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@Article{SantosFontSilvRudo:2014:IdSpTe,
               author = "Santos, Juliana Silveira dos and Fontana, Denise C. and Silva, 
                         Thiago S. F. and Rudorff, Bernardo Friedrich Theodor",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and CEPSRM, 
                         UFRGS and UNESP and {Agrosat{\'e}lite Geotecnologia Aplicada}",
                title = "Identification of the spatial and temporal dynamics for estimating 
                         soybean crop area from MODIS images in the Rio Grande do Sul, 
                         Brazil / Identifica{\c{c}}{\~a}o da din{\^a}mica 
                         espa{\c{c}}o-temporal para estimar {\'a}rea cultivada de soja a 
                         partir de imagens MODIS no Rio Grande do Sul",
              journal = "Revista Brasileira de Engenharia Agr{\'{\i}}cola e Ambiental",
                 year = "2014",
               volume = "18",
               number = "1",
                pages = "54--63",
                month = "Jan.",
             keywords = "imagens multitemporais, fenologia, previs{\~a}o de safras, 
                         sensoriamento remoto, multitemporal imagery, phenology, crop yield 
                         predictive, remote sensing.",
             abstract = "Com este trabalho prop{\~o}e-se definir um m{\'e}todo para 
                         estimar a {\'a}rea cultivada de soja na regi{\~a}o norte do Rio 
                         Grande do Sul. Foram propostos seis m{\'e}todos baseados no 
                         perfil espectro-temporal e de valores m{\'{\i}}nimos e 
                         m{\'a}ximos de imagens NDVI/MODIS referentes {\`a}s etapas de 
                         semeadura, m{\'a}ximo desenvolvimento e colheita das {\'a}reas 
                         de soja. As estimativas obtidas foram comparadas com dados 
                         oficiais do IBGE a partir de an{\'a}lises estat{\'{\i}}sticas e 
                         da an{\'a}lise espacial fuzzy. Os resultados indicaram que 
                         estimativas agr{\'{\i}}colas satisfat{\'o}rias s{\~a}o 
                         dependentes de caracter{\'{\i}}sticas como o tamanho, o tipo de 
                         manejo e a {\'e}poca de plantio e de colheita das lavouras. Para 
                         todos os m{\'e}todos avaliados foram obtidos valores de 
                         coeficientes de determina{\c{c}}{\~a}o e da an{\'a}lise fuzzy 
                         superiores a 0,8 e 0,45, respectivamente. O m{\'e}todo limiar 
                         emp{\'{\i}}rico aplicado {\`a} imagem diferen{\c{c}}a com 
                         inclus{\~a}o do final de ciclo, gerou estimativas iguais {\`a}s 
                         dos dados oficiais do IBGE, caracter{\'{\i}}stica que ressalta a 
                         utiliza{\c{c}}{\~a}o deste m{\'e}todo em programas operacionais 
                         de previs{\~a}o de safras. Para an{\'a}lises espaciais 
                         recomenda-se a aplica{\c{c}}{\~a}o do m{\'e}todo 
                         Classifica{\c{c}}{\~a}o de imagens multitemporais que gerou um 
                         mapa de melhor qualidade. A efici{\^e}ncia dos m{\'e}todos deve 
                         ser avaliada em {\'a}reas de expans{\~a}o de soja no Estado. 
                         ABSTRACT: The objective of this study was to define a method for 
                         estimating soybean crop area in the Northern Rio Grande do Sul 
                         state (Brazil). Overall, six different remote sensing methods were 
                         proposed based on spectral-temporal profile and minimum and 
                         maximum values of NDVI/MODIS related to the stages of sowing, 
                         maximum development and harvesting of soybean areas. The resulting 
                         estimates were compared to official crop area data provided by the 
                         Brazilian government, using statistical analysis and the fuzzy 
                         similarity method. The performance of each method depended on 
                         information such as crop size, type of crop management, and 
                         sowing/harvesting dates. Regression coefficients of determination 
                         and fuzzy agreement values were above 0.8 and 0.45, respectively, 
                         for all methods. For operational monitoring of soybean crop area, 
                         the empirical threshold applied to the image difference with 
                         inclusion of harvest image method was the most effective, 
                         producing estimates that matched closely the official data. For 
                         spatial analysis the application of multitemporal images 
                         classification method is recommended that generated a map of 
                         better quality. The efficiency of these methods should be 
                         evaluated in the areas of soybean expansion in the state.",
                  doi = "10.1590/S1415-43662014000100008",
                  url = "http://dx.doi.org/10.1590/S1415-43662014000100008",
                 issn = "1415-4366",
                label = "scopus 2014-05 SantosFontSilvRudo:2014:IdSpTe",
             language = "en",
           targetfile = "v18n1a08.pdf",
                  url = "http://dx.doi.org/10.1590/S1415-43662014000100008",
        urlaccessdate = "24 jan. 2021"
}


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