Amazon rainforest deforestation influenced by clandestine and regular roadway network. (September 2021)

• Record Type: Journal Article
• Authors: das Neves, Patricia Bittencourt Tavares; Blanco, Claudio José Cavalcante; Montenegro Duarte, André Augusto Azevedo; das Neves, Filipe Bittencourt Souza; das Neves, Isabela Bittencourt Souza; de Paula dos Santos, Marcelo Henrique
• Is Part Of: Land use policy. Volume 108(2021)
• Publication Date: 2021-09
• Subjects: Amazon -- Local development -- Machine learning -- Random forest -- Decision tree; Land use -- Periodicals; Land use -- Government policy -- Periodicals; Sol, Utilisation du -- Périodiques; Sol, Utilisation du -- Politique gouvernementale -- Périodiques; Electronic journals; 333.7305
• DOI: 10.1016/j.landusepol.2021.105510
• Languages: English
• ISSNs: 0264-8377

Abstract

Abstract: Since 1950, the Amazonian and Brazilian transportation complex has prioritized the model of road transport. This model does not adequately consider the singularities of each site, especially the potential for waterway transport in the Amazon. This fact probably contributed to the unsustainable local development, even though it implemented various large projects of integration, hydroelectric, mineral and agroindustry. Past studies point out that the regular roadway system that is integrated into a clandestine roadway complex is strongly related to Amazon forest deforestation. Thus, the objectives of this work are 1) investigate the influence of clandestine and regular roadway network on the deforestation of the Amazon forest and 2) to develop an approach to deforestation forecast, utilizing a time series of 30 years (1988–2018). We used machine learning in the modeling of the quantitative variables related to the transportation infrastructure, social variables and economic variables, e.g., the deforested area. The geographical study area is the state of Pará, located in the Oriental Amazon, the second largest state of Brazil in territorial extension and the most deforested. The random forest model presented the best performance with a mean absolute error (MAE) of 2534.06 and a standard deviation (STD) of 2347.67 km 2, pointing to a strong relationship and showing a very strong tendency. We used sensitivity analysis to evaluate the effects of regular roadway network and clandestine roadway network on deforestation. With the generated function (using the least squares method), the deforested area was estimated for the years 2020, 2030, 2040 and 2050. The results show that given the same scenario, deforestation tends to continue intensively in the next three decades. The total loss is more than 72, 417.93 km 2 (25.77% increase compared to the current deforested area). The results show an increasing curve, although with decreasing rates of forest loss in ten years, on average from 7.80% to 0.80% per year. Highlights: Influence of clandestine roadway network on the deforestation of the Amazon. Clandestine roadways are used for the illegal exploration of natural resources. Variables considered: roadway infrastructure, social, environment and economy. Approach to deforestation forecast.