Mapping of use and occupation of the soil and irrigation water quality in the city of Salto do Lontra-Paraná, Brazil

Abstract

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

Variation of Water Quality Along a River in Agricultural Watershed With Support of Geographic Information Systems and Multivariate Analysis

“…Considering the Thiessen polygons, six of them have most of their area occupied by grazing lands, while other three have the highest intended occupancy of crops (temporary culture), and the polygon relative to the sampling spot 3, defined as the municipality entry/exit is mostly covered by woods. According to this classification the land use is predominantly agricultural (Wrublack et al, 2013a).…”

“…The techniques of remote sensing and geoprocessing can be applied in mapping the use and occupation of soil, providing support to work with systematic information and tools that provide detailed reconnaissance of the study area (Wrublack et al, 2013a). Spaceborne sensors and GIS techniques provide useful information such as land use and occupancy (Wrublack et al, 2013b), also watersheds delimitation, valuable for evaluation of environmental impacts and its source.…”

“…Assuming that the water quality in the watershed is impaired by a diffuse source of pollution (not allowing to determine the exact location of the source), it was necessary the use the Thiessen Polygons technique to determine the influence of the contaminating agents around each sampling spots. Being indicated when there is no uniform distribution of spots (Pearson, 2007;Wrublack et al, 2013a). The polygons were created based on the ten sampling spots within the watershed.…”

Mapping of the current land use in part of the Irrigated Perimeter Nilo Coelho, Petrolina-PE, Brazil

“…Utilizing the reflectances obtained from the hyperspectral bands for the strips: 3, 4, 5, 6 e 7 of the Landsat 8 sensors, the image was classified through the pixel supervised method, employing the maximum likelihood algorithm (Maxver) (Tisot et al, 2007;Leite et al, 2011;Antunes et al, 2012;Fernandes et al, 2012;Mello et al, 2012;Prishchepov et al, 2012;Wrublack et al, 2013), considering 11 classes of predefined land features, with an average of ten samplings for feature training, and ten more samplings for the validation of the proposed model in each class. proposed classes: grape crop "Grape", mango crop "Mango", coconut crop "Coconut", banana crop "Banana", guava crop "Guava", acerola crop "Acerola", other crops "Others", areas with exposed soil "Solo_exp", urban areas "Area_urb", areas with natural vegetation "Veg_ nat" and water bodies "Water".…”

Cobertura florestal e qualidade da água na bacia hidrográfica do rio Jucu, estado do Espírito Santo, Brasil

“…A classificação do uso e ocupação do solo foi determinada por meio da identificação dos alvos de vegetação presentes empregando-se, para tanto, a classificação supervisionada da imagem via máxima verossimilhança (MAXVER), o que permite que o classificador para associar um pixel particular, a uma determinada classe ou regiões de semelhança entre os níveis de cor, conforme também realizado por Wrublack et al [20]. Este método é mais eficiente porque as classes de treinamento são utilizadas para estimar a forma da distribuição dos pixels contidos em uma classe no espaço de n bandas.…”