Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
dARK ID: | ark:/38995/001300000bjxp |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/10380 |
Resumo: | The knowledge and the management of land cover and land use are fundamental in a scenario of food production increasing, due to growth of the world population and change in its eating habits, especially the increase in animal protein consumption, what demands an increase in the herd of cattle and, consequently, in the pasture areas. Remote sensing has been an ally of public managers and the community for a long time, with its abundant data production about the terrestrial surface in different spatial, spectral and temporal resolutions, with particular emphasis on vegetation indices. One of these indices, the NDVI, is calculated and made available from the data generated by the MODIS satellite sensor as a time series, which is one of the most used sources of information for the classification of the most varied types of vegetation. n this work, we present a methodology for classifying pastures that comprises, in a first step, the use of the Linear Temporal Mixture Model -- LTMM, with the final members being obtained from an unsupervised classification method. Secondly, the data are labeled from a pasture map produced by the Processing of Images and Geoprocessing Laboratory of the Federal University of Goi\'as (LAPIG - UFG), which has better spatial resolution than the data generated by MODIS. Then, a classification model is constructed to be applied to the classifying data and its quality is measured by comparison with another pasture map, also produced by LAPIG, with the same spatial resolution than the classified data. The methodology used here presented results with quality compatible with other studies that had purely supervised training approaches for the classification of pastures, using the same data base. |
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Laureano, Gustavo Teodorohttp://lattes.cnpq.br/4418446095942420Soares, Anderson da SilvaParente, Leandro LealLaureano, Gustavo Teodorohttp://lattes.cnpq.br/9247638598083574Oliveira, Evando Natal Fernandes de2020-02-27T11:09:02Z2020-01-28OLIVEIRA, Evando Natal Fernandes de. Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI. 2020.87 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020.http://repositorio.bc.ufg.br/tede/handle/tede/10380ark:/38995/001300000bjxpThe knowledge and the management of land cover and land use are fundamental in a scenario of food production increasing, due to growth of the world population and change in its eating habits, especially the increase in animal protein consumption, what demands an increase in the herd of cattle and, consequently, in the pasture areas. Remote sensing has been an ally of public managers and the community for a long time, with its abundant data production about the terrestrial surface in different spatial, spectral and temporal resolutions, with particular emphasis on vegetation indices. One of these indices, the NDVI, is calculated and made available from the data generated by the MODIS satellite sensor as a time series, which is one of the most used sources of information for the classification of the most varied types of vegetation. n this work, we present a methodology for classifying pastures that comprises, in a first step, the use of the Linear Temporal Mixture Model -- LTMM, with the final members being obtained from an unsupervised classification method. Secondly, the data are labeled from a pasture map produced by the Processing of Images and Geoprocessing Laboratory of the Federal University of Goi\'as (LAPIG - UFG), which has better spatial resolution than the data generated by MODIS. Then, a classification model is constructed to be applied to the classifying data and its quality is measured by comparison with another pasture map, also produced by LAPIG, with the same spatial resolution than the classified data. The methodology used here presented results with quality compatible with other studies that had purely supervised training approaches for the classification of pastures, using the same data base.O conhecimento e o gerenciamento da cobertura e do uso do solo são fundamentais num cenário de aumento na produção de alimentos, devido ao crescimento da população mundial e à mudança de seus hábitos alimentares, com destaque para o incremento no consumo de proteína animal, o que demanda um aumento no rebanho bovino e, consequentemente, nas áreas de pastagens. Um aliado dos gestores e da comunidade tem sido, já há um bom tempo, o sensoriamento remoto, com sua produção de dados abundantes sobre a superfície terrestre em variadas resoluções espaciais, espectrais e temporais, com particular destaque para os índices de vegetação. Um destes índices, o NDVI, é calculado e disponibilizado com dados gerados pelo sensor MODIS em forma de uma série temporal, que é uma das fontes de informação mais usadas para a classificação das mais variadas feições vegetais. Neste trabalho, apresentamos uma metodologia de classificação de pastagens que compreende, numa primeira etapa, o uso do Modelo Linear de Mistura Temporal -- MLMT, sendo que os membros finais foram obtidos a partir de uma classificação não supervisionada. Num segundo momento, é feita a rotulação dos dados a partir de um mapa de pastagens que tem melhor resolução espacial que os dados gerados pelo MODIS e que foi produzido pelo Laboratório de Processamento de Imagens e Geoprocessamento da Universidade Federal de Goiás (LAPIG -- UFG). Em seguida, é construído o modelo de classificação que é aplicado aos dados a serem classificados e sua qualidade é aferida por meio da comparação com outro mapa de pastagens, também produzido pelo LAPIG, com resolução espacial semelhante aos dados classificados. A metodologia aqui utilizada apresentou resultados com qualidade compatível com outros trabalhos que tiveram abordagens de treinamento puramente supervisionado para a classificação de pastagens, usando a mesma base de dados.Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2020-02-26T20:32:12Z No. of bitstreams: 2 Dissertação - Evando Natal Fernandes de Oliveira - 2020.pdf: 22399125 bytes, checksum: 21d3a5484ae57ce04b3711c10abf242a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2020-02-27T11:09:01Z (GMT) No. of bitstreams: 2 Dissertação - Evando Natal Fernandes de Oliveira - 2020.pdf: 22399125 bytes, checksum: 21d3a5484ae57ce04b3711c10abf242a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2020-02-27T11:09:02Z (GMT). No. of bitstreams: 2 Dissertação - Evando Natal Fernandes de Oliveira - 2020.pdf: 22399125 bytes, checksum: 21d3a5484ae57ce04b3711c10abf242a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2020-01-28Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfporUniversidade Federal de GoiásPrograma de Pós-graduação em Ciência da Computação (INF)UFGBrasilInstituto de Informática - INF (RG)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessSéries temporaisNDVIPastagensClusterizaçãoClassificação supervisionadaMLMTMLMEMODISTime seriesPastureClusteringSemisupervised classificationLTMMLSMMCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOUma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVIA semi-supervised approach for pastures classification using NDVI time seriesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis-3303550325223384799600600600600-771226673463364476836717112058112045092075167498588264571reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI |
dc.title.alternative.eng.fl_str_mv |
A semi-supervised approach for pastures classification using NDVI time series |
title |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI |
spellingShingle |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI Oliveira, Evando Natal Fernandes de Séries temporais NDVI Pastagens Clusterização Classificação supervisionada MLMT MLME MODIS Time series Pasture Clustering Semisupervised classification LTMM LSMM CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI |
title_full |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI |
title_fullStr |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI |
title_full_unstemmed |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI |
title_sort |
Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI |
author |
Oliveira, Evando Natal Fernandes de |
author_facet |
Oliveira, Evando Natal Fernandes de |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Laureano, Gustavo Teodoro |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4418446095942420 |
dc.contributor.referee1.fl_str_mv |
Soares, Anderson da Silva |
dc.contributor.referee2.fl_str_mv |
Parente, Leandro Leal |
dc.contributor.referee3.fl_str_mv |
Laureano, Gustavo Teodoro |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9247638598083574 |
dc.contributor.author.fl_str_mv |
Oliveira, Evando Natal Fernandes de |
contributor_str_mv |
Laureano, Gustavo Teodoro Soares, Anderson da Silva Parente, Leandro Leal Laureano, Gustavo Teodoro |
dc.subject.por.fl_str_mv |
Séries temporais NDVI Pastagens Clusterização Classificação supervisionada MLMT MLME MODIS |
topic |
Séries temporais NDVI Pastagens Clusterização Classificação supervisionada MLMT MLME MODIS Time series Pasture Clustering Semisupervised classification LTMM LSMM CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Time series Pasture Clustering Semisupervised classification LTMM LSMM |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
The knowledge and the management of land cover and land use are fundamental in a scenario of food production increasing, due to growth of the world population and change in its eating habits, especially the increase in animal protein consumption, what demands an increase in the herd of cattle and, consequently, in the pasture areas. Remote sensing has been an ally of public managers and the community for a long time, with its abundant data production about the terrestrial surface in different spatial, spectral and temporal resolutions, with particular emphasis on vegetation indices. One of these indices, the NDVI, is calculated and made available from the data generated by the MODIS satellite sensor as a time series, which is one of the most used sources of information for the classification of the most varied types of vegetation. n this work, we present a methodology for classifying pastures that comprises, in a first step, the use of the Linear Temporal Mixture Model -- LTMM, with the final members being obtained from an unsupervised classification method. Secondly, the data are labeled from a pasture map produced by the Processing of Images and Geoprocessing Laboratory of the Federal University of Goi\'as (LAPIG - UFG), which has better spatial resolution than the data generated by MODIS. Then, a classification model is constructed to be applied to the classifying data and its quality is measured by comparison with another pasture map, also produced by LAPIG, with the same spatial resolution than the classified data. The methodology used here presented results with quality compatible with other studies that had purely supervised training approaches for the classification of pastures, using the same data base. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-02-27T11:09:02Z |
dc.date.issued.fl_str_mv |
2020-01-28 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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dc.identifier.citation.fl_str_mv |
OLIVEIRA, Evando Natal Fernandes de. Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI. 2020.87 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/10380 |
dc.identifier.dark.fl_str_mv |
ark:/38995/001300000bjxp |
identifier_str_mv |
OLIVEIRA, Evando Natal Fernandes de. Uma abordagem semissupervisionada para classificação de pastagens usando séries temporais de NDVI. 2020.87 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020. ark:/38995/001300000bjxp |
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http://repositorio.bc.ufg.br/tede/handle/tede/10380 |
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por |
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por |
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3671711205811204509 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Universidade Federal de Goiás |
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UFG |
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Brasil |
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Universidade Federal de Goiás |
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