Satellite data can show where tree cover changed, but a loss alert or colored map pixel alone does not prove deforestation. To check a claim, locate the specific area, inspect dated imagery, compare suitable alert and annual datasets, and establish whether the change was a lasting human-caused conversion of forest to another land use. State what the evidence does—and does not—show.
First, define what the claim means by deforestation
Before opening a map, write down the place, the alleged event date or reporting window, and the claim you need to check. “Trees disappeared” could mean any visible canopy disturbance, permanent forest conversion, clearing in a particular forest type, or unlawful activity. Those are different questions and require different evidence.
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Global Forest Watch’s April 29, 2026 explanation of University of Maryland (UMD) tree-cover-loss data describes global monitoring of woody vegetation at least five meters tall, at 30-meter resolution, for calendar years 2001–2025. The product records satellite-visible tree-cover disturbance, including mortality or clearing in natural forests, plantations, and tree-crop plantations. As WRI and Global Forest Watch explain, “Tree cover loss does not directly measure deforestation.” A loss signal can result from natural or human causes and can be temporary or permanent. Deforestation ordinarily refers to lasting, human-caused conversion from forest to another land use.
Satellite imagery can show biophysical change. By itself, it cannot establish what land use followed, whether a change will persist, or whether clearing broke a law. Treat those as separate questions to investigate rather than conclusions contained in a map layer.
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Use this workflow to check a location-specific claim
- Specify the claim. Record the named place or parcel, the reported date or time window, and whether the allegation concerns disturbance, permanent conversion, a particular forest type, or illegal clearing. Preserve the original wording so your conclusion answers the allegation actually made.
- Map the location. Find a point or, preferably, the claimed area’s boundary. A broad place name is not enough to assess a particular event. Check whether the location overlaps forest, plantation, protected area, concession, or another relevant boundary. An overlap supplies context; it does not establish who caused the change or whether it was legal.
- Screen for recent alerts. Global Forest Watch’s integrated disturbance layer combines GLAD-L, GLAD-S2, RADD, and DIST-ALERT. Use an alert to identify where to look more closely, not as a final measurement of deforested area.
- Open the imagery behind the signal. Compare dated observations from before and after the claimed event. Check acquisition dates and image conditions, then look for a visible change consistent with the claim. Cloud, haze, smoke, seasonality, fire, logging, roads, and other explanations can affect interpretation.
- Check annual data for area or historical patterns. If the question concerns an estimated area or a trend over time, use an annual loss product rather than treating alert pixels or alert counts as annual totals. Interpret what the product measures before calling its result deforestation.
- Corroborate important conclusions. Compare independent observations or seek official monitoring, local expertise, or field confirmation where appropriate. Keep the distinction between observed change and inferred cause clear in your final account.
Know what each satellite alert can tell you
The alert systems differ in sensor, resolution, and coverage. The integrated layer assigns greater confidence when additional observations or independent systems report change in the same location. Agreement is useful corroboration, but multiple alerts still do not prove cause, permanence, legality, or the land use that followed.
| System | Sensor and resolution | Practical implication |
|---|---|---|
| GLAD-L | Landsat, 30 meters | One component of the integrated alert layer. |
| GLAD-S2 | Sentinel-2, 10 meters | One component of the integrated alert layer. |
| RADD | Sentinel-1 radar, 10 meters | Radar can detect change through cloud cover; the result remains a detection signal with its own limitations. |
| DIST-ALERT | Resolution not stated in the Global Forest Watch integrated-alert explanation | Adds global disturbance coverage beyond forests. |
The Global Forest Watch integrated-alert explanation describes DIST-ALERT integration in January 2026. The GLAD alerts explainer, published September 2, 2020, gives a useful caution that still applies to alert interpretation: alerts pinpoint places where deforestation may be happening and are a starting point for further investigation. It recommends checking imagery, relevant concession or protected-area overlap, and field evidence where appropriate.
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Inspect imagery instead of trusting a colored pixel
For the suspected area, compare observations close to the alleged event with earlier imagery. Make sure the dates bracket the change being claimed; an image captured months later may not resolve when it happened. Look at the area around the signal as well as the flagged pixels, because roads, field boundaries, fire scars, or a larger pattern may help distinguish possible explanations.
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- Check the pattern and context. Ask whether the observed change is consistent with clearing, fire, logging, seasonal variation, or another plausible source of disturbance. A satellite image shows surface conditions, not the actor’s intent.
- Check boundaries carefully. A signal inside a protected area or concession can make the location especially relevant, but overlap alone neither proves responsibility nor establishes illegality.
- Record the evidence. Note the coordinates or boundary, image dates, layer or system used, visible conditions, and what interpretation remains uncertain. This makes the check reproducible by another person.
Where the conclusion matters, seek independent evidence. FAO’s FRA 2020 survey illustrates a more controlled reference approach: trained national experts interpreted Landsat and Sentinel mosaics, using high-resolution imagery as auxiliary evidence, and external experts reinterpreted a subsample for quality control. It is a useful example of why image interpretation and quality checks matter; it does not make a single observer’s map reading equivalent to a verified survey.
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Choose annual loss data for area and trend questions
Alerts are designed to help locate possible change quickly. Annual loss products use more observations and are better suited to area estimates and historical comparison, but they still measure tree-cover loss rather than deforestation unless land-use change is interpreted separately. Do not sum alert pixels and report that sum as deforested area, or use alert counts alone to claim an annual regional trend.
Before comparing annual estimates from different products, align their definitions, thresholds, and time windows. For example, the Global Forest Watch 2025 data explanation says Brazil’s PRODES uses a 6.25-hectare minimum mapping unit and an August–July reporting period, while UMD data includes loss above 0.09 hectares on a calendar-year basis. Those figures are not directly interchangeable: they differ in minimum area and reporting period, as well as in the product definitions and methods that must be checked.
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- Check whether a dataset includes plantations or only specified forest types.
- Check whether it detects stand-replacement loss, partial canopy disturbance, degradation, or only clear-cutting.
- Compare minimum mapping unit and spatial resolution; a smaller pixel does not by itself mean a more accurate interpretation.
- Align calendar-year or seasonal reporting periods and the dates of imagery used.
- For alerts, check their maturity or confirmation status and whether systems are independent.
- Consider how fire, logging, shifting cultivation, and other drivers are treated.
Report the result with the right level of certainty
Accuracy varies by place and biome, and a global accuracy statistic is not a site-specific confidence score. The Global Forest Review’s current methods page summarizes the original 2001–12 tree-cover-loss product assessment as having 13 percent commission error prevalence and 12 percent omission error prevalence globally. Those historical figures are not a current estimate for a particular location; the page warns that accuracy varies by biome and does not provide updated-algorithm assessments for later years.
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The same methods page reports 90.5 percent overall accuracy for a mapped dominant-driver classification model based on 6,955 interpreted samples. That statistic concerns classification of dominant drivers in the model; it is not the accuracy of detecting any individual clearing event.
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A careful conclusion separates observation from inference. For example: “Satellite observations show tree-cover disturbance in this area during the stated period. The imagery and alerts reviewed do not, by themselves, establish whether it was permanent forest conversion, who caused it, or whether it was unlawful.” If independent land-use or field evidence supports a stronger conclusion, identify that evidence and keep the time period and area explicit.
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