Government commitments and market transitions lay the foundation for an effort to save the forest and reduce carbon emission.

Understanding the interplay between climate and land-use dynamics is a fundamental concern for assessing the vulnerability of Amazonia to climate change. In this study, we analyse satellite-derived monthly and annual time series of rainfall, fires and deforestation to explicitly quantify the seasonal patterns and relationships between these three variables, with a particular focus on the Amazonian drought of 2005. Our results demonstrate a marked seasonality with one peak per year for all variables analysed, except deforestation. For the annual cycle, we found correlations above 90% with a time lag between variables. Deforestation and fires reach the highest values three and six months, respectively, after the peak of the rainy season. The cumulative number of hot pixels was linearly related to the size of the area deforested annually from 1998 to 2004 (r 2 Z0.84, pZ0.004). During the 2005 drought, the number of hot pixels increased 43% in relation to the expected value for a similar deforested area (approx. 19 000 km 2 ). We demonstrated that anthropogenic forcing, such as land-use change, is decisive in determining the seasonality and annual patterns of fire occurrence. Moreover, droughts can significantly increase the number of fires in the region even with decreased deforestation rates. We may expect that the ongoing deforestation, currently based on slash and burn procedures, and the use of fires for land management in Amazonia will intensify the impact of droughts associated with natural climate variability or human-induced climate change and, therefore, a large area of forest edge will be under increased risk of fires.

Mining and dams threaten protected areas

A prominent goal of policies mitigating climate change and biodiversity loss is to achieve zero-deforestation in the global supply chain of key commodities, such as palm oil and soybean. However, the extent and dynamics of deforestation driven by commodity expansion are largely unknown. Here we mapped annual soybean expansion in South America between 2000 and 2019 by combining satellite observations and sample field data. From 2000–2019, the area cultivated with soybean more than doubled from 26.4 Mha to 55.1 Mha. Most soybean expansion occurred on pastures originally converted from natural vegetation for cattle production. The most rapid expansion occurred in the Brazilian Amazon, where soybean area increased more than 10-fold, from 0.4 Mha to 4.6 Mha. Across the continent, 9% of forest loss was converted to soybean by 2016. Soy-driven deforestation was concentrated at the active frontiers, nearly half located in the Brazilian Cerrado. Efforts to limit future deforestation must consider how soybean expansion may drive deforestation indirectly by displacing pasture or other land uses. Holistic approaches that track land use across all commodities coupled with vegetation monitoring are required to maintain critical ecosystem services.

Fires are both a cause and consequence of important changes in the Amazon region. The development and implementation of better fire management practices and firefighting strategies are important steps to reduce the Amazon ecosystems’ degradation and carbon emissions from land-use change in the region. We extended the application of the maximum entropy method (MaxEnt) to model fire occurrence probability in the Brazilian Amazon on a monthly basis during the 2008 and 2010 fire seasons using fire detection data derived from satellite images. Predictor variables included climatic variables, inhabited and uninhabited protected areas and land-use change maps. Model fit was assessed using the area under the curve (AUC) value (threshold-independent analysis), binomial tests and model sensitivity and specificity (threshold-dependent analysis). Both threshold-independent (AUC = 0.919 ± 0.004) and threshold-dependent evaluation indicate satisfactory model performance. Pasture, annual deforestation and secondary vegetation are the most effective variables for predicting the distribution of the occurrence data. Our results show that MaxEnt may become an important tool to guide on-the-ground decisions on fire prevention actions and firefighting planning more effectively and thus to minimise forest degradation and carbon loss from forest fires in Amazonian ecosystems.