Predictive Modelling of Contagious Deforestation in the Brazilian Amazon

Abstract

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.

Multiple-scale prediction of forest loss risk across Borneo

“…in developing countries it is important to understand the dynamics of land-use change and predict which areas are at highest risk of forest loss. Multiple studies have attempted to quantify and predict future deforestation, most notably in the Amazon basin (Soares-Filho et al 2006;Rosa et al 2013), and in other forests around the world (Rideout et al 2013;Vieilledent et al 2013); however, to date we know of no analyses that predict future forest loss across the full extent of Borneo.…”

“…While many recent studies of land-use change allude to spatial scales, they typically do so implicitly by including distance to various landscape features (roads, rivers, population center, etc.) as a metric (e.g., (Rideout et al 2013;Rosa et al 2013;Vieilledent et al 2013), which is not a true multi-scale analysis (sensu McGarigal et al 2016). To our knowledge there have been no multi-scale optimization efforts applied to landscape change modeling.…”

“…To our knowledge there have been no multi-scale optimization efforts applied to landscape change modeling. Additionally, while several analyses consider some element of landscape composition (e.g., Rosa et al 2013;Vieilledent et al 2013;Rideout et al 2013), we are aware of no studies that have considered both the composition and configuration of the multi-scale neighbourhood around a point as a predictor variable.…”

Scenarios of land-use change in a deforestation corridor in the Brazilian Amazon: combining two scales of analysis

“…For the Brazilian Amazon, a number of scenarios have been published (Aguiar et al 2016;Assis et al 2011;Lapola et al 2010Lapola et al , 2011Laurance et al 2001;Maeda et al 2011;Moreira 2009;Oliveira et al 2013;Rosa et al 2013Rosa et al , 2014Soares-Filho et al 2001, 2004Verburg et al 2014;Wassenaar et al 2007). Dalla-Nora et al (2014) critically assessed key elements of the different scenarios and realized that most scenario models failed to capture the amount of deforestation over recent decades.…”

Epidemiologically inspired approaches to land-use policy evaluation: The influence of the Rural Environmental Registry (CAR) on deforestation in the Brazilian Amazon

“…how much forest had been spared in a given period due to the creation of new protected areas). For example, cellular automata (Soares-Filho et al, 2006;Vega Orozco et al, 2012), econometric (Soares-Filho et al, 2010;Arima et al, 2014) and probabilistic bottom-up models (Rosa et al, 2013;Godar et al, 2014) have been used to establish counterfactual analysis and, consequently, to measure the overall effect of environmental policies in reducing deforestation and greenhouse gas emissions.…”