@MastersThesis{Jesus:2009:AnVeMu,
               author = "Jesus, Silvia Cristina de",
                title = "An{\'a}lise por vetor de mudan{\c{c}}as para a 
                         avalia{\c{c}}{\~a}o multitemporal e multissensores da cobertura 
                         das terras do cerrado",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2009",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2009-08-27",
             keywords = "an{\'a}lise por vetor de mudan{\c{c}}as, detec{\c{c}}{\~a}o de 
                         mudan{\c{c}}as, cerrado, multissensores, an{\'a}lise 
                         multitemporal, change vector analysis, change detection, cerrado, 
                         multisensro, multipemporal analysis.",
             abstract = "O objetivo deste trabalho {\'e} analisar a evolu{\c{c}}{\~a}o 
                         temporal (33 anos) de expans{\~a}o agr{\'{\i}}cola usando a 
                         t{\'e}cnica de An{\'a}lise por Componentes Principais (ACP) para 
                         a gera{\c{c}}{\~a}o de componentes de brilho e verdor, aplicada 
                         a dados de m{\'u}ltiplos sensores com distintas 
                         caracter{\'{\i}}sticas espectrais e de resolu{\c{c}}{\~a}o 
                         espacial. A partir dessas componentes, a An{\'a}lise por Vetores 
                         de Mudan{\c{c}}as (AVM) pode, ent{\~a}o, fornecer 
                         informa{\c{c}}{\~o}es sobre a intensidade e o tipo de 
                         mudan{\c{c}}a ocorrida. Utilizaram-se imagens orbitais 
                         MSS/Landsat, TM/Landsat e CCD/CBERS, adquiridas entre 1975 e 2008. 
                         O coeficiente Kappa variou de 0,18 a 0,41, indicando que a 
                         An{\'a}lise por Vetores de Mudan{\c{c}}as exibe 
                         concord{\^a}ncia fraca ou regular em rela{\c{c}}{\~a}o {\`a} 
                         interpreta{\c{c}}{\~a}o visual. Considerando um n{\'{\i}}vel 
                         de signific{\^a}ncia de p=0,05, verificou-se que o resultado da 
                         AVM {\'e} superior a uma classifica{\c{c}}{\~a}o 
                         aleat{\'o}ria. De modo geral, os erros se devem a confus{\~o}es 
                         espectrais associadas {\`a} cobertura do solo natural ou 
                         antr{\'o}pica, tal como campo sujo e pasto, al{\'e}m de 
                         incrementos na biomassa vegetal, que podem se referir {\`a} 
                         regenera{\c{c}}{\~a}o florestal ou desenvolvimento de culturas 
                         agr{\'{\i}}colas. A AVM mostrou-se {\'u}til na 
                         detec{\c{c}}{\~a}o de mudan{\c{c}}as no sentido de permitir o 
                         uso de m{\'u}ltiplos par{\^a}metros e a an{\'a}lise de suas 
                         varia{\c{c}}{\~o}es ao longo do tempo. Como dados de entrada, as 
                         Componentes Principais mostraram-se meios diretos e r{\'a}pidos 
                         para a gera{\c{c}}{\~a}o de informa{\c{c}}{\~o}es de brilho e 
                         verdor de uma determinada cena. As componentes principais foram 
                         vi{\'a}veis na an{\'a}lise da varia{\c{c}}{\~a}o desses 
                         par{\^a}metros. ABSTRACT: The main objective was to study the 
                         multitemporal expansion of agriculture for 33 years using there 
                         different satellites/sensors, by applying Principal Components 
                         Analysis techniques in order to generate the components of 
                         brightness and greenness for each dataset. The use of these 
                         components for the Change Vector Analysis can thus provide 
                         information on the intensity and type of change occurred. We used 
                         MSS/Landsat, TM/Landsat and CCD/CBERS, acquired between 1975 and 
                         2008. The Kappa coefficients ranged from 0.18 to 0.41, indicating 
                         that the change of Vector Analysis had slight or fair agreement 
                         with visual analysis. Assuming a significance level of 0.05, it 
                         was verified that the result of analysis by Changes Vectors 
                         Analysis is better than a random classification. In general, the 
                         errors are due to spectral confusion associated with natural or 
                         anthropogenic land use, such as campo limpo and grazing, and 
                         increases in plant biomass, which may refer to forest regeneration 
                         or development of agricultural crops. Change Vector Analysis was 
                         useful for detecting changes and it accepts the use of different 
                         parameters, and considers its variation over time. As input data, 
                         the principal components are direct and rapid means for generating 
                         information of brightness and greenness of a particular scene. The 
                         principal components are feasible in studies involving the 
                         analysis of the variation of these parameters.",
            committee = "Formaggio, Antonio Roberto (presidente) and Epiphanio, Jos{\'e} 
                         Carlos Neves (orientador) and Alves, Di{\'o}genes Salas and 
                         Filho, M{\'a}rio Val{\'e}rio",
           copyholder = "SID/SCD",
         englishtitle = "Change vector analysis for multitemporal and multisensor 
                         assessment of Cerrado land cover",
             language = "pt",
                pages = "97",
                  ibi = "8JMKD3MGP8W/369KNAB",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/369KNAB",
        urlaccessdate = "28 abr. 2024"
}