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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
09/03/2022 |
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Data da última atualização: |
15/03/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SILVA, J. L. de S.; ARAUJO, R. A. de; ESTEVES, V. P. P.; LOEBMANN, D. G. dos S. W.; VICENTE, L. E.; PASCHOAL. J. P.; MORGADO, C. do R. V. |
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Afiliação: |
JULI LAGE DE SOUZA SILVA, UFRJ; RAÍSSA ANDRÉ DE ARAUJO, UFRJ; VICTOR PAULO PEÇANHA ESTEVES, UFRJ; DANIEL GOMES DOS SANTOS W LOEBMANN, CNPMA; LUIZ EDUARDO VICENTE, CNPMA; JONILTON PANTOJA PASCHOAL, Hydro Paragominas; CLÁUDIA DO ROSÁRIO VAZ MORGADO, UFRJ. |
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Título: |
Analysis of vegetation recovery in areas impacted by bauxite mining in the Amazon Forest. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Clean Technologies and Environmental Policy, v. 23, n. 5, p. 1617-1640, July 2021. |
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DOI: |
https://doi.org/10.1007/s10098-021-02052-9 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Mining activity is essential for the Brazilian economy, representing 21% of exports in 2018, but it causes several environmental impacts, including deforestation. To minimize these impacts, replanting techniques are applied for environmental recovery and regulatory compliance. In this context, this study aims to monitor vegetation recovery in decommissioned bauxite mining areas located in the Amazon rainforest. The case study was carried out in the municipality of Paragominas (state of Pará). The monitoring used the series of a newly defined spectral index, called Biomass Composite Index (BCI), and the Enhanced Vegetation Index (EVI), both from Landsat images between 1986 and 2017. The analysis was complemented by interpretation of false-color images and by in situ photographs. The method was applied for periods before mining, during exploitation, and after decommissioning, when the traditional planting technique was implemented. BCI demonstrated greater sensitivity to forest disturbance in areas affected by forest degradation and less influence of canopy homogeneity where the forest was substituted by more open formations. The height homogeneity of recovered areas also affected the EVI data more than the BCI data, since these areas presented higher values of EVI, and some areas had index values close to those detected before mine operation. The analysis could have benefited from sampling intensification, with more images analyzed per year, to overcome the presence of clouds, which block the acquisition of surface data by the sensor. Nevertheless, the method proved to be very promising and can improve the evaluation of forest recomposition even in remote areas, besides facilitating large-scale monitoring of recovered forests. MenosAbstract: Mining activity is essential for the Brazilian economy, representing 21% of exports in 2018, but it causes several environmental impacts, including deforestation. To minimize these impacts, replanting techniques are applied for environmental recovery and regulatory compliance. In this context, this study aims to monitor vegetation recovery in decommissioned bauxite mining areas located in the Amazon rainforest. The case study was carried out in the municipality of Paragominas (state of Pará). The monitoring used the series of a newly defined spectral index, called Biomass Composite Index (BCI), and the Enhanced Vegetation Index (EVI), both from Landsat images between 1986 and 2017. The analysis was complemented by interpretation of false-color images and by in situ photographs. The method was applied for periods before mining, during exploitation, and after decommissioning, when the traditional planting technique was implemented. BCI demonstrated greater sensitivity to forest disturbance in areas affected by forest degradation and less influence of canopy homogeneity where the forest was substituted by more open formations. The height homogeneity of recovered areas also affected the EVI data more than the BCI data, since these areas presented higher values of EVI, and some areas had index values close to those detected before mine operation. The analysis could have benefited from sampling intensification, with more images analyzed per year, to overcome the presence... Mostrar Tudo |
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Palavras-Chave: |
Bauxite mining; Recovery of impacted areas. |
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Thesagro: |
Cobertura Vegetal; Floresta Tropical Úmida; Impacto Ambiental; Mineração; Sensoriamento Remoto. |
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Thesaurus Nal: |
Bauxite; Environmental impact; Environmental monitoring; Mining; Remote sensing; Vegetation cover; Vegetation index. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02932naa a2200373 a 4500
001 2140683
005 2022-03-15
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s10098-021-02052-9$2DOI
100 1 $aSILVA, J. L. de S.
245 $aAnalysis of vegetation recovery in areas impacted by bauxite mining in the Amazon Forest.$h[electronic resource]
260 $c2021
520 $aAbstract: Mining activity is essential for the Brazilian economy, representing 21% of exports in 2018, but it causes several environmental impacts, including deforestation. To minimize these impacts, replanting techniques are applied for environmental recovery and regulatory compliance. In this context, this study aims to monitor vegetation recovery in decommissioned bauxite mining areas located in the Amazon rainforest. The case study was carried out in the municipality of Paragominas (state of Pará). The monitoring used the series of a newly defined spectral index, called Biomass Composite Index (BCI), and the Enhanced Vegetation Index (EVI), both from Landsat images between 1986 and 2017. The analysis was complemented by interpretation of false-color images and by in situ photographs. The method was applied for periods before mining, during exploitation, and after decommissioning, when the traditional planting technique was implemented. BCI demonstrated greater sensitivity to forest disturbance in areas affected by forest degradation and less influence of canopy homogeneity where the forest was substituted by more open formations. The height homogeneity of recovered areas also affected the EVI data more than the BCI data, since these areas presented higher values of EVI, and some areas had index values close to those detected before mine operation. The analysis could have benefited from sampling intensification, with more images analyzed per year, to overcome the presence of clouds, which block the acquisition of surface data by the sensor. Nevertheless, the method proved to be very promising and can improve the evaluation of forest recomposition even in remote areas, besides facilitating large-scale monitoring of recovered forests.
650 $aBauxite
650 $aEnvironmental impact
650 $aEnvironmental monitoring
650 $aMining
650 $aRemote sensing
650 $aVegetation cover
650 $aVegetation index
650 $aCobertura Vegetal
650 $aFloresta Tropical Úmida
650 $aImpacto Ambiental
650 $aMineração
650 $aSensoriamento Remoto
653 $aBauxite mining
653 $aRecovery of impacted areas
700 1 $aARAUJO, R. A. de
700 1 $aESTEVES, V. P. P.
700 1 $aLOEBMANN, D. G. dos S. W.
700 1 $aVICENTE, L. E.
700 1 $aPASCHOAL. J. P.
700 1 $aMORGADO, C. do R. V.
773 $tClean Technologies and Environmental Policy$gv. 23, n. 5, p. 1617-1640, July 2021.
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