Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts

Abstract

As Amazon deforestation rates reach the highest levels observed in the past decade, it is extremely important to direct conservation efforts to regions containing preserved forests with a high risk of deforestation. This requires forecasting deforestation, a complex endeavor due to the interplay of multiple socioeconomic, political, and environmental factors across different spatial and temporal scales. Here we couple high-resolution land-cover maps with Bayesian hierarchical spatial models to identify the main drivers of recent deforestation in the Brazilian Amazon and predict which areas are likely to lose a larger proportion of forest in the next 3 years. Recent deforestation was positively associated with forest edge density, the length of roads and waterways, elevation and terrain slope; and negatively associated with distance to urban areas, roads, and indigenous lands, area designated as protected or indigenous territory, and municipality GDP per capita. From these variables, forest edge density and distance to roads showed the largest effect sizes and highest predictive power. Predictive accuracy was highest for shorter time windows and larger grid sizes. Predicted deforestation was largely concentrated in the North-Eastern portions of the Brazilian Amazon, and amounted to roughly 3, 5, and 6 million hectares for 2020, 2021, and 2022, respectively. About 50% of this predicted deforestation is expected to occur inside protected areas or indigenous lands. Our short-term forecasts can help plan preventive measures to limit deforestation while meeting the specific needs of local areas.

Keywords

• Amazon deforestation
• Bayesian hierarchical spatial models
• INLA-SPDE
• predictive modeling
• protected areas