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| Cechim Junior,Clóvis; Johann,Jerry A.; Antunes,João F. G.. |
| ABSTRACT The knowledge on reliable estimates of areas under sugarcane cultivation is essential for the Brazilian agribusiness, since it helps in the development of public policies, in determining prices by sugar mills to producers and allows establishing the logistics of production disposal. The objective of this work was to develop a methodology for mapping the sugarcane crop area in the state of Paraná, Brazil, using images from the Landsat/TM/OLI and IRS/LISS-3 satellites, for the crop years from 2010/2011 to 2013/2014. The mappings were conducted through the supervised Maximum likelihood classification (Maxver) achieving, on average, an overall accuracy of 94.13% and kappa index of 0.82. The correlation with the official data of the IBGE ranged from... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Remote sensing; Digital image processing; Supervised classification; Maxver; Agricultural statistic. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662017000600427 |
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| Oldoni,Lucas V.; Cattani,Carlos E. V.; Mercante,Erivelto; Johann,Jerry A.; Antunes,João F. G.; Almeida,Luiz. |
| ABSTRACT In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Decision tree; Random forest; NDVI temporal metrics. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019001200952 |
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