| dc.creator | Bispo R.C. | |
| dc.creator | Lamparelli R.A.C. | |
| dc.creator | Rocha J.V. | |
| dc.date | 2014 | |
| dc.date | 2015-06-25T18:01:29Z | |
| dc.date | 2015-11-26T15:03:18Z | |
| dc.date | 2015-06-25T18:01:29Z | |
| dc.date | 2015-11-26T15:03:18Z | |
| dc.date.accessioned | 2018-03-28T22:14:10Z | |
| dc.date.available | 2018-03-28T22:14:10Z | |
| dc.identifier | | |
| dc.identifier | Engenharia Agricola. Sociedade Brasileira De Engenharia Agricola, v. 34, n. 1, p. 102 - 111, 2014. | |
| dc.identifier | 1006916 | |
| dc.identifier | 10.1590/S0100-69162014000100012 | |
| dc.identifier | http://www.scopus.com/inward/record.url?eid=2-s2.0-84897079525&partnerID=40&md5=091d9e557f3ee6c156367111c284c36c | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/87593 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/87593 | |
| dc.identifier | 2-s2.0-84897079525 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1256582 | |
| dc.description | 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 the supervised automatic classification using the SVM-Support Vector Machine approach. The best results concerning Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively. | |
| dc.description | 34 | |
| dc.description | 1 | |
| dc.description | 102 | |
| dc.description | 111 | |
| dc.description | Aguiar, D.A., Monitoramento da área colhida de cana-de-açúcar por meio de imagens do sensor MODIS (2010), http://urlib.net/sid.inpe.br/mtc-m1780/2007/05.11.18.56, 99 f. Dissertação (Mestrado em Sensoriamento Remoto)-Istituto Nacional de Pesquisas Espaciais, São José Dos Campos, 2007. Disponível em: Acesso em: 23 agoAlves, G.M., Análise do modelo linear de mistura espectral aplicado a imagens MODIS para o mapeamento da cultura de arroz irrigado no estado do Rio Grande do Sul (2011), http://hdl.handle.net/10183/23937, 69 f. Dissertação (Mestrado em Sensoriamento Remoto) - Universidade Federal do Rio Grande do Sul, Porto Alegre, 2009. Disponível em: . Acesso em: 25 maio(2012), CONAB-Companhia Nacional de Abastecimento. Acompanhamento de safra brasileira: café, safra 2012. Quarta estimativa, dezembro/2012. Brasília/DF: ConabLandis, J.R., Koch, G.G., The measurement of observer agreement for categorical data (1977) Biometrics, Washington, 33 (1), pp. 159-174 | |
| dc.description | Melgani, F., Bruzzone, L., Classification of Hyperspectral Remote Sensing Images With Support Vector Machines (2004) IEEE Transactions on Geoscience and Remote Sensing, 42 (8), pp. 1778-1790. , New York | |
| dc.description | Moreira, M.A., Adami, M., Rudorff, B.F.T., Análise espectral e temporal da cultura do café em imagens Landsat (2004) Pesquisa Agropecuária Brasileira, Brasília/DF, 39 (3), pp. 223-231. , mar | |
| dc.description | Moreira, M.A., Rudorff, B.F.T., Barros, A.M., Faria, V.G.C., Adami, M., Geotecnologias para mapear lavouras de café nos estados de Minas Gerais e São Paulo (2010) Engenharia Agrícola, Jaboticabal, 30 (6), pp. 1123-1135 | |
| dc.description | Lamparelli, R.A.C., Johann, J.A., Santos, E.R., Esquerdo, J.C.D.M., Rocha, J.V., Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants (2012) Engenharia Agrícola, Jaboticabal, 32 (1), pp. 184-196 | |
| dc.description | Santos, J.A., Gosselin, P.-H., Phillip-Foliguet, S., Torres, R.S., Falcão, A.X., Multiscale Classification of Remote Sensing Images (2012) IEEE Transactions on Geoscience and Remote Sensing, 50, pp. 3764-3775. , New York | |
| dc.description | Senthilnath, J., Bajpai, S., Omkar, S.N., Diwakar, P.G., Mani, V., An approach to multi-temporal MODIS image analysis using image classification and segmentation (2012) Advances in Space Research, Oxford, 50, pp. 1274-1287 | |
| dc.description | Shao, Y., Lunetta, R.S., Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points (2012) ISPRS Journal of Photogrammetry and Remote Sensing, Amsterdam, 70, pp. 78-87 | |
| dc.description | Shimabukuro, Y.E., Smith, J.A., The least-squares mixing models to generate fraction images derived from remote sensíng multispectral data (1991) IEEE Transactions on Geoscience and Remote Sensing, 29 (1), pp. 16-20. , New York | |
| dc.description | Soares, J.V., Batista, G.T., Shimabukuro, Y.E., Histórico e Descrição (2007), pp. 3-21. , In: RUDORFF, B. F. T. | |
| dc.description | SHIMABUKURO, Y. E. | |
| dc.description | CEBALLOS, J. C. (Orgs.). O sensor MODIS e suas aplicações ambientais no Brasil. São José dos Campos: Parênteses . cap. 1 | |
| dc.language | en | |
| dc.publisher | Sociedade Brasileira de Engenharia Agricola | |
| dc.relation | Engenharia Agricola | |
| dc.rights | aberto | |
| dc.source | Scopus | |
| dc.title | Using Fraction Images Derived From Modis Data For Coffee Crop Mapping | |
| dc.type | Artículos de revistas | |
