A deceptively simple question underlies many global environmental policies: where, exactly, are the world’s forests? A new study suggests the answer depends heavily on which map one consults—and that the differences are large enough to reshape climate targets, conservation priorities, and development spending. Researchers Sarah Castle, Peter Newton, Johan Oldekop, Kathy Baylis, and Daniel Miller compared ten widely used global forest datasets derived from satellite imagery. These products underpin everything from carbon accounting to biodiversity assessments. Yet they rarely agree. Across the area identified as forest by at least one dataset, only about 26% was classified as forest by all of them. Even after adjusting maps to a common spatial scale, agreement improved only modestly. This divergence stems partly from differing definitions. Some datasets count areas with sparse tree cover as forest; others require dense canopy. A threshold of 10% canopy cover, for example, will include savannas and woodland mosaics, while a 70% threshold captures only closed forests. Resolution also matters. High-resolution imagery can detect narrow forest strips or small patches that coarser data miss. Methodological choices—such as sensor type, machine-learning algorithm, and training data—introduce further variation. A) Spatial agreement of forest cover classifications between eight land cover datasets. Spatial agreement is defined as the number of datasets that define a pixel as forest, between 1 and 8. Full agreement between all eight datasets corresponds to a value of eight (dark green), and no agreement between the datasets corresponds to a value of 1 (dark purple). No color (gray) indicates…This article was originally published on Mongabay
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