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ABSTRACT:The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Paraná, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm -MAXVER, and the water quality parameters analyzed were pH, EC, HCO , turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality. KEYWORDS:Global Navigation Satellite Systems -GNSS, Geographic Information Systems -GIS, irrigation. , turbidez, temperatura e coliformes termotolerantes, em dois períodos anuais distintos de precipitação. Os dados de qualidade da água foram submetidos às análises estatísticas pelas técnicas de ACP e AF, para a identificação das variáveis mais relevantes na avaliação da qualidade da água de irrigação. A caracterização de uso e ocupação do solo pelo classificador MAXVER permitiu a identificação das classes: culturas agrícolas, solo exposto/resteva, mata e área urbana. A aplicação da técnica ACP dos dados de qualidade da água de irrigação explicou 53,27% da variação na qualidade da água entre os pontos amostrados. Verificou-se que os parâmetros nitrato, coliformes termotolerantes, temperatura, condutividade elétrica e bicarbonato foram os parâmetros que melhor explicaram a variação espacial da qualidade da água. MAPEAMENTO DE USO E OCUPAÇÃO DO SOLO E QUALIDADE

There are a number of crops that are viable for small farms. To increase our knowledge of the occurrence of canola cultivation in the State of Paraná, the present study aimed to identify the areas sown with canola (ha) and to examine canola production (t) during the growing seasons of 2005 to 2009. The methodology involved a literature review and analysis of spatial data from the Secretaria da Agricultura e do Abastecimento do Paraná (SEAB/PR). We used the software ArcGis 9.3 for elaboration of thematic maps of variables as well as the free software GeoDa 0.9.5-i. The identification of spatial dependence was conducted through global univariate spatial autocorrelation using Moran's Index (I) statistic and, at a local level, using Local Indicators of Spatial Association (LISA). Spatial correlation was detected in the data on the area sown (ha) and production (t) for the canola crop. The results also indicated the possibility of expansion of cultivated areas in the State of Paraná, in addition to providing interesting options regarding the composition of systems of crop rotation and soil cover. The application of the spatial statistical area contributed to the recognition of the evolution of the canola crop in the State of Paraná, based on the spatialization of production and sown areas in its municipalities. It is evident that most municipalities show no significant pattern, demonstrating the potential for the expansion of sown areas in the State. For the High-High standard, it was noted that the municipalities were grouped mainly in the western and south-central State of Paraná.

This study demonstrates using remote sensing, geographic information systems and multivariate statistics to study water quality in an agricultural watershed. The monitoring of water quality in the watershed of Lontras's river in the southwestern region of the State of Paraná had been done in 2012 and 2013 with a multi-parameter probe in ten sites that were defined upstream and downstream watershed, during four different seasons. Mosaicked images were used from Google Earth, Digital Elevation Model and soil types of maps, defined as the explanatory variables. The definition of the areas of influence and multivariate statistical techniques, particularly the Redundancy Analysis (RDA), were used for the correlation between variables. In a spring area, located upstream watershed, the contribution on water quality variation has gotten smaller, when compared with the other monitoring sites. There was interference in water quality in downstream sites that has become greater due to the effects of diffuse pollution. The RDA enabled synthesizes the data variability structure and the relationship of multidimensional variables. These statistical techniques added to products resulting from the GIS contributed to a better understanding of the variation of water quality in the watershed.