136 resultados para Limnological variables


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Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

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Pós-graduação em Ciências Cartográficas - FCT

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Two drainage basins located in the mid-southern region of Paraná state were comparatively studied for analysis of limnological characteristics in lotic ecosystems. Ten segments of rivers and streams were evaluated in each basin, from June 4th through to June, 29th , 2007. The following physical and chemical parameters were measured: water temperature, specific conductance, oxygen saturation, pH, turbidity, current velocity and depth. The two drainage basins presented similar nominal values for all parameters investigated. There were significant differences between the two environments in relation to temperature, pH, and oxygen saturation. Cluster analysis revealed five small groups of samplings, each one with particular limnological characteristics. The Principal Components Analysis (PCA) confirmed the difference among the drainage basins. These results suggest an influence of regional and local factors in limnological characteristics of rivers and streams in the studied drainage basins.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)