Análise multitemporal de uso e cobertura da terra nas planícies costeiras do baixo Jaguaribe e de Camocim no estado do Ceará com fins de determinação de mudanças e relações com a instalação de parques eólicos.

• Ano de defesa: 2020
• Autor(a) principal: Durán, Gloria Yamileth Bolívar
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Detecção de mudanças; Uso e cobertura; Sensoriamento remoto; Parques eólicos
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/56967

The coastal plains of Baixo Jaguaribe in the municipality of Aracati, on the east coast, and the coastal plain of Camocim on the west coast in the municipality of the same name, in the state of Ceará, are geographical spaces, with their peculiarities and differences, with respect to geomorphology, physiography of the coastline, land use, occupation and development of shrimp farming. As of 2008, there has been a change in the field of mobile dunes, with the construction of wind farms to generate electricity. The objective of this research was to analyze the changes in land use and land cover in the coastal plains of Baixo Jaguaribe and Camocim located in the state of Ceará over a period of 33 years, using techniques for detecting multitemporal changes in active and passive sensor images to establishing the relationship with the changes produced by the installation of wind farms. The detailed knowledge of the study areas with the delimitation of geoenvironmental units and flows of matter and energy, was very important to reduce the uncertainties in the images classification and change detection. The methods for change detection were applied according to the type of sensor. In the multitemporal analysis, orbital images images on dates related to the dry period were chosen, from the LANDSAT 5-TM, Landsat 7-ETM + and LANDSAT 8-OLI sensors between the years 1985 to 2018, and were applied post classification comparison method and the transition or change matrix. With the radar sensor ALOS PALSAR, was used images from 2008 to 2011, using incoherent changes detection method. To change detection in the dune fields, spectral mixture analysis was used to determine the displacement in the dune field. In the case of optical images, the post classification comparison method showed the changes for 11 land-use/land-cover classes established; while the change matrix analysis showed relationships beyond the comparison of net gains and net losses, showed the change relationships from proportion, intensity, transition and hierarchy in the study period between 1985 - 2018. With the ALOS PALSAR images it was possible to discriminate the change areas and non-change areas, based on the increment and decrease of backscatter value, taking advantage the active sensor's sensitivity to topographic surface, roughness and soil water content, revealing changes features in the coastal plain and in the construction process of the wind farm in the mobile dunes field. It was established that land use – land cover changes identified on the coastal plain as a whole have no relation with the deployment of wind farms over the mobile dune field. Both in Aracati and Camocim Highlighted the most intense transitions between the shrub, deciduous shrub and cultivation classes, the transitions from wetlands and mangroves to shrimp farming, as well as the urban lands expansion over the dune field in Aracati. The spectral mixture analysis in the mobile dunes fields, showed two behaviors in the areas closest to the wind farm: in Aracati a significant change was not identified in terms of reduction in the displacement of the dunes, but in Camocim the reduction in displacement was evident and it seems sustained over time. The implantation of the wind turbines effectively caused morphological transformations in the dune field, as well as the permanent modification of interdunal lagoons. In view of these results, specific studies are recommended to help determine the relationship between the reduction in the displacement of the dunes in the study areas and the interference of other variables, such as seasonal and/or climatic events beyond the wind farm. Change detection should be applied before the construction of wind farms to better identify the subsequent impact and after for results avaliation.