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@MastersThesis{Diniz:2019:AvPoDa,
               author = "Diniz, Juliana Maria Ferreira de Souza",
                title = "Avalia{\c{c}}{\~a}o do potencial dos dados polarim{\'e}tricos 
                         Sentinel-1A para mapeamento do uso e cobertura da terra na 
                         regi{\~a}o de Ariquemes - RO",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2019",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2019-02-15",
             keywords = "aprendizagem de m{\'a}quina, simula{\c{c}}{\~a}o de Monte 
                         Carlo, radares de abertura sint{\'e}tica, banda C, Floresta 
                         Amaz{\^o}nica, machine learning, Monte Carlo simulation, 
                         synthetic aperture radars (SAR), C band, Amazon Forest.",
             abstract = "O processo de ocupa{\c{c}}{\~a}o humana vem ocorrendo de forma 
                         cada vez mais acentuada na Amaz{\^o}nia Legal, devido 
                         principalmente a expans{\~a}o da fronteira agr{\'{\i}}cola. 
                         Entender o processo de mudan{\c{c}}a no uso e cobertura da terra 
                         {\'e} fundamental para o planejamento e gerenciamento dos 
                         recursos naturais. Atrav{\'e}s das t{\'e}cnicas de sensoriamento 
                         remoto {\'e} poss{\'{\i}}vel mapear o uso e cobertura da terra 
                         de forma r{\'a}pida e eficaz. A utiliza{\c{c}}{\~a}o de dados 
                         de radares de abertura sint{\'e}tica (SAR) vem se mostrando uma 
                         alternativa para o monitoramento e mapeamento em regi{\~o}es 
                         tropicais, principalmente por serem pouco influenciados pela 
                         cobertura de nuvens. Nesse sentido, o objetivo principal desta 
                         disserta{\c{c}}{\~a}o foi avaliar o potencial do uso das imagens 
                         dual VV-VH do sat{\'e}lite Sentinel-1A (banda C) para o 
                         mapeamento do uso e cobertura da terra na regi{\~a}o de 
                         Ariquemes, RO. Para isso, foram testados sete cen{\'a}rios de 
                         classifica{\c{c}}{\~o}es a partir dos atributos 
                         extra{\'{\i}}dos dos dados de radar: coeficientes de 
                         retroespalhamento, decomposi{\c{c}}{\~a}o polarim{\'e}trica e 
                         coer{\^e}ncia interferom{\'e}trica, com os classificadores SVM 
                         (Support Vector Machine) e RF (Random Forest). O mapeamento foi 
                         dividido em duas fases, a Fase 1 buscando discriminar as classes 
                         Pastagem, Agricultura e Floresta e a Fase 2, realizando-se uma 
                         estratifica{\c{c}}{\~a}o da classe Floresta nas classes Floresta 
                         Degradada, Floresta Prim{\'a}ria e Sucess{\~o}es 
                         Secund{\'a}rias Avan{\c{c}}ada, Intermedi{\'a}ria e Inicial. A 
                         valida{\c{c}}{\~a}o dos mapeamentos foi realizada atrav{\'e}s 
                         da Simula{\c{c}}{\~a}o de Monte Carlo, utilizando-se as amostras 
                         de campo, com 1000 itera{\c{c}}{\~o}es, onde se obteve os 
                         valores m{\'e}dios de Kappa, acur{\'a}cia Global e matriz de 
                         confus{\~a}o para cada cen{\'a}rio. A an{\'a}lise da 
                         diferen{\c{c}}a estat{\'{\i}}stica entre os mapeamentos foi 
                         realizada pelo teste de McNemar. Al{\'e}m disso, foi realizado o 
                         mapeamento com o sensor {\'o}ptico Sentinel-2B com o objetivo de 
                         comparar os resultados em rela{\c{c}}{\~a}o ao radar. A partir 
                         da compara{\c{c}}{\~a}o dos cen{\'a}rios, observou-se que o 
                         cen{\'a}rio sete, que utilizou todos os atributos em conjunto, 
                         apresentou os melhores resultados com os classificadores SVM e RF, 
                         com uma acur{\'a}cia global igual a 81,6% e 69,6% para as Fases 1 
                         e 2, respectivamente, com o classificador SVM e 85,7% e 71,6% para 
                         as Fases 1 e 2 com o classificador RF, respectivamente. Al{\'e}m 
                         disso, o algoritmo RF apresentou superioridade para o mapeamento 
                         em rela{\c{c}}{\~a}o ao SVM, sendo considerados diferentes 
                         estatisticamente pelo teste de McNemar com 95% de 
                         confian{\c{c}}a. A partir da an{\'a}lise de import{\^a}ncia das 
                         vari{\'a}veis pelo RF, notou-se que a coer{\^e}ncia 
                         interferom{\'e}trica foi o atributo que apresentou a maior 
                         import{\^a}ncia para a discrimina{\c{c}}{\~a}o das classes 
                         tem{\'a}ticas para as duas fases do mapeamento. Atrav{\'e}s da 
                         compara{\c{c}}{\~a}o do mapeamento realizado com o cen{\'a}rio 
                         RF-7 do Sentinel-1A com o mapeamento realizado com o sensor 
                         {\'o}ptico, observou-se uma diferen{\c{c}}a de 4,3% na 
                         acur{\'a}cia global para a Fase 1 e de 13,8% para a Fase 2, com o 
                         melhor desempenho do sensor {\'o}ptico. Al{\'e}m disso, 
                         evidenciou-se a limita{\c{c}}{\~a}o da utiliza{\c{c}}{\~a}o de 
                         dados {\'o}pticos diante dos efeitos atmosf{\'e}ricos, como a 
                         presen{\c{c}}a de fuma{\c{c}}a. De modo geral, notou-se que os 
                         dados de radar foram capazes de discriminar as classes 
                         tem{\'a}ticas analisadas, apresentando valores de acur{\'a}cia 
                         global e Kappa considerados satisfat{\'o}rios. ABSTRACT: The 
                         human occupation process in the Legal Amazon has occurred in an 
                         increasingly pronounced way, especially with the expansion of the 
                         agricultural frontier. Understanding the process of land use and 
                         land cover change is critical to the planning and management of 
                         natural resources. Through remote sensing techniques, it is 
                         possible to map land use and land cover quickly and efficiently. 
                         The use of synthetic aperture radar (SAR) data have shown to be an 
                         alternative for monitoring and mapping in tropical regions, mainly 
                         because they are little influenced by cloud cover. So, the main 
                         objective of this dissertation was to evaluate the potential of 
                         the VV-VH dual images of the Sentinel-1A satellite (band C) for 
                         mapping land use and land cover in the region of Ariquemes, RO. 
                         Seven classification scenarios were tested using the attributes 
                         extracted from the radar data: backscatter coefficients, 
                         polarimetric decomposition and interferometric coherence, and the 
                         SVM (Support Vector Machine) and RF (Random Forest) classifiers. 
                         The mapping process was divided into two phases, Phase 1, which 
                         aimed to discriminate the classes Pasture, Agriculture and Forest, 
                         and Phase 2, whose Forest class was stratified in the classes: 
                         Degraded Forest, Primary Forest and Advanced, Intermediate and 
                         Initial Successions. The validation of the mappings was done 
                         through the Monte Carlo Simulation, using the field samples, with 
                         1000 iterations, where the mean values of Kappa, Global Accuracy 
                         and confusion matrix were obtained for each scenario. The analysis 
                         of the statistical difference between the mappings was performed 
                         by the McNemar test. In addition, it was carried out the mapping 
                         using the Sentinel-2B optical image in order to compare to the 
                         results obtained using the radar data. From the scenario 
                         comparison, it was observed that scenario seven, which used all 
                         the attributes together, presented the best results for both SVM 
                         and RF classifiers, with a global accuracy of 81.6% and 69.6% for 
                         Phases 1 and 2, respectively, with SVM classifier and 85.7% and 
                         71.6% for Phases 1 and 2 with Rf classifier, respectively. In 
                         addition, RF algorithm showed superiority over the SVM algorithm 
                         for mapping the region. The mappings obtained using the RF and SVM 
                         classifiers were considered statistically different by the McNemar 
                         test with 95% confidence. From the analysis of variable importance 
                         by the RF, it was noticed that the interferometric coherence was 
                         the attribute that presented the most importance for the 
                         discrimination of the thematic classes for the two phases of the 
                         mapping. By comparing the mapping performed with the Sentinel-1A 
                         RF-7 scenario with the mapping performed with the optical sensor, 
                         a difference of 4.3% was observed in the overall accuracy for 
                         Phase 1 and 13.8% for Phase 2, with the best optical sensor 
                         performance. In addition, it was evidenced the limitation of 
                         optical data to the atmospheric effects, such as the presence of 
                         smoke. In general, it was observed that the radar data were able 
                         to discriminate the classes of land use and land cover analyzed in 
                         this dissertation, presenting values of global accuracy and Kappa 
                         considered satisfactory.",
            committee = "Shimabukuro, Yosio Edemir (presidente) and Gama, F{\'a}bio Furlan 
                         (orientador) and Mura, Jos{\'e} Claudio and Araujo, Luciana 
                         Spinelli",
         englishtitle = "Evaluation of the potential of polarimetric data sentinel-1a for 
                         mapping land use and land cover at the ariquemes-RO region",
             language = "pt",
                pages = "132",
                  ibi = "8JMKD3MGP3W34R/3SL65N8",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3SL65N8",
           targetfile = "publicacao.pdf",
        urlaccessdate = "02 dez. 2020"
}


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