Object-based image analysis supported by data mining to discriminate large areas of soybean |
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| Authors: | Carlos Antonio da Silva Junior Marcos Rafael Nanni José Francisco de Oliveira-Júnior Everson Cezar Paulo Eduardo Teodoro Rafael Coll Delgado |
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| Institution: | 1. Geotechnology Applied in Agriculture and Forest (GAAF), State University of Mato Grosso (UNEMAT), Alta Floresta, Mato Grosso, Brazilcarlosjr@unemat.br;3. Department of Agronomy (DAG/PGA), State University of Maringá (UEM), Maringá, Paraná, Brazil;4. Institute of Atmospheric Sciences (ICAT), Federal University of Alagoas (UFAL), Maceió, Alagoas, Brazil;5. Federal University of Mato Grosso do Sul (UFMS), Chapad?o do Sul, Mato Grosso do Sul, Brazil;6. Department of Environmental Sciences (DCA), Forestry Institute (IF), Federal Rural University of Rio de Janeiro (UFRRJ), Seropédica, Rio de Janeiro, Brazil |
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| Abstract: | This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. Two major techniques were used: GEOgraphic-Object-Based Image Analysis (GEOBIA) and Data Mining (DM). In order to obtain the images, the segmentation algorithm implemented by Definiens Developer was used. A decision tree (DT) was created from a training set previously prepared. Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle. The time-series data were used only for the CEI index. Furthermore, to compare the results obtained from GEOBIA, the slicing technique was used at the CEI level. After the training, the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean. In accordance with the error matrix and kappa parameter analysis, tests for statistical significance were created. Results indicate that the classification achieved by Kappa coefficients is 0.76. In short, the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale. |
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| Keywords: | GEOBIA time-series vegetation indices MODIS vegetation dynamics |
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