Fechar
Metadados

@MastersThesis{Mariano:2015:DeAvSe,
               author = "Mariano, Denis Araujo",
                title = "Detec{\c{c}}{\~a}o e avalia{\c{c}}{\~a}o de seca 
                         agron{\^o}mica atrav{\'e}s da an{\'a}lise de s{\'e}ries 
                         temporais de dados MODIS e PERSIANN",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2015",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2015-03-16",
             keywords = "seca agron{\^o}mica, MODIS, PERSIANN, s{\'e}ries temporais, 
                         transformada wavelet, agricultural drought, MODIS, PERSIANN, 
                         time-series, wavelet transform.",
             abstract = "Eventos de seca causam grande preju{\'{\i}}zo para a agricultura 
                         brasileira, sendo a regi{\~a}o Sul frequentemente castigada por 
                         esse fen{\^o}meno. Essas ocorr{\^e}ncias prejudicam gravemente a 
                         cadeia agr{\'{\i}}cola nacional, o que causa 
                         oscila{\c{c}}{\~a}o de pre{\c{c}}os, onera produtores e 
                         empresas. Tais ocorr{\^e}ncias evidenciam a necessidade de 
                         sistemas para monitorar e quantificar a seca com 
                         informa{\c{c}}{\~o}es precisas e espacializadas. O presente 
                         trabalho objetivou mensurar em termos de intensidade e 
                         abrang{\^e}ncia os eventos de seca agron{\^o}mica ocorridos no 
                         Paran{\'a} para as safras de ver{\~a}o de 2002 a 2013, 
                         considerando milho e soja. Foram utilizados dados dos espectros 
                         vis{\'{\i}}vel, infravermelho e termal do \emph{Moderate 
                         Resolution Imaging Spectroradiometer} (MODIS) e de 
                         precipita{\c{c}}{\~a}o estimada por sensoriamento remoto (SR) 
                         oriundos do produto \emph{Precipitation Estimation from Remotely 
                         Sensed Information Using Artificial Neural Networks} (PERSIANN). 
                         V{\'a}rios {\'{\i}}ndices de vegeta{\c{c}}{\~a}o (IV) foram 
                         avaliados, tendo estes em geral apresentado maior 
                         correla{\c{c}}{\~a}o com a precipita{\c{c}}{\~a}o de outubro 
                         para os anos secos e janeiro para os normais, indicando que, para 
                         o desempenho da vegeta{\c{c}}{\~a}o, estes meses foram os mais 
                         relevantes para os respectivos tipos de anos. Foram criadas linhas 
                         de base para cada IV, considerando a mediana hist{\'o}rica para 
                         cada data fenol{\'o}gica. Os IVs foram ent{\~a}o avaliados para 
                         caracteriza{\c{c}}{\~a}o da seca, tendo o \emph{Land Surface 
                         Water Index} (LSWI) se destacado, apresentando maior 
                         correla{\c{c}}{\~a}o com m{\'e}tricas de 
                         precipita{\c{c}}{\~a}o e produtividade agr{\'{\i}}cola. Foram 
                         estudadas as rela{\c{c}}{\~o}es entre o LSWI e \emph{Land 
                         surfasse temperature} (LST), sendo detectada uma 
                         rela{\c{c}}{\~a}o inversa entre as vari{\'a}veis. Foram 
                         analisadas as transformadas de \emph{wavelet [wavelet transform} 
                         (WT)] para cada vari{\'a}vel e utilizado os m{\'e}todos da WT 
                         cruzada (XWT) e coer{\^e}ncia de WT (WCT). N{\~a}o foi 
                         verificada rela{\c{c}}{\~a}o de causa-efeito entre as 
                         vari{\'a}veis, mas sim, uma rela{\c{c}}{\~a}o de espelho, ou 
                         seja, outros fatores governam o comportamento de LSWI e LST. 
                         Segundo a literatura, o principal fator {\'e} a umidade do solo, 
                         a qual {\'e} bem correlacionada com o LSWI. Por fim, as 
                         diferen{\c{c}}as acumuladas entre LSWI de cada safra e sua linha 
                         de base (LSWI-dif) foram espacializadas na forma de mapas para 
                         cada ano-safra, o que visualmente foi bastante coerente com os 
                         mapas de precipita{\c{c}}{\~a}o acumulada. O m{\'e}todo de 
                         mensura{\c{c}}{\~a}o de seca agron{\^o}mica proposto se mostrou 
                         eficiente e potencialmente aplic{\'a}vel para fins de 
                         monitoramento agr{\'{\i}}cola, tendo como maior 
                         limita{\c{c}}{\~a}o a resolu{\c{c}}{\~a}o espacial dos dados 
                         utilizados. ABSTRACT: Drought events strike Brazilian agriculture 
                         causing yield losses, being the south region often stricken by 
                         this phenomenon. These occurrences lead to negative impacts in the 
                         agricultural chain by causing commodities prices fluctuation and 
                         hampering farmers and companies finances condition. A need for a 
                         system for monitoring and retrieving drought on time and 
                         spatialized information regarding the agriculture emerges in this 
                         context. The present study aimed at measuring and quantify the 
                         intensity and geographical spreading of the agricultural drought 
                         phenomena occurred in Paran{\'a} state between the 2002 and 2013 
                         summer seasons, considering maize and soybean crops. Remote sensed 
                         reflectance and thermal data from Moderate Resolution Imaging 
                         Spectroradiometer (MODIS) sensors and precipitation from the 
                         Precipitation Estimation from Remotely Sensed Information Using 
                         Artificial Neural Networks (PERSIANN) were used as input for the 
                         methods. Several vegetation indices (VI) were tested and 
                         generally, they were better correlated to accumulated 
                         precipitation in October for dry years and January for normal 
                         years, showing that these months are crucial for the vegetation 
                         condition regarding these kind of years. Using phenology and 
                         historical data, baselines for each index were created in a median 
                         basis. The VIs were then analyzed in order to better assess the 
                         agricultural drought occurrences being the Land Surface Water 
                         Index (LSWI) the most well suited for this task. LSWI showed 
                         better correlation to precipitation metrics and estimated yield. 
                         The relationships between LSWI and Land Surface Temperature (LST) 
                         were studied as well, showing an inverse correlation between then. 
                         The wavelet transform (WT) were used in each variable and the 
                         cross WT (XWT) and WT coherence (WCT) methods were applied for 
                         testing these relations. Cause-effect relationships were not 
                         found, instead, LST and LSWI simply mirrored each other, this 
                         means that other variables govern the LST and LSWI behavior, 
                         according to the literature, the main factor is the soil moisture 
                         which is also well correlated to LSWI. Finally, the accumulated 
                         differences between LSWI for each season and the baseline 
                         (LSWI-dif ) were spatialized and mapped being possible to 
                         visualize the coherence between precipitation and LSWI-dif maps. 
                         The proposed method proved itself on being well suitable for 
                         agricultural monitoring needs, however, the main hurdle was the 
                         spatial resolution of the input data.",
            committee = "Moreira, Maur{\'{\i}}cio Alves (presidente/orientador) and 
                         Formaggio, Ant{\^o}nio Roberto and Sanches, Ieda Del'Arco and 
                         Galv{\~a}o, L{\^e}nio Soares and Zullo J{\'u}nior, Jurandir",
           copyholder = "SID/SCD",
         englishtitle = "Agricultural drought detection and assessment through MODIS and 
                         PERSIANN time-series data analysis",
             language = "pt",
                pages = "110",
                  ibi = "8JMKD3MGP3W34P/3HU5BP5",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3HU5BP5",
           targetfile = "publicacao.pdf",
        urlaccessdate = "25 nov. 2020"
}


Fechar