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| Silva,Alessandra F.; Barbosa,Ana Paula; Zimback,Célia R. L.; Landim,Paulo M. B.. |
| This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Indicator Kriging; Cluster; Maxver; CBERS-2B satellite; Spatial classification. |
| Ano: 2013 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162013000600017 |
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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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