Detalhes bibliográficos
| Ano de defesa: |
2016 |
| Autor(a) principal: |
Wrublack, Suzana Costa
 |
| Orientador(a): |
Mercante, Erivelto
|
| Banca de defesa: |
Poleto, Cristiano
,
Bortoli, Marcelo
,
Coelho, Silvia Renata Machado
,
Prior, Maritane
|
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Estadual do Oeste do Parana
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação "Stricto Sensu" em Engenharia Agrícola
|
| Departamento: |
Engenharia
|
| País: |
BR
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://tede.unioeste.br:8080/tede/handle/tede/2716
|
Resumo: |
This research aimed to contribute to the monitoring of water quality using orbital remote sensing and GIS techniques, use and occupation of mapping land in Lontra river watershed, focusing on information to apply water resources management instruments. The first phase consisted on mapping the use and soil occupation and on evaluating quality of irrigation water used in Salto do Lontra municipality, in Paraná state, Brazil. SPOT-5 satellite images were used to carry out the supervised classification of the Maximum Likelihood algorithm ML. Water quality data were submitted to statistical analyses by the PCA and FA techniques, in order to identify the most relevant variables during the evaluation of irrigation water quality. The UCS characterization by maximum likelihood estimation allowed identifying the classes: agricultural crops, bare soil, forest and urban area. The PCA use concerning parameters of irrigation water quality explained 53.27% of variation in water quality according to the monitored points, represented by family-based farming. In a second phase, a variation of water quality was studied along Lontra river, with the support of Geographic Information Systems (GIS) integrated with multivariate statistical techniques to investigate the dependency relationships among variables responses associated with UCS. Mosaic images of 2014 from Google Earth were used to map such land use and occupation. Digital Elevation Model (DEM) and soil maps made up database, along with UCS categories, defined as explanatory variables. The definition of areas of influence by Thiessen polygon method and multivariate statistics techniques, especially the Redundancy Analysis (RDA), were used to investigate correlation among explanatory variables (land use and occupation, slope, soil types and monitoring points) in parameters such as water quality, defined as exploratory variables. Land use mapping and Linear Redundancy Analysis allowed the identification of anthropogenic pressures on water quality parameters, especially when compared to points located by upstream and downstream of Lontras s river watershed. Finally, an approach concerning the use of Geotechnologies on the study of environmental issues was carried out focusing the contribution of information to apply water resources management instruments. The UCS characterization, using SAM supervised classification and Landsat-8 image, defined five UCS categories that with different seasons and monitoring points (upstream and downstream watershed) investigated the correlation among these variables and water quality parameters. RDA identified positive correlation among dependent variables (electrical conductivity and total dissolved solids) and warmer seasons (fall, spring and summer). The highest answers of temperature and pH were positively related to land use especially in the categories of forest, water and pasture. Temporary crops and urban areas showed negative correlation to other UCS categories. The correlation of turbidity and reducing oxidation potential parameters, especially during the winter season. Geotechnologies used in this trial, especially represented by geoprocessing and GIS, have allowed the study of geographical space structure and environmental aspects. Multivariate statistical methods enabled the synthesis of data variability structure and identification of the most significant variables, especially to the seasons and different monitoring points along the Lontra river watershed. This research mainly focused on irrigated family farming, where subsidies have been raised to assist with management decision-making on water use and the development of actions in the application of available rational technologies, aiming at improving different water use systems. Remote sensing techniques combined with GIS have contributed to carry out studies concerning management of territories and, in particular, water resources management. |
