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| Registros recuperados : 167 | |
| Registros recuperados : 167 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
22/11/2019 |
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Data da última atualização: |
22/11/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
OLDONI, L. V.; CATTANI, C. E. V.; MERCANTE, E.; JOHANN, J. A.; ANTUNES, J. F. G.; ALMEIDA, L. |
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Afiliação: |
LUCAS VOLOCHEN OLDONI, INPE; CARLOS EDUARDO VIZZOTTO CATTANI, Unioeste; ERIVELTO MERCANTE, Unioeste; JERRY ADRIANI JOHANN, Unioeste; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LUIZ ALMEIDA, INPE. |
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Título: |
Annual cropland mapping using data mining and OLI Landsat-8. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 23, n. 12, p. 952-958, 2019. |
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DOI: |
http://dx.doi.org/10.1590/1807-1929/agriambi.v23n12p952-958 |
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Idioma: |
Inglês |
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Conteúdo: |
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 compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC). At first, seven classes were mapped (water bodies, sugarcane, urban area, annual crops, forest, pasture and reforestation areas); then, only two classes were considered (annual crops and other targets). When classifying the seven targets, both methods had corresponding results, showing global accuracy near 84%. NDVI temporal metrics showed producer?s and user?s accuracy for the annual crop class of 86 and 100%, respectively. However, if considering only two classes, the NDVI temporal metrics reached global accuracy of near 98% and producer?s and user?s accuracy above 94%. |
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Palavras-Chave: |
Árvore de decisão; Data mining; Decision tree; Métricas temporais de NDVI; Mineração de dados; NDVI temporal metrics; Random forest; Séries temporais. |
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Thesaurus NAL: |
Normalized difference vegetation index; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/205238/1/AP-Annual-cropland.pdf
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Marc: |
LEADER 02446naa a2200313 a 4500
001 2114915
005 2019-11-22
008 2019 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1590/1807-1929/agriambi.v23n12p952-958$2DOI
100 1 $aOLDONI, L. V.
245 $aAnnual cropland mapping using data mining and OLI Landsat-8.$h[electronic resource]
260 $c2019
520 $aABSTRACT: 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 compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC). At first, seven classes were mapped (water bodies, sugarcane, urban area, annual crops, forest, pasture and reforestation areas); then, only two classes were considered (annual crops and other targets). When classifying the seven targets, both methods had corresponding results, showing global accuracy near 84%. NDVI temporal metrics showed producer?s and user?s accuracy for the annual crop class of 86 and 100%, respectively. However, if considering only two classes, the NDVI temporal metrics reached global accuracy of near 98% and producer?s and user?s accuracy above 94%.
650 $aNormalized difference vegetation index
650 $aTime series analysis
653 $aÁrvore de decisão
653 $aData mining
653 $aDecision tree
653 $aMétricas temporais de NDVI
653 $aMineração de dados
653 $aNDVI temporal metrics
653 $aRandom forest
653 $aSéries temporais
700 1 $aCATTANI, C. E. V.
700 1 $aMERCANTE, E.
700 1 $aJOHANN, J. A.
700 1 $aANTUNES, J. F. G.
700 1 $aALMEIDA, L.
773 $tRevista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande$gv. 23, n. 12, p. 952-958, 2019.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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