Data/hora: 25/04/2024 06:33:27

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	27/02/2013	
Data da última atualização: 	12/09/2013	
Autoria: 	ARRAES, C. L.; LAMPARELLI, R. A. C.; ROCHA, J. V.; ESQUERDO, J. C. D. M.; SALVADOR, P.; RODRÍGUES, J.; ROQUE, J.-L. C.; JUSTO, J. S.; BENATTI, B. G.	
Afiliação: 	CHRISTIANO L. ARRAES, Unicamp; RUBENS A. C. LAMPARELLI, Unicamp; JANSLE V. ROCHA, Unicamp; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; PABLO SALVADOR, University of Valladolid; JAVIER RODRÍGUEZ, University of Valladolid; JOSÉ-LUIS CASANOVA ROQUE, University of Valladolid; JULIA SANZ JUSTO, University of Valladolid; BEATRIZ G. BENATTI, IMECC/Unicamp.	
Título: 	Reliability of summer crop masks derived from second order polynomial equations.	
Ano de publicação: 	2013	
Fonte/Imprenta: 	Journal of Agricultural Science, Toronto, v. 5, n. 3, p. 63-75, 2013.	
Idioma: 	Inglês	
Conteúdo: 	Remote Sensing techniques are useful to determine planted areas, especially of commodities like maize and soybeans (summer cultures). The vegetation indexes as NDVI (Normalized Difference Vegetation Index) has been used to map agricultural areas due to its low cost and accessibility. Therefore the objective of this study was to develop a new methodology to generate masks of summer crops based on fitting second order polynomial equations to temporal NDVI profiles. The results showed that selecting polynomial equations with r2 fitting above 0.75, a Kappa index of 0.86 and a global accuracy of 93% is obtained. This is slightly higher than the results obtained when using the maximum-minimum NDVI technique, with a Kappa index of 0.82 and a global accuracy of 91%. However, while quantifying the areas under study, one verified that the mask used by the proposed methodology, was closer to the official IBGE data, with a difference of -10.25%, followed by the vector technique with 23% and by the maximum-minimum NDVI, with a difference of 42.8%.	
Palavras-Chave: 	Índice de vegetação.	
Thesagro: 	Milho; Sensoriamento Remoto; Soja.	
Thesaurus Nal: 	Corn; Remote sensing; Soybeans; Vegetation.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/77471/1/22216-82104-1-PB.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (17289-1) - UPC - AP - DD -  -  -  - DIS