Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | http://tede.unioeste.br/handle/tede/3057 |
Resumo: | The acquisition of effective technologies for prediction and monitoring of agricultural crops highlights the search for methodologies that make this information available before harvesting. Currently, the monitoring of agricultural production is still partially carried out through subjective and onerous techniques by Brazilian official bodies. The study of the agricultural monitoring and/or estimation of winter crops yield, using vegetation indexes extracted from multitemporal images of the MODIS sensor, is a reality that has been tested by several authors, in the search for greater objectivity to the generated figures. In this context, this research aims to map and estimate areas with winter and maize crops, using temporal series of the EVI vegetation index from the MODIS sensor of the Terra and Aqua satellites, for the 2012, 2013, and 2014 harvests for the state of Paraná. As a way of adjusting the mapping through the MODIS sensor (250 meters), the visual analysis was performed in which images of medium spatial resolution (30 meters) were used to identify the chosen cultures. In article 1, color compositions were generated using images from the pre-planting period until the initial development and images representing the vegetative peak of the crops. Subsequently, the extraction of cultivated areas with the crops of interest was performed, so that these could be compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 2, colored compositions were generated using only the vegetative peak images of the cultures to be classified using the Spectral Angle Mapper (SAM) algorithm. Subsequently, the masks were compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 1, an underestimation of the safflower and winter crops areas was found for the 2012 and 2013 crops, and an overestimation for the 2014 safflower, and for the winter crops, overestimation. By the accuracy analyzes, the masks were classified with excellence. In Article 2, it was verified that the data of areas were overestimated for the safflower corn and underestimated for the winter crops. The accuracy analyzes were classified as excellent, in relation to the medium resolution image. |
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Mercante, Eriveltohttp://lattes.cnpq.br/4061800207647478Correa, Marcus Metrihttp://lattes.cnpq.br/3722390324317011Prior, Maritanehttp://lattes.cnpq.br/4825760115389832Maggi, Marcio Furlanhttp://lattes.cnpq.br/8677221771738301Souza, Carlos Henrique Wachholz dehttp://lattes.cnpq.br/2804633646710952http://lattes.cnpq.br/3308426960226685Nicolau, Rafaela Fernandes2017-09-15T17:50:23Z2017-02-15NICOLAU, Rafaela Fernandes. Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno. 2017. 76 f. Tese (Doutorado - Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, 2017http://tede.unioeste.br/handle/tede/3057The acquisition of effective technologies for prediction and monitoring of agricultural crops highlights the search for methodologies that make this information available before harvesting. Currently, the monitoring of agricultural production is still partially carried out through subjective and onerous techniques by Brazilian official bodies. The study of the agricultural monitoring and/or estimation of winter crops yield, using vegetation indexes extracted from multitemporal images of the MODIS sensor, is a reality that has been tested by several authors, in the search for greater objectivity to the generated figures. In this context, this research aims to map and estimate areas with winter and maize crops, using temporal series of the EVI vegetation index from the MODIS sensor of the Terra and Aqua satellites, for the 2012, 2013, and 2014 harvests for the state of Paraná. As a way of adjusting the mapping through the MODIS sensor (250 meters), the visual analysis was performed in which images of medium spatial resolution (30 meters) were used to identify the chosen cultures. In article 1, color compositions were generated using images from the pre-planting period until the initial development and images representing the vegetative peak of the crops. Subsequently, the extraction of cultivated areas with the crops of interest was performed, so that these could be compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 2, colored compositions were generated using only the vegetative peak images of the cultures to be classified using the Spectral Angle Mapper (SAM) algorithm. Subsequently, the masks were compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 1, an underestimation of the safflower and winter crops areas was found for the 2012 and 2013 crops, and an overestimation for the 2014 safflower, and for the winter crops, overestimation. By the accuracy analyzes, the masks were classified with excellence. In Article 2, it was verified that the data of areas were overestimated for the safflower corn and underestimated for the winter crops. The accuracy analyzes were classified as excellent, in relation to the medium resolution image.A obtenção de tecnologias eficazes para a previsão e o acompanhamento de safras agrícolas ressalta a busca de metodologias que disponibilizem essas informações antes da colheita. Atualmente, o acompanhamento da produção agrícola é ainda em parte realizado por meio de técnicas subjetivas e onerosas por órgãos oficiais brasileiros. O estudo do monitoramento agrícola e/ou estimativa de safras das culturas de inverno, utilizando índices de vegetação extraídos de imagens multitemporais do sensor MODIS, é uma realidade que tem sido testada por diversos autores na busca de maior objetividade para os valores gerados. Nesse contexto, esta pesquisa tem por objetivo mapear e estimar áreas com as lavouras de inverno e de milho safrinha, utilizando séries temporais do índice de vegetação EVI, provenientes do sensor MODIS dos satélites Terra e Aqua, nas safras 2012, 2013 e 2014 para o estado do Paraná. Como forma de ajustar o mapeamento por meio do sensor MODIS (250 metros), foi realizada uma análise visual em que foram utilizadas imagens de média resolução espacial (30 metros) para identificação das culturas desejadas. No Artigo 1 foram geradas composições coloridas utilizando imagens do período de pré-plantio até o desenvolvimento inicial e imagens que representam o pico vegetativos das lavouras. Posteriormente, foi realizada a extração de áreas cultivadas com as lavouras de interesse para que pudessem ser comparadas com dados oficiais por meio de estatísticas e correlações, como também análises de acurácia. No Artigo 2 foram geradas composições coloridas utilizando somente as imagens que representam os picos vegetativos das lavouras para serem classificadas, utilizando o algoritmo SAM (Spectral angle mapper). Posteriormente, as máscaras foram comparadas com dados oficiais por meio de estatísticas e correlações, como também análises de acurácia. No Artigo 1 foi verificada subestimação para o milho 2ª safra nas safras 2012 e 2013 e superestimação em 2014 e, para lavouras de inverno, superestimação. Pelas análises de acurácia, as máscaras foram classificadas com excelência. No Artigo 2 foi verificado que os dados de áreas foram superestimados para o milho 2ª safra e subestimados para as lavouras de inverno. As análises de acurácia foram classificadas como excelentes em relação à imagem de média resolução.Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2017-09-15T17:50:23Z No. of bitstreams: 1 Rafaela_Nicolau2017.pdf: 2526644 bytes, checksum: b34f04c83f091610940b76b8a104e99a (MD5)Made available in DSpace on 2017-09-15T17:50:23Z (GMT). No. of bitstreams: 1 Rafaela_Nicolau2017.pdf: 2526644 bytes, checksum: b34f04c83f091610940b76b8a104e99a (MD5) Previous issue date: 2017-02-15Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfpor6588633818200016417500Universidade Estadual do Oeste do ParanáCascavelPrograma de Pós-Graduação em Engenharia AgrícolaUNIOESTEBrasilCentro de Ciências Exatas e TecnológicasSensor MODISSensoriamento remotoSérie temporalMODIS sensorTemporal seriesRemote sensingCIENCIAS AGRARIAS::ENGENHARIA AGRICOLAÍndice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de invernoEVI vegetation index for area estimating of second harvest corn and winter cropsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis-5347692450416052129600600600600221437444286838201591854457215887615552075167498588264571info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALRafaela_Nicolau2017.pdfRafaela_Nicolau2017.pdfapplication/pdf2526644http://tede.unioeste.br:8080/tede/bitstream/tede/3057/2/Rafaela_Nicolau2017.pdfb34f04c83f091610940b76b8a104e99aMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://tede.unioeste.br:8080/tede/bitstream/tede/3057/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/30572017-09-15 14:50:23.04oai:tede.unioeste.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede.unioeste.br/PUBhttp://tede.unioeste.br/oai/requestbiblioteca.repositorio@unioeste.bropendoar:2017-09-15T17:50:23Biblioteca Digital de Teses e Dissertações do UNIOESTE - Universidade Estadual do Oeste do Paraná (UNIOESTE)false |
dc.title.por.fl_str_mv |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno |
dc.title.alternative.eng.fl_str_mv |
EVI vegetation index for area estimating of second harvest corn and winter crops |
title |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno |
spellingShingle |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno Nicolau, Rafaela Fernandes Sensor MODIS Sensoriamento remoto Série temporal MODIS sensor Temporal series Remote sensing CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
title_short |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno |
title_full |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno |
title_fullStr |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno |
title_full_unstemmed |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno |
title_sort |
Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno |
author |
Nicolau, Rafaela Fernandes |
author_facet |
Nicolau, Rafaela Fernandes |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Mercante, Erivelto |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4061800207647478 |
dc.contributor.referee1.fl_str_mv |
Correa, Marcus Metri |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/3722390324317011 |
dc.contributor.referee2.fl_str_mv |
Prior, Maritane |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/4825760115389832 |
dc.contributor.referee3.fl_str_mv |
Maggi, Marcio Furlan |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/8677221771738301 |
dc.contributor.referee4.fl_str_mv |
Souza, Carlos Henrique Wachholz de |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/2804633646710952 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3308426960226685 |
dc.contributor.author.fl_str_mv |
Nicolau, Rafaela Fernandes |
contributor_str_mv |
Mercante, Erivelto Correa, Marcus Metri Prior, Maritane Maggi, Marcio Furlan Souza, Carlos Henrique Wachholz de |
dc.subject.por.fl_str_mv |
Sensor MODIS Sensoriamento remoto Série temporal |
topic |
Sensor MODIS Sensoriamento remoto Série temporal MODIS sensor Temporal series Remote sensing CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
dc.subject.eng.fl_str_mv |
MODIS sensor Temporal series Remote sensing |
dc.subject.cnpq.fl_str_mv |
CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
description |
The acquisition of effective technologies for prediction and monitoring of agricultural crops highlights the search for methodologies that make this information available before harvesting. Currently, the monitoring of agricultural production is still partially carried out through subjective and onerous techniques by Brazilian official bodies. The study of the agricultural monitoring and/or estimation of winter crops yield, using vegetation indexes extracted from multitemporal images of the MODIS sensor, is a reality that has been tested by several authors, in the search for greater objectivity to the generated figures. In this context, this research aims to map and estimate areas with winter and maize crops, using temporal series of the EVI vegetation index from the MODIS sensor of the Terra and Aqua satellites, for the 2012, 2013, and 2014 harvests for the state of Paraná. As a way of adjusting the mapping through the MODIS sensor (250 meters), the visual analysis was performed in which images of medium spatial resolution (30 meters) were used to identify the chosen cultures. In article 1, color compositions were generated using images from the pre-planting period until the initial development and images representing the vegetative peak of the crops. Subsequently, the extraction of cultivated areas with the crops of interest was performed, so that these could be compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 2, colored compositions were generated using only the vegetative peak images of the cultures to be classified using the Spectral Angle Mapper (SAM) algorithm. Subsequently, the masks were compared with official data through statistics and correlations, as well as accuracy analyzes. In Article 1, an underestimation of the safflower and winter crops areas was found for the 2012 and 2013 crops, and an overestimation for the 2014 safflower, and for the winter crops, overestimation. By the accuracy analyzes, the masks were classified with excellence. In Article 2, it was verified that the data of areas were overestimated for the safflower corn and underestimated for the winter crops. The accuracy analyzes were classified as excellent, in relation to the medium resolution image. |
publishDate |
2017 |
dc.date.accessioned.fl_str_mv |
2017-09-15T17:50:23Z |
dc.date.issued.fl_str_mv |
2017-02-15 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
NICOLAU, Rafaela Fernandes. Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno. 2017. 76 f. Tese (Doutorado - Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, 2017 |
dc.identifier.uri.fl_str_mv |
http://tede.unioeste.br/handle/tede/3057 |
identifier_str_mv |
NICOLAU, Rafaela Fernandes. Índice de vegetação EVI para estimativa de área de milho 2.ª safra e lavouras de inverno. 2017. 76 f. Tese (Doutorado - Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, 2017 |
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http://tede.unioeste.br/handle/tede/3057 |
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Universidade Estadual do Oeste do Paraná Cascavel |
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Programa de Pós-Graduação em Engenharia Agrícola |
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UNIOESTE |
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Brasil |
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Centro de Ciências Exatas e Tecnológicas |
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Universidade Estadual do Oeste do Paraná Cascavel |
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