@Article{EpiphanioForRudMaeLui:2010:EsSoCr,
               author = "Epiphanio, Rui Dalla Valle and Formaggio, Antonio Roberto and 
                         Rudorff, Bernardo Friedrich Theodor and Maeda, Eduardo Eiji and 
                         Luiz, Alfredo Jos{\'e} Barreto",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and University 
                         of Helsinki, Department of Geosciences and Geography, Gustaf 
                         H{\"a}llstr{\"o}min katu 2, Kumpula, FI-00014, Helsinki, Finland 
                         and Embrapa Meio Ambiente, Caixa Postal 69, CEP 13820-000 
                         Jaguari{\'u}na, SP, Brazil",
                title = "Estimating soybean crop areas using spectral-temporal surfaces 
                         derived from MODIS images in Mato Grosso, Brazil/Estimativa de 
                         {\'a}reas de soja usando superf{\'{\i}}cies espectro-temporais 
                         derivadas de imagens MODIS em Mato Grosso, Brasil",
              journal = "Pesquisa Agropecu{\'a}ria Brasileira",
                 year = "2010",
               volume = "45",
               number = "1",
                pages = "72--80",
                month = "Jan.",
                 note = "Scopus and {CAB Abstracts} and AGRIS and {DOAJ Directory of Open 
                         Access Journals Free}",
             keywords = "Glycine max, accuracy, agricultural statistics, Classification, 
                         Remote Sensing, thematic map, Glycine max, acur{\'a}cia, 
                         estat{\'{\i}}sticas agr{\'{\i}}colas, 
                         classifica{\c{c}}{\~a}o, sensoriamento remoto, mapa 
                         tem{\'a}tico.",
             abstract = "The objective of this work was to evaluate the application of the 
                         spectral-temporal response surface (STRS) classification method on 
                         Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) 
                         sensor images in order to estimate soybean areas in Mato Grosso 
                         state, Brazil. The classification was carried out using the 
                         maximum likelihood algorithm (MLA) adapted to the STRS method. 
                         Thirty segments of 30x30 km were chosen along the main 
                         agricultural regions of Mato Grosso state, using data from the 
                         summer season of 2005/2006 (from October to March), and were 
                         mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 
                         images. Five thematic classes were considered: Soybean, Forest, 
                         Cerrado, Pasture and Bare Soil. The classification by the STRS 
                         method was done over an area intersected with a subset of 30x30-km 
                         segments. In regions with soybean predominance, STRS 
                         classification overestimated in 21.31% of the reference values. In 
                         regions where soybean fields were less prevalent, the classifier 
                         overestimated 132.37% in the acreage of the reference. The overall 
                         classification accuracy was 80%. MODIS sensor images and the STRS 
                         algorithm showed to be promising for the classification of soybean 
                         areas in regions with the predominance of large farms. However, 
                         the results for fragmented areas and smaller farms were less 
                         efficient, overestimating soybean areas. RESUMO O objetivo deste 
                         trabalho foi avaliar a aplica{\c{c}}{\~a}o do m{\'e}todo de 
                         classifica{\c{c}}{\~a}o por superf{\'{\i}}cies de resposta 
                         espectro-temporal (STRS) em imagens do sensor Moderate Resolution 
                         Imaging Spectroradiometer (MODIS, 250 m) para estimar {\'a}reas 
                         de plantio de soja no Estado de Mato Grosso, Brasil. A 
                         classifica{\c{c}}{\~a}o foi realizada usando o algoritmo de 
                         m{\'a}xima verossimilhan{\c{c}}a (MLA) adaptado ao algoritmo 
                         STRS. Trinta segmentos de 30x30 km foram escolhidos ao longo das 
                         principais regi{\~o}es agr{\'{\i}}colas do estado, com dados da 
                         safra de ver{\~a}o de 2005/2006 (outubro a mar{\c{c}}o), e 
                         mapeados com base em dados de campo e de imagens orbitais 
                         TM/Landsat-5 e CCD/CBERS-2. Cinco classes tem{\'a}ticas foram 
                         consideradas: Soja, Floresta, Cerrado, Pastagem e Solos Expostos. 
                         A classifica{\c{c}}{\~a}o pelo m{\'e}todo das STRS foi feita 
                         com base em uma {\'a}rea interseccionada por um subconjunto de 
                         segmentos de 30x30 km. O STRS superestimou os valores de 
                         refer{\^e}ncia em 21,31% em regi{\~o}es com predom{\'{\i}}nio 
                         da cultura da soja e em 132,37% em regi{\~o}es nas quais a soja 
                         era menos predominante. A exatid{\~a}o global da 
                         classifica{\c{c}}{\~a}o foi de 80%. As imagens MODIS e o 
                         algoritmo STRS mostraram-se promissores para a 
                         classifica{\c{c}}{\~a}o da soja em regi{\~o}es com 
                         predomin{\^a}ncia de grandes fazendas. Entretanto, os resultados 
                         para {\'a}reas fragmentadas em fazendas menores foram menos 
                         eficientes, superestimando as {\'a}reas de soja.",
                  doi = "10.1590/S0100-204X2010000100010",
                  url = "http://dx.doi.org/10.1590/S0100-204X2010000100010",
                 issn = "0100-204X",
                label = "lattes: 7514918598084999 3 
                         EpiphanioForRudMaeLui:2010:Es{\'A}rSo",
             language = "pt",
           targetfile = "a10v45n1.pdf",
                  url = "http://webnotes.sct.embrapa.br/pab/pab.nsf/FrAnual",
        urlaccessdate = "15 jun. 2024"
}