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Deforestation drivers in the Brazilian Amazon: assessing new spatial predictors.
Santos, Alex Mota Dos; Silva, Carlos Fabricio Assunção da; Almeida Junior, Pedro Monteiro de; Rudke, Anderson Paulo; Melo, Silas Nogueira de.
Afiliação
  • Santos AMD; Center of Agroforestry Sciences and Technologies, Federal University of Southern Bahia, Rodovia Ilhéus/Itabuna, Km 22, Itabuna, 45604-811, Brazil. Electronic address: alex.geotecnologias@gmail.com.
  • Silva CFAD; Department of Cartographic and Survey Engineering, Center of Technologies and Geosciences, Federal University of Pernambuco, UFPE, Avenida Acadêmico Hélio Ramos, Cidade Universitária, s/n, Recife, 50740-530, Brazil. Electronic address: carlosufpe26@gmail.com.
  • Almeida Junior PM; Department of Statistics, Center of Nature and Exact Sciences, Federal University of Pernambuco, UFPE, Avenida Professor Moraes Rego, 1235, Cidade Universitária, 50670-901, Recife, Pernambuco, Brazil. Electronic address: pedroallmeiida@gmail.com.
  • Rudke AP; Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Pres. Antônio Carlos, 6627, 31270-901, Belo Horizonte, Brazil; Federal University of Technology - Paraná, Av. Dos Pioneiros, 3131, 86036-370, Londrina, Brazil. Electronic address: rudke@alunos.utfpr.edu.br.
  • Melo SN; Department of History and Geography, State University of Maranhão, Cidade Universitária Paulo VI, São Luís, 65055-000, Brazil. Electronic address: silasmelo@professor.uema.br.
J Environ Manage ; 294: 113020, 2021 Sep 15.
Article em En | MEDLINE | ID: mdl-34126530
Researches on the deforestation of the Amazon have gained prominence in the last recent years, mainly with the change in the policy regarding the facing of this phenomenon by the Brazilian government. Therefore, an understanding about the causes that pressure the occurrence of deforestation remains relevant and has a leading role in the world. Therefore, the aim of this study is to perform the analysis of the spatial variability of the reasons for the deforestation in the Amazon Biome, in Brazil, (2010-2019). To achieve this goal, 14 variables were selected, the choice and adjustment of the regression model were determined and a diagnosis was carried out in order to verify the most appropriate model. To achieve this purpose, a geographic database was structured in a geographic information system environment. The main results revealed that the adjusted R2 of the Geographically Weighted Regression (GWR) was 0.96, that is, the GWR model explains 96% of the variations in deforestation. Therefore, it was observed a significant gain when using this model. In addition, it was also observed that the average variable of the number of oxen was, among those analyzed, the one that showed the highest correlation with deforestation. Thus, it was found that the livestock sector in southern Amazonia is the main economic agent that pressures large areas of deforestation, since stockfarming is practiced extensively. Finally, it was concluded that the municipalities with the largest areas of deforestation formed a cluster in the southern portion of the Amazon, in the arc of deforestation.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ecossistema / Conservação dos Recursos Naturais Tipo de estudo: Prognostic_studies / Risk_factors_studies País como assunto: America do sul / Brasil Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ecossistema / Conservação dos Recursos Naturais Tipo de estudo: Prognostic_studies / Risk_factors_studies País como assunto: America do sul / Brasil Idioma: En Ano de publicação: 2021 Tipo de documento: Article