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Articles by Aline de Carvalho Gasparotto
Total Records ( 3 ) for Aline de Carvalho Gasparotto
  Marcos Rafael Nanni , Jose Alexandre Melo Dematte , Carlos Antonio da Silva Junior , Franciele Romagnoli , Anderson Antonio da Silva , Everson Cezar and Aline de Carvalho Gasparotto
  The objective of this study was to evaluate the use of a laboratory and an orbital sensor on the classification of soils in a complete 180 ha bare soil area located in Brazil. The 180 points were located as a regular grid of 100x100 m (one point per ha). In each point, soil samples were georreferenced (GPS) and collected (0-20 and 80-100 cm depths). Based on the traditional soil analysis and field work (with 18 profile evaluation), a detailed soil map was developed as to be our “real pattern”. This soil map determined 18 soil classes and 53 polygons. Other soil maps were developed based on the following described methods. The first method was based on the orbital image (landsat) interpretation. It was used a color composition 5R, 4G, 3B. Based on the visual interpretation it was determined 16 classes of soils and 35 polygons. A statistical method was used to compare the traditional soil map with the color composition soil map. The traditional soil map was more accurate although the color composition had several. The second method was performed with laboratory sensor information. Spectral data (400-2500 nm) was acquired from soil samples (both depths of each point). Data was modelized and cluster analysis grouped the spectral curves. The third method consisted on the evaluation of the surface soil information (obtained in laboratory but convoluted for the landsat spectral bands). With this method 9 groups were discriminated. The fourth method was determined by quantitative analyses of each pixel information extracted from a processed and reflectance transformed landsat image. The number of groups determined were nine. The main conclusion was: Any sensor method reached the detailed soil map information.
  Carlos Antonio da Silva Junior , Marcos Rafael Nanni , Everson Cezar , Aline de Carvalho Gasparotto , Anderson Antonio da Silva , Guilherme Fernando Capristo Silva , Cassiele Uliana Facco and Josė Alexandre M. Demattė
  The monitoring of the Earth's surface and the dynamics of its vegetation using remote sensing techniques stands out in agricultural activities. The objective of this study was to estimate and map areas cultivated with soybean [Glycine max (L.) Merr.] by means of mono and time-series MODIS images in Paraná state through principal component techniques. For this mapping were used vegetation index (EVI and CEI) with the help of as time-series from images of MODIS sensor also was performed by supervised classification algorithms and partially unsupervised with use of principal component analysis. For statistical evaluation parameters were used Kappa and overall accuracy and their respective Z and t-tests. When analyzing the data obtained by the methods used in the estimates of soybean areas it appears that the ratings by the CEI index was highlighted with higher Kappa parameters (κ) and Overall Accuracy (OA), unlike the classifier K-means. For the principal component used five images including vegetation indices, presented to the Kappa 0.48 parameter. The mapping, discrimination and quantification of soybean fields in the state of Paraná was possible with the use of classifiers and MODIS images, which the systematization presented results of Kappa parameters and overall accuracy satisfactory.
  Aline de Carvalho Gasparotto , Marcos Rafael Nanni , Carlos Antonio da Silva Junior , Everson Cesar , Franciele Romagnoli , Anderson Antonio da Silva and Gabriel Cipolaro Guirado
  This study aimed to evaluate the use of vegetation indices, GNIR and RNIR extracted from digital images and using a spectroradiometer in the adjustment models for discrimination nitrogen levels in corn, evaluate the contents and their relationship with corn production components. The experiment was established in a greenhouse with a corn plant per pot in DIC with 5 treatments (0, 50, 100, 200 and 300 kg ha-1 of nitrogen) and 10 repetitions. Evaluations were performed at 15, 30, 45, 60 and 80 DAE, with capturing images using the 720 nm-IR filters, 850 nm-IR and UV-IR cut in a digital camera Fujifilm IS Pro and spectroradiometer readings were used the Spring and Excel software to calculate GNIR and RNIR vegetation indices. The GNIR showed higher sensitivity for assessment of nitrogen deficiency in maize. The use of camera appeared as a promising tool for nitrogen discrimination. The use 720 nm filter was higher to 850 nm. The best times to break down the nitrogen doses evaluated by vegetation indices were 60 and 80 DAE.
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