Definição de áreas prioritárias à recuperação florestal em bacias hidrográficas a partir de análise multicritério

• Ano de defesa: 2020
• Autor(a) principal: MAFRA, Renata Cristina
• Orientador(a): Ramos, Ana Paula Marques
• Banca de defesa: Ramos, Ana Paula Alves , Silva, Paulo Antônio da , Moryia, Érika A. S.
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade do Oeste Paulista
• Programa de Pós-Graduação: Mestrado em Meio Ambiente e Desenvolvimento Regional
• Departamento: Mestrado em Meio Ambiente e Desenvolvimento Regional
• País: Brasil
• Palavras-chave em Português: Inferência geográfica; Análise multicritério; Impacto ambiental; Recuperação florestal; Áreas prioritárias
• Palavras-chave em Inglês: Geographical inference; Multicriteria analysis; Environmental impact; Forest recuperation; Priority areas
• Área do conhecimento CNPq: OUTROS::CIENCIAS
• Link de acesso: http://bdtd.unoeste.br:8080/jspui/handle/jspui/1268

The master's thesis, presented in this document, was written in a model of scientific article. The final document of the dissertation was organized into three sections. The first section with the general contextualization of the research, promoted by the Coordination for the Improvement of Higher Education Personnel (CAPES) and developed in the Graduate Program in Environment and Regional Development (PPGMADRE) of the University of Western São Paulo (UNOESTE). The second section is composed of a manuscript, in which presents an approach of validation of vulnerability map to erosion elaborated by different methods of inference. The third section presents priority areas for forest recovery in hydrographic basins through Multicriteria Analysis in GIS environment. For the second section we adopted a hydrographic basin and considered the following criteria: geomorphology, pedology, slope, drainage density and land cover. Among the methods tested: Weighted Linear Combination (CLP) and three Fuzzy operators: algebraic sum, algebraic product and gamma, varying the exponent "γ" between the values 0.4; 0.6 and 0.8. The weights of the criteria were defined based on the Hierarchical Analytical Process. The validation of the maps occurred using 1902 points, of which 951 erosion points were in the area, defined based on images from Google Earth Pro, and 951 points without erosion, randomly generated in QGIS 3.8. The logistic regression model was used to compare the performance of each map by pointing out the areas with the highest and lowest degree of vulnerability. The best modeling was achieved with the Fuzzy gamma operator when parameterized with γ = 0.6. Although CLP is the recurrent approach in environmental studies involving geographic inference, our results show that other operators can produce results closer to those found with the reality observed in the field. For the third section we worked with relevant criteria for the determination of priority areas, such as: drainage network, distance from highways, distance from urban areas, fragments of vegetation, and vulnerability to erosion. The weights of each criterion were obtained from the Hierarchical Analytical Process (AHP). We tested two methods for creating the synthesis map: CLP (Weighted Linear Combination) and Fuzzy Gamma operator. As a result we obtained two scenarios; the first with the PLC method where we prioritized areas with vegetation fragments and high drainage density, and the second with the Gamma method, which prioritized vulnerable areas of the basin. We conclude that the proposed integration model satisfies the identification of areas for forest recovery in watersheds, and that different scenarios can be constructed