Agricultural expansion is the main driver of deforestation in the Brazilian Amazon. It causes substantial losses of a unique biodiversity and severe emissions of greenhouse gases. In addition, post-deforestation land use often uses agricultural fires that frequently cause severe degradation in adjacent forests. In the past, national and international efforts reduced deforestation in the Brazilian Amazon and aimed on the prevention of agricultural fires. However, since 2016 the political developments in Brazil lowered environmental regulations and law enforcement (de Area Leão Pereira et al. 2020). Increasing rates of deforestation and fires have been reported and its intensity attracted the attention of international media. Nevertheless, it often remains unclear how different stakeholders contribute to the observed changes in deforestation and burning. In particular for fragmented landscape with smallholder agriculture the spatial resolutions of many fire- or burned area products is too coarse to differentiate between fires used for deforestation and those used for agricultural land management.
To better address this knowledge gap, we assessed the occurrence of deforestation and burned areas in Pará state. Specifically, the agricultural frontier of the Novo Progresso region is known for its high deforestation rates and raised media attention in 2019, when agricultural producers promoted a “Day of Fire” in response to president Bolsonaro’s call to use the Amazon more economically (Caetano 2021).
We used the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE, Frantz 2019) to process an integrated time series of all Landsat and Sentinel-2 observations between January 2014 and October 2020. We derived clear observation sequences (COS), classified the COS with a Random Forest and aggregated class probabilities into annual COS scores (Jakimow et al. 2018) that were then translated into annual maps of burned area and land-cover. Based on these maps we assessed the average treatment effect of the last three Brazilian presidencies (2011 – 2016 Dilma Rousseff, 2016-2018 Michel Temer, since 2019 Jair Bolsonaro) on deforestation and burning. We used propensity score weighting to account for confounding variables like the slope and distance to road and then fitted and compared deforestation and burning between different land use zones and four size classes of rural properties.
Our results showed a four-fold increase of deforestation from 2017 to 2020, i.e. after the impeachment of President Rousseff. Conservation areas showed significantly less deforestation than non-designated areas and agrarian settlements. Alarmingly this effect has been significantly lowered during the presidencies of Temer and even more so Bolsonaro. Strikingly, deforestation rates per property increased in particular on larger properties.
The burned area mapped was largest in 2017 (4,343 km²) and 2020 (4,805 km²), but the majority (> 80 %) of it was mapped on land that had been already deforested. The relative fraction that was burned compared to the individual property sizes was highest in agrarian settlement projects and smallholder properties. More broadly, our approach shows the suitability of combining maps of land-use changes and burned areas with medium to high spatial resolution with quasi-experimental methods for improving our understanding of how political changes influence the land-use of different agricultural stakeholders.
Caetano, M.A.L. (2021). Political activity in social media induces forest fires in the Brazilian Amazon. Technological Forecasting and Social Change, 167
de Area Leão Pereira, E.J., de Santana Ribeiro, L.C., da Silva Freitas, L.F., & de Barros Pereira, H.B. (2020). Brazilian policy and agribusiness damage the Amazon rainforest. Land Use Policy, 92
Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11
Jakimow, B., Griffiths, P., van der Linden, S., & Hostert, P. (2018). Mapping pasture management in the Brazilian Amazon from dense Landsat time series. Remote Sensing of Environment, 205, 453-468