Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/5030 |
Resumo: | Precision Agriculture is constantly concerned with coherent use of natural resources, which makes it key in optimizing management, as it considers the spatial variability of an area and allows its segmentation into subregions (Management Zones – MZ). One of the possible ways to evaluate such variability is by using spectral data, i.e., via orbital, sub-orbital, and terrestrial sensors. Among the suborbital sensors, Unmanned Aerial Vehicles (UAVs) enable non-intrusive surveys with high level of detail and at lower cost. Thus, this work proposes to create MZ focused on temporal analysis on a short period (one harvest) from three datasets: visible vegetation indexes from UAVs and Sentinel-2, and soil data; comparing the MZs with yield maps. In this study, one area was monitored throughout two harvest cycles (soybean, during the 2018/2019 crop, and maize, crop of 2019), collecting UAV images, field data (soil sampling and yield). MZs were designed for the subsets through spectral data (VANT and Sentinel-2) and soil sampling using hierarchical clustering with spatial constraints weights, evaluating different spatial contiguity matrices (Queen, Rook, and KNN). Among the visible vegetation indexes ExG, GLI, GRVI, RGBVI, and VARI, GRVI and VARI achieved higher results. MZs created by UAV or Sentinel-2 for both crops achieved higher performance with two groups; and for soil data, by four groups. None of the MZs shared any similarities regarding their cultures’ productivity. Visible vegetation index achieved poor results for MZ creation, regardless of the sensor employed (VANT or Sentinel-2). |
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Johann, Jerry Adrianihttp://lattes.cnpq.br/3499704308301708Johann, Jerry Adrianihttp://lattes.cnpq.br/3499704308301708Richetti, Jonathanhttp://lattes.cnpq.br/9862888690056331Maggi, Marcio Furlanhttp://lattes.cnpq.br/8677221771738301http://lattes.cnpq.br/5346022545225636Silva, Laíza Cavalcante de Albuquerque2020-10-13T18:22:44Z2020-05-29SILVA, Laíza Cavalcante de Albuquerque. Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo. 2020. 103 f. Dissertação (Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel - PR.http://tede.unioeste.br/handle/tede/5030Precision Agriculture is constantly concerned with coherent use of natural resources, which makes it key in optimizing management, as it considers the spatial variability of an area and allows its segmentation into subregions (Management Zones – MZ). One of the possible ways to evaluate such variability is by using spectral data, i.e., via orbital, sub-orbital, and terrestrial sensors. Among the suborbital sensors, Unmanned Aerial Vehicles (UAVs) enable non-intrusive surveys with high level of detail and at lower cost. Thus, this work proposes to create MZ focused on temporal analysis on a short period (one harvest) from three datasets: visible vegetation indexes from UAVs and Sentinel-2, and soil data; comparing the MZs with yield maps. In this study, one area was monitored throughout two harvest cycles (soybean, during the 2018/2019 crop, and maize, crop of 2019), collecting UAV images, field data (soil sampling and yield). MZs were designed for the subsets through spectral data (VANT and Sentinel-2) and soil sampling using hierarchical clustering with spatial constraints weights, evaluating different spatial contiguity matrices (Queen, Rook, and KNN). Among the visible vegetation indexes ExG, GLI, GRVI, RGBVI, and VARI, GRVI and VARI achieved higher results. MZs created by UAV or Sentinel-2 for both crops achieved higher performance with two groups; and for soil data, by four groups. None of the MZs shared any similarities regarding their cultures’ productivity. Visible vegetation index achieved poor results for MZ creation, regardless of the sensor employed (VANT or Sentinel-2).A Agricultura de Precisão possui contínua preocupação com o uso coerente dos recursos naturais, tornando-se uma peça-chave na otimização do manejo, por considerar a variabilidade espacial da área e segmentá-la em sub-regiões (Zonas de Manejo, ZM). Uma das formas de avaliar esta variabilidade é por dados espectrais, ou seja, por sensores orbitais, suborbitais e terrestres. Dentre os sensores suborbitais, os acoplados aos Veículos Aéreos Não-Tripulados (VANTs) possibilitam o acompanhamento da área de forma não intrusiva, com elevado nível de detalhamento e sob menor custo. Nesse sentido, este trabalho tem como objetivo criar ZM focadas na análise temporal sob curto período (uma safra), por índices de vegetação derivados de imagens de VANT e do sensor Sentinel-2, e por variáveis de solo. Após isso, compará-las com a produtividade. No estudo, monitorou-se uma propriedade agrícola ao longo de dois ciclos produtivos (soja, safra 2018/2019 e trigo, safra 2019), coletando-se dados espectrais na faixa do visível e dados de campo (amostragem de solo e produtividade). Construíram-se ZMs para o subconjunto de dados espectrais (VANT e Sentinel-2) e amostragem do solo pelo método de agrupamento hierárquico com restrição espacial, e foram avaliadas diferentes matrizes de contiguidade (Rainha, Torre e KNN). Dentre os índices de vegetação ExG, GLI, GRVI, RGBVI e VARI, os de melhor desempenho foram o GRVI e VARI. A quantidade ideal de ZMs para os subconjuntos de dados espectrais foi duas para ambas as culturas e quatro para as variáveis do solo. Nenhuma das ZMs se assemelharam à produtividade das culturas. Constatou-se que índices derivados do visível não apresentaram bons resultados para o delineamento das Zonas de Manejo, independente do sensor utilizado (VANT ou Sentinel-2).Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2020-10-13T18:22:44Z No. of bitstreams: 2 Laiza_Albuquerque Silva2020.pdf: 7307143 bytes, checksum: 9f9005ebb1beb36c53a832eab857b14f (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2020-10-13T18:22:44Z (GMT). No. of bitstreams: 2 Laiza_Albuquerque Silva2020.pdf: 7307143 bytes, checksum: 9f9005ebb1beb36c53a832eab857b14f (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2020-05-29Coordenação 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-nc-nd/4.0/info:eu-repo/semantics/openAccessMonitoramento agrícolaÍndice de vegetaçãoagrupamento hierárquicoAgricultural monitoringHierarchical clusteringVegetation indexCIENCIAS AGRARIAS::ENGENHARIA AGRICOLADelineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de soloDesigning of management zones using soil variables, orbital and suborbital imagesinfo: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:UNIOESTEORIGINALLaiza_Albuquerque Silva2020.pdfLaiza_Albuquerque Silva2020.pdfapplication/pdf7307143http://tede.unioeste.br:8080/tede/bitstream/tede/5030/5/Laiza_Albuquerque+Silva2020.pdf9f9005ebb1beb36c53a832eab857b14fMD55CC-LICENSElicense_urllicense_urltext/plain; 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dc.title.por.fl_str_mv |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo |
dc.title.alternative.eng.fl_str_mv |
Designing of management zones using soil variables, orbital and suborbital images |
title |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo |
spellingShingle |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo Silva, Laíza Cavalcante de Albuquerque Monitoramento agrícola Índice de vegetação agrupamento hierárquico Agricultural monitoring Hierarchical clustering Vegetation index CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
title_short |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo |
title_full |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo |
title_fullStr |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo |
title_full_unstemmed |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo |
title_sort |
Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo |
author |
Silva, Laíza Cavalcante de Albuquerque |
author_facet |
Silva, Laíza Cavalcante de Albuquerque |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Johann, Jerry Adriani |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/3499704308301708 |
dc.contributor.referee1.fl_str_mv |
Johann, Jerry Adriani |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/3499704308301708 |
dc.contributor.referee2.fl_str_mv |
Richetti, Jonathan |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/9862888690056331 |
dc.contributor.referee3.fl_str_mv |
Maggi, Marcio Furlan |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/8677221771738301 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/5346022545225636 |
dc.contributor.author.fl_str_mv |
Silva, Laíza Cavalcante de Albuquerque |
contributor_str_mv |
Johann, Jerry Adriani Johann, Jerry Adriani Richetti, Jonathan Maggi, Marcio Furlan |
dc.subject.por.fl_str_mv |
Monitoramento agrícola Índice de vegetação agrupamento hierárquico |
topic |
Monitoramento agrícola Índice de vegetação agrupamento hierárquico Agricultural monitoring Hierarchical clustering Vegetation index CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
dc.subject.eng.fl_str_mv |
Agricultural monitoring Hierarchical clustering Vegetation index |
dc.subject.cnpq.fl_str_mv |
CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
description |
Precision Agriculture is constantly concerned with coherent use of natural resources, which makes it key in optimizing management, as it considers the spatial variability of an area and allows its segmentation into subregions (Management Zones – MZ). One of the possible ways to evaluate such variability is by using spectral data, i.e., via orbital, sub-orbital, and terrestrial sensors. Among the suborbital sensors, Unmanned Aerial Vehicles (UAVs) enable non-intrusive surveys with high level of detail and at lower cost. Thus, this work proposes to create MZ focused on temporal analysis on a short period (one harvest) from three datasets: visible vegetation indexes from UAVs and Sentinel-2, and soil data; comparing the MZs with yield maps. In this study, one area was monitored throughout two harvest cycles (soybean, during the 2018/2019 crop, and maize, crop of 2019), collecting UAV images, field data (soil sampling and yield). MZs were designed for the subsets through spectral data (VANT and Sentinel-2) and soil sampling using hierarchical clustering with spatial constraints weights, evaluating different spatial contiguity matrices (Queen, Rook, and KNN). Among the visible vegetation indexes ExG, GLI, GRVI, RGBVI, and VARI, GRVI and VARI achieved higher results. MZs created by UAV or Sentinel-2 for both crops achieved higher performance with two groups; and for soil data, by four groups. None of the MZs shared any similarities regarding their cultures’ productivity. Visible vegetation index achieved poor results for MZ creation, regardless of the sensor employed (VANT or Sentinel-2). |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-10-13T18:22:44Z |
dc.date.issued.fl_str_mv |
2020-05-29 |
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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masterThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
SILVA, Laíza Cavalcante de Albuquerque. Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo. 2020. 103 f. Dissertação (Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel - PR. |
dc.identifier.uri.fl_str_mv |
http://tede.unioeste.br/handle/tede/5030 |
identifier_str_mv |
SILVA, Laíza Cavalcante de Albuquerque. Delineamento de zonas de manejo por imagens suborbitais, orbitais e variáveis de solo. 2020. 103 f. Dissertação (Programa de Pós-Graduação em Engenharia Agrícola) - Universidade Estadual do Oeste do Paraná, Cascavel - PR. |
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http://tede.unioeste.br/handle/tede/5030 |
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por |
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por |
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600 600 600 600 |
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2214374442868382015 |
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9185445721588761555 |
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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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openAccess |
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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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