Disclosing contrasting scenarios for future land cover in Brazil: Results from a high-resolution spatiotemporal model

Sci Total Environ. 2020 Nov 10;742:140477. doi: 10.1016/j.scitotenv.2020.140477. Epub 2020 Jun 27.


Gaining information on the dynamics of land cover changes is a valuable step towards improving practical conservation actions. In recent years, the Brazilian presidential elections in 2018 and the recovery from one of the nation's worst economic recessions defined a political scenario that has been causing shifts in the patterns of land cover change. A variety of national plans for the near-future exist and include the construction of new roads connecting remote Amazonian areas and large dams that could flood up to 10 million hectares. These development plans threaten environmental conservation, but the potential effects on the local or regional land cover are mostly unknown. In this work, we construct a model to evaluate the possible consequences of policy actions on land cover dynamics in the near-future at a high-resolution scale. The regression model extracts the historical relationships between land cover and spatial drivers of change, and its extrapolation for the future enables the simulation of scenarios for the national plans currently discussed in Brazil. We also simulate three scenarios based on the Representative Concentration Pathways of the Intergovernmental Panel on Climate Change, which makes contrasting management assumptions. The resulting maps indicate that considerable changes in land cover composition and configuration may occur even in a short period. The historical Brazilian economic forces make the decrease in natural vegetation probabilities challenging to stop even in an environmentally oriented scenario, where plans for the construction of new infrastructure are abruptly interrupted. Our results also indicate that environmental degradation cannot be prevented without coordinated efforts between public agencies with a broad diversity of development viewpoints.

Keywords: Forecast; Integrated modeling; Land system science; Land-use modeling.