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| Bispo,Rafael C.; Lamparelli,Rubens A. C.; Rocha,Jansle V.. |
| Coffee production was closely linked to the economic development of Brazil and, even today, coffee is an important product of the national agriculture. The State of Minas Gerais currently accounts for 52% of the whole coffee area in Brazil. Remote sensing data can provide information for monitoring and mapping of coffee crops, faster and cheaper than conventional methods. In this context, the objective of this study was to assess the effectiveness of coffee crop mapping in Monte Santo de Minas municipality, Minas Gerais State, Brazil, from fraction images derived from MODIS data, in both dry and rainy seasons. The Spectral Linear Mixing Model was used to derive fraction images of soil, coffee, and water/shade. These fraction images served as input data for... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Spectral linear mixing model; Supervised classification; Overall Accuracy; Kappa Index. |
| Ano: 2014 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000100012 |
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| Wacquet, Guillaume; Grosjean, Philippe; Colas, Florent; Hamad, Denis; Artigas, Luis Felipe. |
| The coupled system FlowCAM/ZooPhytoImage has become a real operational tool in 2014. However, to be fully adapted to the observations of phytoplankton performed in the context of the REPHY observation network and in order to better respond to present and future requests concerning the evaluation of quality of coastal and marine waters within the European requirements, such as the WFD and MSFD, new functionalities must be integrated into existing tools. Therefore, different axis of development have been proposed by UMONS and Ifremer to adapt both the digitization device and the Zoo/PhytoImage software to the constraints defined by the REPHY. First, version 5 of Zoo/PhytoImage contains recent innovations such as the development of routines to automatically... |
| Tipo: Text |
Palavras-chave: Plancton; Analyse automatisée; Analyse d'image; Classification supervisée; Apprentissage actif; Dénombrement de cellules; Plankton; Automated analysis; Image processing; Supervised classification; Active learning; Cells counting. |
| Ano: 2015 |
URL: http://archimer.ifremer.fr/doc/00389/49986/50573.pdf |
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| Gusmawati, Niken; Soulard, Benoit; Selmaoui-folcher, Nazha; Proisy, Christophe; Mustafa, Akhmad; Le Gendre, Romain; Laugier, Thierry; Lemonnier, Hugues. |
| From the 1980's, Indonesian shrimp production has continuously increased through a large expansion of cultured areas and an intensification of the production. As consequences of diseases and environmental degradations linked to this development, there are currently 250,000 ha of abandoned ponds in Indonesia. To implement effective procedure to undertake appropriate aquaculture ecosystem assessment and monitoring, an integrated indicator based on four criteria using very high spatial optical satellite images, has been developed to discriminate active from abandoned ponds. These criteria were: presence of water, aerator, feeding bridge and vegetation. This indicator has then been applied to the Perancak estuary, a production area in decline, to highlight the... |
| Tipo: Text |
Palavras-chave: Aquaculture; Abandoned ponds; VHSR images; Indicator; Supervised classification; Spatial and temporal survey. |
| Ano: 2018 |
URL: http://archimer.ifremer.fr/doc/00392/50343/51081.pdf |
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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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