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Título: | Análise multicritério na definição de áreas prioritárias para recuperação florestal na bacia do Rio Doce, em Minas Gerais |
Título(s) alternativo(s): | Multricriteria analysis to define priority areas for forest recovery in the Rio Doce basin, Minas Gerais |
Palavras-chave: | Manejo de ecossistemas Combinação linear ponderada Processo analítico hierárquico Ecossystem management Linear weighted combination Analytical hierarchical process |
Data do documento: | 2020 |
Editor: | Universidade Federal de Mato Grosso |
Citação: | ALMEIDA, F. C. de et al. Análise multicritério na definição de áreas prioritárias para recuperação florestal na bacia do Rio Doce, em Minas Gerais. Pesquisas Agrárias e Ambientais, Sinop, v. 8, n. 1, p. 81-90, jan./fev. 2020. DOI: http://dx.doi.org/10.31413/nativa.v8i1.8130. |
Resumo: | The Brazilian Atlantic forest is one of the most fragmented ecosystems and exploited Brazilian biome. As restoration activities are expensive, multicriteria decision analysis (MCDA) integrated with GIS (geographic information system) provide a satisfactory spatial decision support system to efficiently produce maps. The collapse of a mining dam in a region of Brazilian Atlantic forest, resulted in the destruction of communities by a river of mud and mining waste. Thus, the objective of this study was to map and identify priority areas for forest recover in the Rio Doce Basin, Minas Gerais. We used GIS-based multicriteria decision analysis associated with the analytic hierarchy process (AHP) and weighted linear combination (WLC) method in the aggregation of criteria. Five factors were used, receiving different weights: distance from the drainage network, distance from the native vegetation patches, slope, soil class and precipitation. According to the priority areas map, 92.69% of the area was classified as an area of low or very low importance for forest recovery and the remained (2.92%) of the Rio Doce basin was mapped as an area with high and very high priority for forest recovery. The ADMC is easy to implement, producing maps that can predict the right solutions to conduct recovery actions, provided the database is trusted for satisfactory results. |
URI: | http://dx.doi.org/10.31413/nativa.v8i1.8130 http://repositorio.ufla.br/jspui/handle/1/42645 |
Aparece nas coleções: | DCS - Artigos publicados em periódicos |
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