Science‐based planning can support law enforcement actions to curb deforestation in the Brazilian Amazon. Issue 6 (27th June 2022) - Mataveli, Guilherme; de Oliveira, Gabriel; Chaves, Michel E. D.; Dalagnol, Ricardo; Wagner, Fabien H.; Ipia, Alber H. S.; Silva‐Junior, Celso H. L.; Aragão, Luiz E. O. C.
Abstract: While Brazil publicly committed to reduce deforestation in Amazonia at the 26th Conference of the Parties (COP26), the Brazilian parliament is moving toward weakening environmental laws. Deforestation rates continue ascending, reaching in 2021 the highest value since 2006 (13, 235 km 2 ). To overcome this paradox, strategies to curb deforestation are mandatory. The current strategy, " Plano Amazônia 21/22, " prioritizes law enforcement actions to curb illegal deforestation in only 11 Amazonian municipalities. Here, we show that this prioritization is likely to be insufficient since these municipalities account for just 37% of the current deforestation rate. This strategy may also be undermined by the leakage of deforestation actions to unmonitored municipalities. Using a set of spatially explicit datasets integrated into a deforestation‐prediction modeling approach, we propose a science‐based alternative method for ranking deforestation hotspots to be prioritized by law enforcement actions. Our prioritization method accounts for more than 60% of the deforestation, detecting larger deforested areas in both private and public lands, while covering 27% less territory than " Plano Amazônia 21/22 ." Optimizing the detection of priority areas for curbing deforestation, as proposed here, is the first step to reducing deforestation rates and comply with the Brazilian legal commitment of 3925 km 2 year −1 .