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@PhDThesis{Bendini:2019:AgLaCl,
               author = "Bendini, Hugo do Nascimento",
                title = "Agricultural land classification based on phenological information 
                         from dense time-series Landsat-like images in the brazilian 
                         Cerrado",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2019",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2018-07-13",
             keywords = "Big-data, time-series analysis, agricultural land use 
                         classification, multi-sensor, remote sensing, big data, 
                         an{\'a}lise de s{\'e}ries temporais, classifica{\c{c}}{\~a}o 
                         de uso agr{\'{\i}}cola, multi-sensor, sensoriamento remoto.",
             abstract = "Brazil has an important role in the world in terms of food 
                         production and the largest native forest, providing essential 
                         environmental services for the planet and humanity. However, this 
                         highlights the challenge of creating an economic development model 
                         that takes into account the environmental conservation. Brazil has 
                         already demonstrated successful experiences in Amazon 
                         deforestation reduction, but other biomes of great environmental 
                         importance, such as the Cerrado, has been under great pressure of 
                         agricultural expansion. Satellite image time series can be used to 
                         derive phenological information of vegetation, and considering the 
                         high heterogeneity of crop types and their respective planting 
                         calendars in Brazil, is essential for crop classification and 
                         monitoring. Our hypothesis in this thesis is that phenological 
                         information can be extracted from Landsatlike dense image time 
                         series, allowing the development of a method for agriculture 
                         mapping with more detail. We tested the integration of different 
                         satellite, such as Landsat-8, Landsat-7 and CBERS-4, combined with 
                         different smoothing techniques, to generate EVI (Enhanced 
                         Vegetation Index) image time series at high frequency in order to 
                         extract the phenological metrics. A hierarchical classification 
                         approach using the Random Forest algorithm was developed to 
                         produce detailed agricultural maps. The classification results are 
                         promising (higher than 80% of overall accuracy) and showed the 
                         feasibility of applying the method on a large scale and over a 
                         longer period of time for the Cerrado biome. In addition, the 
                         phenological information obtained by the method showed a potential 
                         to be used in the understanding of different agricultural 
                         practices adopted by farmers in property level. RESUMO: O Brasil 
                         tem um papel importante no mundo em termos de produ{\c{c}}{\~a}o 
                         de alimentos e a maior floresta nativa, fornecendo servi{\c{c}}os 
                         ambientais essenciais para o planeta e para a humanidade. No 
                         entanto, isso destaca o desafio de criar um modelo de 
                         desenvolvimento econ{\^o}mico que leve em 
                         considera{\c{c}}{\~a}o a conserva{\c{c}}{\~a}o ambiental. O 
                         Brasil j{\'a} demonstrou experi{\^e}ncias bem-sucedidas na 
                         redu{\c{c}}{\~a}o do desmatamento da Amaz{\^o}nia, mas outros 
                         biomas de grande import{\^a}ncia ambiental, como o Cerrado, 
                         est{\~a}o sob grande press{\~a}o de expans{\~a}o 
                         agr{\'{\i}}cola. S{\'e}ries temporais de imagens de 
                         sat{\'e}lite podem ser usadas para derivar 
                         informa{\c{c}}{\~o}es fenol{\'o}gicas da vegeta{\c{c}}{\~a}o. 
                         Considerando a diversidade de culturas agr{\'{\i}}colas e seus 
                         respectivos calend{\'a}rios de plantio no Brasil, essas 
                         informa{\c{c}}{\~o}es s{\~a}o essenciais para a 
                         classifica{\c{c}}{\~a}o e monitoramento agr{\'{\i}}cola. Nossa 
                         hip{\'o}tese {\'e} que informa{\c{c}}{\~o}es fenol{\'o}gicas 
                         podem ser extra{\'{\i}}das de s{\'e}ries temporais de imagens 
                         de resolu{\c{c}}{\~a}o espacial Landsat-like, permitindo o 
                         desenvolvimento de m{\'e}todo para mapeamento detalhado da 
                         agricultura. Testamos a integra{\c{c}}{\~a}o de diferentes 
                         sat{\'e}lites, como Landsat-8, Landsat-7 e CBERS-4, combinados 
                         com diferentes t{\'e}cnicas de suaviza{\c{c}}{\~a}o para gerar 
                         s{\'e}ries temporais de imagem EVI (Enhanced Vegetation Index) em 
                         alta frequ{\^e}ncia e extrair as m{\'e}tricas fenol{\'o}gicas. 
                         Uma abordagem de classifica{\c{c}}{\~a}o hier{\'a}rquica usando 
                         o algoritmo Random Forest foi aplicada para produzir os mapas. Os 
                         resultados da classifica{\c{c}}{\~a}o s{\~a}o promissores 
                         (acima de 80% da acur{\'a}cia) e mostraram a viabilidade de 
                         aplicar o m{\'e}todo em larga escala e por um longo 
                         per{\'{\i}}odo para o Bioma Cerrado. Al{\'e}m disso, as 
                         informa{\c{c}}{\~o}es fenol{\'o}gicas mostraram potencial para 
                         serem utilizadas na compreens{\~a}o de diferentes pr{\'a}ticas 
                         agr{\'{\i}}colas adotadas pelos agricultores no Cerrado, em 
                         escala de propriedade.",
            committee = "Sanches, Ieda Del' Arco (presidente) and Fonseca, Leila Maria 
                         Garcia (orientadora) and K{\"o}rting, Thales Sehn (orientador) 
                         and Camargo Neto, Jo{\~a}o and Feitosa, Raul Queiroz and Silva, 
                         Silvia Helena Modenese Gorla da",
         englishtitle = "Classifica{\c{c}}{\~a}o de {\'a}reas agr{\'{\i}}colas baseada 
                         em informa{\c{c}}{\~o}es fenol{\'o}gicas de s{\'e}ries 
                         temporais de imagens Landsat-like no Cerrado brasileiro",
             language = "en",
                pages = "122",
                  ibi = "8JMKD3MGP3W34R/3RJS628",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/3RJS628",
           targetfile = "publicacao.pdf",
        urlaccessdate = "04 dez. 2020"
}


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