetation disturbance. * **PALSAR radar:** confirms forest independently of clouds and detects shade crops under tree canopy. * **ESA WorldCover:** land cover, for example cropland, forest or agroforestry. ### 5. How the signals become a risk level No single dataset decides on its own. These rules apply: **Measured tree-cover loss (Hansen)** * **“High Risk”** arises when more than **5 %** of the 2020 forest area was lost after 2020. In addition, the 2020 forest area must be at least **0.5 ha** and the lost area at least **0.18 ha** (two satellite pixels). * Smaller but detectable loss leads to **“Moderate Risk”** with the note “Minor post-2020 tree cover change detected”. For wood, a loss of up to **1 %** of the forest area is treated as too small; the other datasets then decide. * Loss smaller than one pixel (**0.09 ha**) is not treated as deforestation. Signals that small cannot be told apart from edge effects at the plot boundary. **Disturbance alerts (GFW)** * Alerts **alone** lead to **“Moderate Risk”** at most. * **“High Risk”** only arises when a second dataset confirms the loss (Hansen or TMF), **or** when high-confidence alerts occur in at least **two different years** after 2020. In the second case, the 2020 forest area must be at least 0.5 ha and the alert area at least 0.18 ha. * Low-confidence alerts only lead to **“Moderate Risk”**. **Tropical moist forest (TMF)** * If TMF shows deforestation that the time series dates **before 2020**, it counts as historical and does not raise the risk. * A TMF signal **after 2020 on its own** leads to **“Moderate Risk”** at most. For **“High Risk”**, a second dataset must confirm the change. Exception: for wood on primary or natural forest, TMF deforestation after 2020 counts as evidence of degradation (see the next paragraph). **Forest degradation for wood (GFT)** * If primary forest (more than **1 %** of the plot) or natural forest (more than **5 %** of the plot) stood on the land in 2020, the system checks for degradation. * **“High Risk”** only arises when a change after 2020 is confirmed: by Hansen or TMF, and for primary forest also by alerts in at least two years. For natural forest alone, alerts are never enough. * For primary forest, in addition: if the measured change lies demonstrably on the planted part only, the result stays at **“Moderate Risk”**. * Without a confirmed change, the result stays at **“Moderate Risk”** (“Primary or naturally regenerating forest at the 2020 baseline, but no post-2020 conversion evidence. Verification recommended.”). **Low risk** * **“Low Risk”** arises, for example, when Hansen shows no loss **and** GFW covers the plot and reports no alerts. * Or, for wood, when the land is a confirmed plantation (see [Forest types](#forest-types)). **Missing data never lowers the risk** * If a dataset is missing, the system does not treat the plot as clean. Typical results are then **“Moderate Risk”**, for example when GFW data is missing, when no change dataset covered the plot after 2020, or when GFT data is missing for wood (“Forest detected but GFT degradation data unavailable”). * Forest presence alone is not evidence of deforestation either. If forest stands on an agricultural plot and no dataset shows a change, the result stays at **“Moderate Risk”** for verification. ## Forest types The forest types come from the **JRC Global Forest Types 2020** map. They describe the state **on the cut-off date**. On the map, the layers are called **“GFT natural or primary forest”** and **“GFT planted forest”**. | Forest type (label in the app) | What it means | Example | | - | - | - | | **Primary Forest** | Original forest of native species, without visible human intervention. | Untouched rainforest in the Amazon or an old mountain forest in the Carpathians. | | **Natural Forest** (naturally regenerating forest) | Forest that renews itself, even if it is used. | A managed beech or oak forest in Europe that regrows from its own seeds. | | **Planted Forest** | Planted forest, usually one or a few species, in fixed rows and rotations. | A eucalyptus or pine plantation that is harvested and replanted every few years. | ### What this means for the EUDR * **Harvesting a plantation is neither deforestation nor degradation.** When a plantation is harvested and replanted, it stays forest of the same type. * **Degradation** means primary or natural forest becomes plantation forest or other wooded land (Art. 2(7)). That is why the 2020 forest type matters for wood. * **Deforestation** means forest of any type becomes agricultural land (Art. 2(3)). ### When land counts as a confirmed plantation The system treats wood land as a **confirmed plantation** when all three conditions hold: 1. primary forest covers at most **1 %** of the plot, 2. natural forest covers at most **5 %** of the plot, and 3. more than **half** of the 2020 forest is planted forest. On a confirmed plantation, tree-cover loss and disturbance alerts after 2020 count as harvest. The result is then **“Low Risk”**. The signals stay visible, and the land continues to be monitored for a change in land use. The harvest attribution no longer applies if: * TMF measures **deforestation after 2020**, meaning the land does not become forest again, or * the disturbances lie on the natural or primary part of the land and affect at least **5 ha** and at least **10 %** of that part. ### Datasets and their limits | Dataset | What it measures | Limits | | - | - | - | | **ESA WorldCover** | Land cover: cropland, forest, water, settlement, grassland, agroforestry | A snapshot. Basis of the plausibility check, not a change dataset. | | **JRC Global Forest Cover 2020** | Where forest stood on the cut-off date | No forest types. A data gap and treeless land look the same. | | **JRC Global Forest Types 2020 (GFT)** | Primary, natural and planted forest in 2020 | Used for wood only. Shows the state in 2020, not a change. | | **Hansen Global Forest Change** | Annual tree-cover loss | 30 m resolution (one pixel = 0.09 ha). Does not distinguish harvest from conversion. The most recent months are missing. | | **JRC TMF** | Deforestation and degradation in tropical moist forests, with a time series | Available in the tropics only. Elsewhere, the gap is reported. | | **GFW disturbance alerts (GFW/DIST)** | Near-real-time signs of vegetation disturbance | Show a disturbance, not the later land use. The monitored time window is limited. | | **PALSAR radar** | Forest structure, even under clouds | Confirms forest, does not measure change. Not available in every region. | ## Risk levels | Level (label in the app) | What it means | What you do | | - | - | - | | **Low Risk** | The analysis found no deforestation and no degradation after the cut-off date. | Nothing. No further geo step is needed for this plot. | | **Moderate Risk** | The analysis could not resolve whether conversion happened after the cut-off date. A confirmation or a dataset is missing. | Check the plot's flags and obtain suitable evidence. | | **High Risk** | At least two independent signals, or a clear measured loss, point to conversion after the cut-off date. | Clarify the case with the supplier. Without solid evidence, the plot counts as non-compliant. | | **Invalid** | The geometry is not plausible. There is no risk assessment. | Correct the geometry (see below). | > [!NOTE] > > High risk is a finding of the analysis, not a final verdict. Suitable evidence can mitigate the risk. Which evidence closes a geo risk is described under [Evidence Acceptance](https://help.polygon-one.com/en/eudr/datensammlung/nachweis-akzeptanz.md). ## When the geometry is not plausible If the result is **“Invalid”**, the plot failed the plausibility check. The plot details then show **“Sanity Check Failed”** and the reason. There are three reasons: | Message in the app | Meaning | | - | - | | **“Plot overlaps Urban area (…%)”** | More than 10 % of the area is built up. | | **“Plot overlaps Water body (…%)”** | More than 10 % of the area is water. | | **“No land-cover data for this geometry …”** | There is no land data for the area, usually because it lies in the sea. | The article then shows the status **“Invalid”**. It blocks until the plot is resolved. How this affects the article status is described in the [Findings Catalogue](https://help.polygon-one.com/en/eudr/datensammlung/befund-katalog.md). > [!WARNING] > > **Re-running the analysis will not change the result.** The check reads the same geometry and reaches the same result. Change the geometry first. ### Typical causes * **Company or warehouse address instead of the growing area.** The coordinates point to an office, a sawmill or a warehouse. That lies in a settlement, hence the overlap with urban area. * **Latitude and longitude swapped.** The point lands somewhere completely different, often in the sea. Then there is no land-cover data. * **Point instead of polygon.** For the analysis, a point is expanded into a circle the size of the declared area. If the point sits at the farmstead or in the village, the circle falls on buildings and roads. * **Boundary drawn into a lake or river.** The polygon reaches past the shore into the water. ### What you do **The geometry is wrong:** correct it, or ask the supplier to correct it. As the customer, you can correct or delete any invalid plot. A supplier can only change the plots it created itself. After the correction, the plot is analysed again. **The geometry is correct:** sometimes the area is captured correctly and still overlaps, for example a plantation right on a lakeshore. In that case, contact support. > [!NOTE] > > If the analysis found clear forest change after 2020 despite the “Invalid” result, the result says so explicitly: “Material post-2020 tree-cover change was also detected on this plot … — the geometry problem does not dismiss it.” Take this note seriously, even before the geometry is resolved. ## What to do with moderate or high risk 1. Open the plot and read the **Risk Flags**. They name the dataset and the reason. 2. On the map, use **“Layers”** to see where the change lies, for example **“Hansen loss on planted forest”** or **“GFW alerts on natural or primary forest”**. 3. Request suitable evidence from the supplier, such as a planting record or a management plan. You do this in the compliance gaps view. > **[Risk Mitigation](https://help.polygon-one.com/en/eudr/datensammlung/risiko-behebung.md)** > > Resolve detected risks with the compliance gaps view. > **[Evidence Acceptance](https://help.polygon-one.com/en/eudr/datensammlung/nachweis-akzeptanz.md)** > > Which evidence actually closes a geo risk. ## Frequently asked questions > **Why is my plantation flagged?** > > Usually for one of these reasons: > > * **The map sees more natural or primary forest than expected.** If primary forest exceeds 1 % or natural forest exceeds 5 % of the plot, the land does not count as a confirmed plantation. The system then checks for degradation. > * **GFT data is missing.** Without the forest type, the system cannot tell harvest from conversion. Result: “Moderate Risk”. > * **TMF measures deforestation after 2020.** The land did not become forest again after the harvest. That speaks against a normal rotation harvest. > * **The disturbances lie on the natural forest part.** If they affect at least 5 ha and 10 % of that part there, the harvest attribution no longer applies. > > A planting record, a plantation registry entry or a management plan for the stand can clarify this. > **Why does the map show forest loss, but the result is “Low Risk”?** > > Forest loss is not always deforestation under the EUDR. Common reasons: > > * **Harvest on a confirmed plantation.** The land stays forest. The result then names the harvest and the planted share. > * **The loss is smaller than one satellite pixel (0.09 ha).** Signals that small often come from the plot boundary. > * **There was no forest on the land in 2020**, and the change is too small to contradict that. > * **The loss is from before 2020.** It is then labelled “Historical deforestation (pre-cutoff)” and does not count for the EUDR. > **Why “Moderate Risk” when nothing was found?** > > Because missing data never lowers the risk. If no change dataset covered the plot after 2020, or the GFW alerts are missing, the system cannot rule out conversion. The flag then names the missing dataset, for example “No post-2020 change dataset available”. > **Why don't disturbance alerts lead to high risk straight away?** > > An alert shows that the vegetation changed. It does not show whether agriculture follows or the forest grows back. That is why high risk needs confirmation by a second dataset, or alerts across at least two years. > **Does re-running the analysis help with “Invalid”?** > > No. The check reads the same geometry and reaches the same result. Correct the geometry. If it is correct, contact support. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.