GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/4470 |
Resumo: | Brazilian agricultural production directly influences the country's economy, with national grain production covering a large part of this sector. In this way, the information of area sown with the main agricultural crops has great value in the planning of logistic actions, in public or private policies of the commodities market. One method to obtain more reliable data from this sector of the economy is with the aid of remote sensing, obtaining data more quickly and efficiently. However, in order to work with remote sensing, a high computational capacity is required for data processing. To overcome this problem, the use of cloud data processing in the Google Earth Engine (GEE) platform was employed, which is available to users free of charge, has been used to perform various activities related to orbital remote sensing. Thus, the objective of this work was to map the main summer and winter crops in the state of Paraná, for the 2016/2017 and 2017/2018 harvest years, using the GEE platform. For this purpose, images of the OLI and MSI sensors, images of the digital elevation model (SRTM), false color mosaic segmentation of the sensor images and the Naive Bayes Continuous algorithm as classification methods were used. The mapping process was parameterized separately for each of the 39 microregions of the state. Finally, the crop areas (soybean, 1st and 2nd maize and winter crops) were quantified by municipality and compared with official data and field data. The accuracy of the summer mapping resulted in Global Accuracy ranging from 87.6% (Maize, 2017/2018) to 96.7% (Soybean, 2016/2017), with a kappa index (K) between 72% (Maize, 2017/2018 crop year) and 91% (Soybean, 2016/2017). The linear correlation (r) between the mapped area and the official area per municipality was 0.92 and the agreement index dr = 0.81 for the soybean crop and for the 1st crop corn yielded r = 0.59 and dr = 0.53. For the mapping of 2nd crop maize and winter crops, the Global Accuracy varied between 95% (winter crops, 2018 crop year) and 96.7% (Maize 2nd crop, 2017 crop year), with index kappa (K) between 90% (winter crops, 2018 crop year) and 92% (Maize 2nd crop, 2017 crop year). Between the mapped area and the official area per municipality, r = 0.95 and dr = 0.83 for maize 2ndcrop and r = 0.78 and dr = 0.76 for winter crops were obtained. The linear (r) correlation between the mapped area and the field data was 0.96 and the agreement index dr = 0.86 for the 1st crop corn and for the soybean yield r = 0.96 and p = 0.92 for the crop year 2017/2018, r = 0.79 and dr = 0.71 for maize 2nd crop r = 0.80 and dr = 0.72 for winter crops in the crop year 2017, r = 0.88 and dr = 0.86 for maize crop 2nd crop er = 0.71 and dr = 0.78 for winter crops in the crop year 2018. The mapping of areas with agricultural crops carried out with the GHG platform can be carried out quickly, accurately and efficiently. Through the mappings it is possible to have the spatial distribution of crops per crop plot, as well as the quantification of areas by area of coverage of a company, municipality, microregion, mesoregion and for every state. |
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Johann, Jerry Adrianihttp://lattes.cnpq.br/34997043083017Johann, Jerry Adrianihttp://lattes.cnpq.br/34997043083017Grzegozewski, Denise Mariahttp://lattes.cnpq.br/8302784025687552Mercante, Eriveltohttp://lattes.cnpq.br/4061800207647478http://lattes.cnpq.br/3065593150601602Paludo, Alex2019-09-19T18:05:43Z2019-02-11PALUDO, Alex. GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná. 2019. 75 f. Dissertação( Mestrado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, 2019.http://tede.unioeste.br/handle/tede/4470Brazilian agricultural production directly influences the country's economy, with national grain production covering a large part of this sector. In this way, the information of area sown with the main agricultural crops has great value in the planning of logistic actions, in public or private policies of the commodities market. One method to obtain more reliable data from this sector of the economy is with the aid of remote sensing, obtaining data more quickly and efficiently. However, in order to work with remote sensing, a high computational capacity is required for data processing. To overcome this problem, the use of cloud data processing in the Google Earth Engine (GEE) platform was employed, which is available to users free of charge, has been used to perform various activities related to orbital remote sensing. Thus, the objective of this work was to map the main summer and winter crops in the state of Paraná, for the 2016/2017 and 2017/2018 harvest years, using the GEE platform. For this purpose, images of the OLI and MSI sensors, images of the digital elevation model (SRTM), false color mosaic segmentation of the sensor images and the Naive Bayes Continuous algorithm as classification methods were used. The mapping process was parameterized separately for each of the 39 microregions of the state. Finally, the crop areas (soybean, 1st and 2nd maize and winter crops) were quantified by municipality and compared with official data and field data. The accuracy of the summer mapping resulted in Global Accuracy ranging from 87.6% (Maize, 2017/2018) to 96.7% (Soybean, 2016/2017), with a kappa index (K) between 72% (Maize, 2017/2018 crop year) and 91% (Soybean, 2016/2017). The linear correlation (r) between the mapped area and the official area per municipality was 0.92 and the agreement index dr = 0.81 for the soybean crop and for the 1st crop corn yielded r = 0.59 and dr = 0.53. For the mapping of 2nd crop maize and winter crops, the Global Accuracy varied between 95% (winter crops, 2018 crop year) and 96.7% (Maize 2nd crop, 2017 crop year), with index kappa (K) between 90% (winter crops, 2018 crop year) and 92% (Maize 2nd crop, 2017 crop year). Between the mapped area and the official area per municipality, r = 0.95 and dr = 0.83 for maize 2ndcrop and r = 0.78 and dr = 0.76 for winter crops were obtained. The linear (r) correlation between the mapped area and the field data was 0.96 and the agreement index dr = 0.86 for the 1st crop corn and for the soybean yield r = 0.96 and p = 0.92 for the crop year 2017/2018, r = 0.79 and dr = 0.71 for maize 2nd crop r = 0.80 and dr = 0.72 for winter crops in the crop year 2017, r = 0.88 and dr = 0.86 for maize crop 2nd crop er = 0.71 and dr = 0.78 for winter crops in the crop year 2018. The mapping of areas with agricultural crops carried out with the GHG platform can be carried out quickly, accurately and efficiently. Through the mappings it is possible to have the spatial distribution of crops per crop plot, as well as the quantification of areas by area of coverage of a company, municipality, microregion, mesoregion and for every state.A produção agropecuária brasileira influencia diretamente na economia do país, sendo que a produção nacional de grãos abrange boa parte desse setor. Desta forma, a informação de área semeada com as principais culturas agrícolas tem grande valor no planejamento de ações logísticas, em políticas públicas ou privadas do mercado de commodities. Uma forma de obter dados mais confiáveis desse setor da economia é com o auxílio do sensoriamento remoto, obtendo dados de maneira mais rápida e eficiente. Entretanto, para se trabalhar com sensoriamento remoto, se torna necessário uma alta capacidade computacional para o processamento dos dados. Para contornar este problema, tem sido utilizado o processamento de dados em nuvem na plataforma Google Earth Engine (GEE), que está disponível de forma gratuita aos usuários e que permite a realização de diversas atividades ligadas ao sensoriamento remoto orbital. Assim, o objetivo deste trabalho foi mapear as principais culturas agrícolas de verão e de inverno no estado do Paraná, para os anos-safra 2016/2017 e 2017/2018, utilizando a plataforma GEE. Para tanto, foram utilizadas imagens dos sensores OLI e MSI, imagens do modelo digital de elevação (SRTM), processo de segmentação no mosaico falsa cor das imagens dos sensores e o algoritmo Continuous Naive Bayes como métodos de classificação. O processo de mapeamento foi parametrizado separadamente para cada uma das 39 microrregiões do estado. Por fim, quantificaram-se as áreas das culturas (soja, milho 1ª e 2ª safras e culturas de inverno) por município e compararam-se estas com dados oficiais e dados de campo. A acurácia dos mapeamentos de verão resultou em Exatidão Global (EG) variando entre 87,6% (Milho, ano-safra 2017/2018) a 96,7% (Soja, ano-safra 2016/2017), com índice kappa (K) entre 72% (Milho, ano-safra 2017/2018) e 91% (Soja, ano-safra 2016/2017). A correlação linear (r) entre a área mapeada e a oficial por município foi de 0,92 e o índice de concordância dr = 0,81 para a cultura da soja; para o milho 1ª safra obtiveram-se r = 0,59 e dr = 0,53. Para os mapeamentos de milho 2ª safra e culturas de inverno, a EG variou entre 95% (culturas de inverno, ano-safra 2018) e 96,7% (Milho 2ª safra, ano-safra 2017), com K entre 90% (culturas de inverno, ano-safra 2018) e 92% (Milho 2ª safra, ano-safra 2017). Entre a área mapeada e a oficial por município obtiveram-se r = 0,95 e dr = 0,83 para a cultura do milho 2ª safra, e r = 0,78 e dr = 0,76 para as culturas de inverno. Na comparação entre a área mapeada e os dados de campo obtiveram-se r = 0,96 e dr = 0,86 para a cultura do milho 1ª safra; e r = 0,96 e dr = 0,92 para a soja, no ano-safra 2017/2018. Para o milho 2ª safra obtiveram-se r = 0,79 e dr = 0,71; e r = 0,80 com dr = 0,72 para as culturas de inverno no ano safra 2017. No ano-safra 2018, para milho 2ª safra obtiveram-se r = 0,88 e dr = 0,86; e r = 0,71 com dr = 0,78 para as culturas de inverno. O mapeamento de áreas com culturas agrícolas realizado com a plataforma GEE pode ser realizado forma rápida, precisa e eficiente. Pelos mapeamentos é possível ter-se a distribuição espacial das culturas por talhão das lavouras, bem como a quantificação de áreas por área de abrangência de uma empresa, município, microrregião, mesorregião e para todo estado.Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2019-09-19T18:05:43Z No. of bitstreams: 2 Alex_Paludo_2019.pdf: 3937200 bytes, checksum: 6d630b4137f272b30f9a821ebbccfd83 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2019-09-19T18:05:43Z (GMT). No. of bitstreams: 2 Alex_Paludo_2019.pdf: 3937200 bytes, checksum: 6d630b4137f272b30f9a821ebbccfd83 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2019-02-11Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfpor6588633818200016417500Universidade Estadual do Oeste do ParanáCascavelPrograma de Pós-Graduação em Engenharia AgrícolaUNIOESTEBrasilCentro de Ciências Exatas e Tecnológicashttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessModelo Digital de ElevaçãoSegmentaçãoContinuous Naive BayesSensoriamento RemotoLandsat-8Sentinel-2CIENCIAS AGRARIAS::ENGENHARIA AGRICOLAGOOGLE earth engine para mapeamento de culturas agrícolas no ParanáMapping agricultural cultures in the state of paraná with the online platform google earth engineinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis-5347692450416052129600600600600221437444286838201591854457215887615552075167498588264571reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALAlex_Paludo_2019.pdfAlex_Paludo_2019.pdfapplication/pdf3937200http://tede.unioeste.br:8080/tede/bitstream/tede/4470/5/Alex_Paludo_2019.pdf6d630b4137f272b30f9a821ebbccfd83MD55CC-LICENSElicense_urllicense_urltext/plain; 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dc.title.por.fl_str_mv |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná |
dc.title.alternative.eng.fl_str_mv |
Mapping agricultural cultures in the state of paraná with the online platform google earth engine |
title |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná |
spellingShingle |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná Paludo, Alex Modelo Digital de Elevação Segmentação Continuous Naive Bayes Sensoriamento Remoto Landsat-8 Sentinel-2 CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
title_short |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná |
title_full |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná |
title_fullStr |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná |
title_full_unstemmed |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná |
title_sort |
GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná |
author |
Paludo, Alex |
author_facet |
Paludo, Alex |
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 |
Grzegozewski, Denise Maria |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/8302784025687552 |
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/3065593150601602 |
dc.contributor.author.fl_str_mv |
Paludo, Alex |
contributor_str_mv |
Johann, Jerry Adriani Johann, Jerry Adriani Grzegozewski, Denise Maria Mercante, Erivelto |
dc.subject.por.fl_str_mv |
Modelo Digital de Elevação Segmentação Continuous Naive Bayes Sensoriamento Remoto Landsat-8 Sentinel-2 |
topic |
Modelo Digital de Elevação Segmentação Continuous Naive Bayes Sensoriamento Remoto Landsat-8 Sentinel-2 CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
dc.subject.cnpq.fl_str_mv |
CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
description |
Brazilian agricultural production directly influences the country's economy, with national grain production covering a large part of this sector. In this way, the information of area sown with the main agricultural crops has great value in the planning of logistic actions, in public or private policies of the commodities market. One method to obtain more reliable data from this sector of the economy is with the aid of remote sensing, obtaining data more quickly and efficiently. However, in order to work with remote sensing, a high computational capacity is required for data processing. To overcome this problem, the use of cloud data processing in the Google Earth Engine (GEE) platform was employed, which is available to users free of charge, has been used to perform various activities related to orbital remote sensing. Thus, the objective of this work was to map the main summer and winter crops in the state of Paraná, for the 2016/2017 and 2017/2018 harvest years, using the GEE platform. For this purpose, images of the OLI and MSI sensors, images of the digital elevation model (SRTM), false color mosaic segmentation of the sensor images and the Naive Bayes Continuous algorithm as classification methods were used. The mapping process was parameterized separately for each of the 39 microregions of the state. Finally, the crop areas (soybean, 1st and 2nd maize and winter crops) were quantified by municipality and compared with official data and field data. The accuracy of the summer mapping resulted in Global Accuracy ranging from 87.6% (Maize, 2017/2018) to 96.7% (Soybean, 2016/2017), with a kappa index (K) between 72% (Maize, 2017/2018 crop year) and 91% (Soybean, 2016/2017). The linear correlation (r) between the mapped area and the official area per municipality was 0.92 and the agreement index dr = 0.81 for the soybean crop and for the 1st crop corn yielded r = 0.59 and dr = 0.53. For the mapping of 2nd crop maize and winter crops, the Global Accuracy varied between 95% (winter crops, 2018 crop year) and 96.7% (Maize 2nd crop, 2017 crop year), with index kappa (K) between 90% (winter crops, 2018 crop year) and 92% (Maize 2nd crop, 2017 crop year). Between the mapped area and the official area per municipality, r = 0.95 and dr = 0.83 for maize 2ndcrop and r = 0.78 and dr = 0.76 for winter crops were obtained. The linear (r) correlation between the mapped area and the field data was 0.96 and the agreement index dr = 0.86 for the 1st crop corn and for the soybean yield r = 0.96 and p = 0.92 for the crop year 2017/2018, r = 0.79 and dr = 0.71 for maize 2nd crop r = 0.80 and dr = 0.72 for winter crops in the crop year 2017, r = 0.88 and dr = 0.86 for maize crop 2nd crop er = 0.71 and dr = 0.78 for winter crops in the crop year 2018. The mapping of areas with agricultural crops carried out with the GHG platform can be carried out quickly, accurately and efficiently. Through the mappings it is possible to have the spatial distribution of crops per crop plot, as well as the quantification of areas by area of coverage of a company, municipality, microregion, mesoregion and for every state. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-09-19T18:05:43Z |
dc.date.issued.fl_str_mv |
2019-02-11 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
PALUDO, Alex. GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná. 2019. 75 f. Dissertação( Mestrado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, 2019. |
dc.identifier.uri.fl_str_mv |
http://tede.unioeste.br/handle/tede/4470 |
identifier_str_mv |
PALUDO, Alex. GOOGLE earth engine para mapeamento de culturas agrícolas no Paraná. 2019. 75 f. Dissertação( Mestrado em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel, 2019. |
url |
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