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@MastersThesis{Crusco:2006:SeReAn,
               author = "Crusco, Nat{\'a}lia de Almeida",
                title = "Sensoriamento remoto para an{\'a}lise multitemporal da 
                         din{\^a}mica de {\'a}reas agr{\'{\i}}colas",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2006",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2006-06-19",
             keywords = "projeto geosafras, an{\'a}lise multitemporal, estimativa de 
                         {\'a}rea agr{\'{\i}}cola, estat{\'{\i}}stica 
                         agr{\'{\i}}cola, auditoria, geosafras project, remote sensing, 
                         multitemporal analysis, crop area estimative, agriculture 
                         statistics, audit.",
             abstract = "Estat{\'{\i}}sticas agr{\'{\i}}colas s{\~a}o importantes em 
                         um pa{\'{\i}}s como o Brasil, onde a agricultura desempenha um 
                         papel fundamental na economia brasileira. As metodologias 
                         atualmente utilizadas s{\~a}o baseadas em dados subjetivos, e 
                         apresentam um car{\'a}ter n{\~a}o probabil{\'{\i}}stico. No 
                         intuito de aprimorar os resultados obtidos, o projeto Geosafras 
                         utiliza dados de sensoriamento remoto associado a dados de campo 
                         na gera{\c{c}}{\~a}o de estimativa de {\'a}reas 
                         agr{\'{\i}}colas para as principais culturas existentes no 
                         pa{\'{\i}}s. Por{\'e}m, este m{\'e}todo apresenta algumas 
                         limita{\c{c}}{\~o}es quanto {\`a} valida{\c{c}}{\~a}o dos 
                         dados provenientes do campo. Neste sentido, este trabalho tem como 
                         hip{\'o}tese central a exist{\^e}ncia de rela{\c{c}}{\~a}o 
                         entre os dados coletados em campo no presente e 
                         informa{\c{c}}{\~o}es de uso do solo em tempos passados. Assim, 
                         o principal objetivo {\'e} avaliar como a din{\^a}mica de 
                         {\'a}reas agr{\'{\i}}colas, pela abordagem multitemporal, pode 
                         auxiliar o processo de previs{\~a}o, auditoria e 
                         valida{\c{c}}{\~a}o de dados de campo. O estudo de imagens 
                         multitemporais possibilitou a avalia{\c{c}}{\~a}o da 
                         din{\^a}mica de {\'a}reas agr{\'{\i}}colas e do padr{\~a}o de 
                         uso do solo na {\'a}rea de estudo. Foram utilizadas 24 imagens 
                         dos sensores TM/Landsat-5 e ETM+/Landsat-7 no per{\'{\i}}odo de 
                         2002 a 2005. As classes avaliadas neste trabalho - soja, 
                         cana-de-a{\c{c}}{\'u}car, pasto e mata - foram bem discriminadas 
                         visual e espectralmente. A an{\'a}lise da din{\^a}mica temporal 
                         mostrou que cada classe possui padr{\~o}es distintos, que 
                         est{\~a}o associados tamb{\'e}m ao calend{\'a}rio 
                         agr{\'{\i}}cola da regi{\~a}o. A metodologia empregada neste 
                         trabalho foi eficiente tanto na realiza{\c{c}}{\~a}o da 
                         previs{\~a}o de uso do solo, como na indica{\c{c}}{\~a}o dos 
                         pontos a serem auditados no painel amostral do projeto Geosafras, 
                         apontando os erros que podem ser cometidos em campo e depurados 
                         pela utiliza{\c{c}}{\~a}o das imagens de sat{\'e}lite. 
                         ABSTRACT: Agricultural statistics are important in a country like 
                         Brazil, where agriculture plays an important role over the 
                         economy. The methodologies for crop area estimates are commonly 
                         based on subjective data, and they present a non-probabilistic 
                         profile. In order to increase the quality of the results, the 
                         Geosafras Project uses remote sensing data associated to field 
                         data for estimating the area of agricultural crops for the main 
                         crop types existing in the country. However, this method presents 
                         some limitations regarding the field data validation. This work 
                         tackled this aspect and has as central hypothesis the existence of 
                         relation between the field data colleted in the present and 
                         information about the land-use in the past. Thus, the main 
                         objective is to evaluate how the agricultural crop land use 
                         dynamics, evaluated here by remote sensing multitemporal analysis, 
                         can assist the early estimation process, auditing and field data 
                         validation. The analysis of multitemporal images showed that it 
                         was possible the validation of the agricultural land dynamics and 
                         the land-use patterns in the study area. In order to accomplish 
                         the study, 24 TM/Landsat-5 and ETM+/Landsat-7 images in the 
                         time-frame from 2002 to 2005 were used. The crop land use classes 
                         evaluated in this work soybean, sugarcane, grassland and forest 
                         were well distinguished visually and spectrally. The analysis of 
                         the temporal dynamics showed that each class has a distinct 
                         pattern, which is also associated to the agricultural 
                         schedule/calendar of the region. The methodology used in this work 
                         was efficient for the land-use prediction, as well as for the 
                         indication of the plotted points to be audited in the sample panel 
                         of the Geosafras Project. Also, it was possible to identify the 
                         errors that can be committed during field sampling and corrected 
                         them by using multitemporal satellite images.",
            committee = "Formaggio, Antonio Roberto (presidente) and Epiphanio, Jos{\'e} 
                         Carlos Neves (orientador) and Kuplich, Tatiana Mora and Luiz, 
                         Alfredo Jos{\'e} Barreto and Xavier, Alexandre C{\^a}ndido",
           copyholder = "SID/SCD",
         englishtitle = "Remote sensing for multitemporal analysis of crop land use 
                         dynamics",
             language = "pt",
                pages = "107",
                  ibi = "6qtX3pFwXQZGivnJSY/Mg6TV",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZGivnJSY/Mg6TV",
           targetfile = "publicacao.pdf",
        urlaccessdate = "09 maio 2024"
}


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