ABSTRACT Many studies have determined the concentration of trace elements in river sediments in Brazil. Notwithstanding, mercury assessments are scarce, especially because of exclusive extraction techniques and expensive analysis techniques. Still, this element is known for its toxicity, persistence, and bioaccumulation, making its presence in the environment an important factor for biota and human health. For this reason, the objective of this study was to determine the mercury concentration in the sediment of the Pelotas River basin, located on the border of the states of Santa Catarina and Rio Grande do Sul. The sediment was collected at eight locations of the Pelotas basin and, after drying, the mercury was quantified by atomic absorption spectrometry based on the Zeeman-background correction, coupled to a pyrolysis reactor. The mercury concentrations in the sediments of the Pelotas River varied from 40.5 ng g-1 to 62.0 ng g-1 and presented a positive correlation with the fraction of silt and clay. The concentrations of mercury found in sediments of the Pelotas River basin have a low probability of negatively affecting the biota. Nonetheless, given the persistence and bioaccumulation potential of this element, the aforementioned region needs further studies to quantify the risks it may cause on the local biota and human health.

ABSTRACT 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.

ABSTRACT: The traditional per-pixel classification methods consider only spectral information, and may be limited. Object-based classifiers, however, also consider shape and texture, firstly segmenting the image, and then classifying individual objects. Thus, a Geographic Object-Based Image Analysis (GEOBIA) was compared in conjunction with data mining techniques and a traditional per-pixel method. A cut of Landsat-8, bands 2 to 7, orbit/point 223/77, located between the municipalities of Cascavel, Corbélia, Cafelândia and Tupãssi, in the west part of the state of Paraná, from 12/18/2013 was used. In the GEOBIA approach was realized image segmentation, spatial and spectral attribute extraction, and classification using the decision tree supervised algorithm, J48. For the per-pixel method, we used the supervised Maximum Likelihood Classifier. Both approaches presented equivalent results, with Kappa Index of 0.75 and Global Accuracy (GA) of 78.97% for the approach by GEOBIA and Kappa Index of 0.72 and GA of 77.44% for the perpixel classification. The classification by GEOBIA showed better accuracy for the soil, forest and soybean classes, and did not show the splash aspect, which visually improves the classification result.

No presente trabalho, realizou-se a análise da distribuição espacial de área dos valores de produção municipal para a cultura da soja, no Estado do Paraná. Os dados dos sete anos-safras, entre 2003/2004 e 2009/2010, foram obtidos da Secretaria da Agricultura e do Abastecimento do Paraná (SEAB). Elaborou-se um Boxmap para cada ano-safra, com o intuito de demonstrar como ocorreu a alternância dos valores de produção de soja neste intervalo de tempo. O Índice de Moran foi utilizado para apresentar os níveis de autocorrelação espacial a nível global entre os municípios e o índice de autocorrelação LISA, para estudar a autocorrelação espacial a nível local, com diferentes matrizes de contiguidade e com diferentes ordens de contiguidade, assim como o estudo dos níveis de significância. Através destes métodos, foi possível demonstrar como os municípios se relacionam espacialmente quanto à produção da soja, o que permitiu a indicação de clusters de alta-baixa produção, possibilitando assim que se conheçam os principais municípios produtores de soja, subsidiando os membros da cadeia produtiva com informações para melhorar seus planos no fortalecimento da cultura no Paraná.

In the current study, we performed a soybean production spatial distribution analysis in Paraná State. Seven crop-year data, from 2003-04 to 2009-10, obtained from the Paraná Department of Agriculture and Supply (SEAB) were used to develop a Boxmap for each crop-year, show soybean production throughout this time interval. Moran's index was used to measure spatial autocorrelation among municipalities at an aggregate level, while LISA index local correlation. For each index, different contiguity matrix and order were used and there was a significance level study. As a result, we have showed spatial relationship among cities regarding the production, which allowed the indication of high and low production clusters. Finally, identifying main soybean-producing cities, what may provide supply chain members with information to strengthen the crop production in Paraná.

C.H.W Souza, E. Mercante, V.H.R. Prudente and D.D.D. Justina. 2013. Methods of performance evaluation for the supervised classification of satellite imagery in determining land cover classes. Cien. Inv. Agr. 40(2): 419-428. Satellite imagery, in combination with remote sensing techniques, provides a new opportunity for monitoring and assessing crops with lower cost and greater objectivity than traditional surveys. The present research employed Landsat 5/TM satellite imagery to identify the land cover classes in Cafelândia (Paraná, Brasil), a predominantly agricultural town. Five supervised classification methods (parallelepiped (PL), minimum distance (MND), Mahalanobis distance (MHD), maximum likelihood classifier (MLC) and spectral angle mapper (SAM)) were tested in this work. To assess the efficiency of the classifications, accuracy indices and error metrics obtained through total confusion matrices were used. The results indicated that the Mahalanobis and SAM methods generated the smallest errors for the four studied land use classes (soybean, corn, forest, and bare soil), with overall accuracy values of 88% and 86%, respectively, and kappa index values 0.83 and 0.80, respectively. The values of these methods for the applied metrics were 0.88 and 0.86 for the sensitivity index, 0.96 and 0.95 for the total specificity index and 0.84 and 0.81 for Matthews correlation coefficient, respectively. The different classification methods clearly exhibited large variations in their performance for land cover mapping. The use of measures obtained from the error matrix is a suitable method for comparisons of thematic maps.

C.H.W. Souza, E. Mercante, V.H.R. Prudente y D.D.D. Justina. 2013. Métodos de evaluación de desempeño para la clasificación supervisada de imágenes satelitales en la determinación de las clases de cobertura terrestre. Cien. Inv. Agr. 40(2): 419-428. Imágenes satelitales junto con técnicas de sensoriarniento remoto ofrecen nuevas oportunidades para el monitoreo y evaluación de cultivos, con menores costos y una mayor objetividad en relación a los métodos tradicionales. La presente investigación utilizó imágenes de satélite Landsat 5/TM para identificar las clases de cobertura del suelo en Cafelândia (Paraná, Brasil), un municipio predominantemente agrícola. Cinco métodos de clasificación supervisada: Paralelepípedo (PL), Distancia mínima (MND), Distancia de Mahalanobis (MHD), Máximo probabilidad (MLC) y Mapeador de Ángulo Espectral (SAM) fueron probados en este trabajo. Con el fin de evaluar la eficiencia de las clasificaciones, se utilizaron índices y métricas de precisión obtenidos a través de matrices de errores y confusión total. Los resultados indicaron que los clasificadores de distancia de Mahalanobis y SAM superaron a los demás, consiguiendo los mejores resultados y proporcionando los menores errores para las cuatro clases estudiadas (soja, maíz, bosque, suelo desnudo), con valores de precisión global de 88 y 86%, respectivamente, y valores de índice de Kappa de 0,83 y 0,80, respectivamente. Para las métricas aplicadas, los valores fueron 0,88 y 0,86 para el índice de sensibilidad, 0,96 y 0,95 para el índice de especificidad y de 0,84 y 0,81 para el coeficiente de correlación de Matthews, respectivamente. La elección de los diferentes tipos de clasificadores claramente resultó en grandes variaciones en el desempeño del mapeamiento de la cobertura y uso de la tierra. El uso de las medidas obtenidas a partir de la matriz de errores es un método adecuado para hacer comparaciones entre mapas temáticos.