Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas

Detalhes bibliográficos
Autor(a) principal: Becker, Willyan Ronaldo
Data de Publicação: 2016
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
Texto Completo: http://tede.unioeste.br/handle/tede/2975
Resumo: Orbital remote sensing techniques have proved to be a valuable tool, since they enable the agricultural monitoring of the vigor and the type of vegetation coverage in a regional scale, bringing results with greater anticipation and precision, and lower operational cost when compared to traditional techniques. Automatic identification of cultivated areas is one of the most important steps in the crop forecasting process. The improvement in the estimate of area cultivated with each crop directly influences the result of the forecast of each crop year, since the agricultural production is a function of the cultivated area. The general objective of this research was to create an automatic methodology for the separation of agricultural crops from soybean and maize by means of data mining (Article 1) and a methodology for forecasting the harvest date from the date of maximum vegetative development (Article 2). The methods used corresponded to the application of the seasonal trends analysis and data mining for soybean and corn agricultural areas in the state of Paraná, with images of the EVI vegetation index of MODIS sensors, TERRA and AQUA satellites. The results obtained in Article 1 show that, through the decision tree, one of the techniques of data mining, it was verified that, among eleven variables that characterize the spectral-temporal pattern of the EVI of each culture, five were enough to perform the separation of soybean and maize crops, in the year 2014/2015, with an accuracy of 96.3% and a kappa index of 0.92, being the maximum value of EVI, the date of sowing (DS), the Date of maximum vegetative development (DMDV), Cycle, and Major Integral. In Article 2 the DS, DMDV and Harvest Date (DC) of the EVI temporal profile were estimated for each mapped soybean and maize pixel in the crop years 2011/2012 to 2013/2014. Then, for each crop and crop year, the variables Delta1 (DMDV minus DS) and Delta2 (DC minus DMDV) were created. The results of the differences (DCDifference) between DC estimated by EVI (DCEVI) and predicted by mean time (DCDelta2) show that, for soybeans, it is possible to use only the mean value of the interval between DMDV and DC in the three harvested years studied, with 55 days for soybeans. For corn, the mean interval between DMDV and DC was 60 days, but it is verified that there is a large difference between the results obtained with DCEVI and DCDelta2. For corn DCDelta2 there were large variations among the mesoregions. Differences in DC (DCDifference), when using the means by mesoregions, presented better results than for Paraná as a whole.
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spelling Johann, Jerry Adrianihttp://lattes.cnpq.br/34997043083017Johann , Jerry Adrianihttp://lattes.cnpq.br/34997043083017Correa, Marcus Metrihttp://lattes.cnpq.br/3722390324317011Mercante, Eriveltohttp://lattes.cnpq.br/4061800207647478http://lattes.cnpq.br/6173806880513817Becker, Willyan Ronaldo2017-08-31T19:33:36Z2016-02-10BECKER, Willyan Ronaldo. Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas. 2016. 115 f. Dissertação ( Mestrado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel 2017 .http://tede.unioeste.br/handle/tede/2975Orbital remote sensing techniques have proved to be a valuable tool, since they enable the agricultural monitoring of the vigor and the type of vegetation coverage in a regional scale, bringing results with greater anticipation and precision, and lower operational cost when compared to traditional techniques. Automatic identification of cultivated areas is one of the most important steps in the crop forecasting process. The improvement in the estimate of area cultivated with each crop directly influences the result of the forecast of each crop year, since the agricultural production is a function of the cultivated area. The general objective of this research was to create an automatic methodology for the separation of agricultural crops from soybean and maize by means of data mining (Article 1) and a methodology for forecasting the harvest date from the date of maximum vegetative development (Article 2). The methods used corresponded to the application of the seasonal trends analysis and data mining for soybean and corn agricultural areas in the state of Paraná, with images of the EVI vegetation index of MODIS sensors, TERRA and AQUA satellites. The results obtained in Article 1 show that, through the decision tree, one of the techniques of data mining, it was verified that, among eleven variables that characterize the spectral-temporal pattern of the EVI of each culture, five were enough to perform the separation of soybean and maize crops, in the year 2014/2015, with an accuracy of 96.3% and a kappa index of 0.92, being the maximum value of EVI, the date of sowing (DS), the Date of maximum vegetative development (DMDV), Cycle, and Major Integral. In Article 2 the DS, DMDV and Harvest Date (DC) of the EVI temporal profile were estimated for each mapped soybean and maize pixel in the crop years 2011/2012 to 2013/2014. Then, for each crop and crop year, the variables Delta1 (DMDV minus DS) and Delta2 (DC minus DMDV) were created. The results of the differences (DCDifference) between DC estimated by EVI (DCEVI) and predicted by mean time (DCDelta2) show that, for soybeans, it is possible to use only the mean value of the interval between DMDV and DC in the three harvested years studied, with 55 days for soybeans. For corn, the mean interval between DMDV and DC was 60 days, but it is verified that there is a large difference between the results obtained with DCEVI and DCDelta2. For corn DCDelta2 there were large variations among the mesoregions. Differences in DC (DCDifference), when using the means by mesoregions, presented better results than for Paraná as a whole.Técnicas de sensoriamento remoto orbital têm se mostrado uma ferramenta valiosa, pois possibilitam o monitoramento agrícola do vigor e do tipo de cobertura vegetal em escala regional, trazendo resultados com maior antecedência e precisão e menor custo operacional em relação às técnicas tradicionais. A identificação automática de áreas cultivadas constitui uma das etapas mais importantes no processo de previsão de safras. A melhoria na estimativa de área cultivada com cada cultura influencia diretamente o resultado da previsão de cada ano-safra, uma vez que a produção agrícola é função da área cultivada. O objetivo geral desta pesquisa foi criar uma metodologia automática para separação das culturas agrícolas de soja e milho, por meio da mineração de dados (Artigo 1) e uma metodologia de previsão da data de colheita das culturas a partir da data de máximo desenvolvimento vegetativo (Artigo 2). Os métodos utilizados corresponderam à aplicação da análise de padrões sazonais e mineração de dados para áreas agrícolas de soja e milho no estado do Paraná, com imagens do índice de vegetação EVI dos sensores MODIS, satélites TERRA e AQUA. Os resultados obtidos no Artigo 1 mostram que, por meio da árvore de decisão, uma das técnicas de mineração de dados, constatou-se que, dentre onze variáveis que caracterizam o padrão espectro-temporal do EVI de cada cultura, cinco foram suficientes para realizar a separação das culturas de soja e milho, ano-safra 2014/2015, com uma exatidão de 96,3% e um índice kappa de 0,92, sendo elas o valor máximo de EVI, a data de semeadura (DS), a data de máximo desenvolvimento vegetativo (DMDV), o ciclo e a integral maior. No Artigo 2 foram estimadas as DS, DMDV e Data de Colheita (DC) do perfil temporal EVI para cada pixel mapeado de soja e milho nos anos-safra 2011/2012 a 2013/2014. Posteriormente criaram-se, para cada cultura e ano-safra, as variáveis Delta1 (DMDV menos a DS) e o Delta2 (DC menos a DMDV). Os resultados das diferenças (DCDiferença) entre a DC estimada pelo EVI (DCEVI) e a prevista por média temporal (DCDelta2) apontam que, para a soja, há a possibilidade de utilizar-se apenas do valor médio do intervalo entre a DMDV e a DC nos três anos-safra estudados, sendo 55 dias para a soja. Para a cultura do milho, o intervalo médio entre a DMDV e a DC foi de 60 dias, porém verifica-se que existe grande diferença entre os resultados obtidos com a DCEVI e a DCDelta2. Para a DCDelta2 do milho houve grandes variações entre as mesorregiões. As diferenças nas DC (DCDiferença), quando utilizadas as médias por mesorregiões, apresentam melhores resultados que para o Paraná como um todo.Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2017-08-31T19:33:36Z No. of bitstreams: 2 Willyan_Becker2017.pdf: 12083877 bytes, checksum: 7a9e90225376028c123e5c6e1c568603 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2017-08-31T19:33:36Z (GMT). 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dc.title.por.fl_str_mv Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
dc.title.alternative.none.fl_str_mv Seasonal trend analysis ofthe vegetation index of agricultural crops with data mining techniques
title Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
spellingShingle Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
Becker, Willyan Ronaldo
Ciclo fenológico
EVI
Timesat
Mineração de Dados
Phenological cycle
EVI
Timesat
Data Mining
CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
title_short Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
title_full Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
title_fullStr Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
title_full_unstemmed Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
title_sort Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas
author Becker, Willyan Ronaldo
author_facet Becker, Willyan Ronaldo
author_role author
dc.contributor.advisor1.fl_str_mv Johann, Jerry Adriani
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/34997043083017
dc.contributor.referee1.fl_str_mv Johann , Jerry Adriani
dc.contributor.referee1Lattes.fl_str_mv http://lattes.cnpq.br/34997043083017
dc.contributor.referee2.fl_str_mv Correa, Marcus Metri
dc.contributor.referee2Lattes.fl_str_mv http://lattes.cnpq.br/3722390324317011
dc.contributor.referee3.fl_str_mv Mercante, Erivelto
dc.contributor.referee3Lattes.fl_str_mv http://lattes.cnpq.br/4061800207647478
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/6173806880513817
dc.contributor.author.fl_str_mv Becker, Willyan Ronaldo
contributor_str_mv Johann, Jerry Adriani
Johann , Jerry Adriani
Correa, Marcus Metri
Mercante, Erivelto
dc.subject.por.fl_str_mv Ciclo fenológico
EVI
Timesat
Mineração de Dados
topic Ciclo fenológico
EVI
Timesat
Mineração de Dados
Phenological cycle
EVI
Timesat
Data Mining
CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
dc.subject.eng.fl_str_mv Phenological cycle
EVI
Timesat
Data Mining
dc.subject.cnpq.fl_str_mv CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
description Orbital remote sensing techniques have proved to be a valuable tool, since they enable the agricultural monitoring of the vigor and the type of vegetation coverage in a regional scale, bringing results with greater anticipation and precision, and lower operational cost when compared to traditional techniques. Automatic identification of cultivated areas is one of the most important steps in the crop forecasting process. The improvement in the estimate of area cultivated with each crop directly influences the result of the forecast of each crop year, since the agricultural production is a function of the cultivated area. The general objective of this research was to create an automatic methodology for the separation of agricultural crops from soybean and maize by means of data mining (Article 1) and a methodology for forecasting the harvest date from the date of maximum vegetative development (Article 2). The methods used corresponded to the application of the seasonal trends analysis and data mining for soybean and corn agricultural areas in the state of Paraná, with images of the EVI vegetation index of MODIS sensors, TERRA and AQUA satellites. The results obtained in Article 1 show that, through the decision tree, one of the techniques of data mining, it was verified that, among eleven variables that characterize the spectral-temporal pattern of the EVI of each culture, five were enough to perform the separation of soybean and maize crops, in the year 2014/2015, with an accuracy of 96.3% and a kappa index of 0.92, being the maximum value of EVI, the date of sowing (DS), the Date of maximum vegetative development (DMDV), Cycle, and Major Integral. In Article 2 the DS, DMDV and Harvest Date (DC) of the EVI temporal profile were estimated for each mapped soybean and maize pixel in the crop years 2011/2012 to 2013/2014. Then, for each crop and crop year, the variables Delta1 (DMDV minus DS) and Delta2 (DC minus DMDV) were created. The results of the differences (DCDifference) between DC estimated by EVI (DCEVI) and predicted by mean time (DCDelta2) show that, for soybeans, it is possible to use only the mean value of the interval between DMDV and DC in the three harvested years studied, with 55 days for soybeans. For corn, the mean interval between DMDV and DC was 60 days, but it is verified that there is a large difference between the results obtained with DCEVI and DCDelta2. For corn DCDelta2 there were large variations among the mesoregions. Differences in DC (DCDifference), when using the means by mesoregions, presented better results than for Paraná as a whole.
publishDate 2016
dc.date.issued.fl_str_mv 2016-02-10
dc.date.accessioned.fl_str_mv 2017-08-31T19:33:36Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.citation.fl_str_mv BECKER, Willyan Ronaldo. Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas. 2016. 115 f. Dissertação ( Mestrado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel 2017 .
dc.identifier.uri.fl_str_mv http://tede.unioeste.br/handle/tede/2975
identifier_str_mv BECKER, Willyan Ronaldo. Análise do padrão sazonal de imagens de índice de vegetação do sensor modis para culturas agrícolas. 2016. 115 f. Dissertação ( Mestrado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel 2017 .
url http://tede.unioeste.br/handle/tede/2975
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dc.publisher.none.fl_str_mv Universidade Estadual do Oeste do Paraná
Cascavel
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dc.publisher.initials.fl_str_mv UNIOESTE
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv Centro de Ciências Exatas e Tecnológicas
publisher.none.fl_str_mv Universidade Estadual do Oeste do Paraná
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repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações do UNIOESTE - Universidade Estadual do Oeste do Paraná (UNIOESTE)
repository.mail.fl_str_mv biblioteca.repositorio@unioeste.br
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