Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10348/10857 |
Resumo: | Inadequate pasture management causes land degradation through reduction of grass, increased presence of invasive plants or pests, compaction, erosion, and nutrient deficiency. The recognition of pasture degradation is therefore essential. Remote sensing satellite systems allow us to do so at regional-to global scales. A struggle is in progress nowadays is to improve detection accuracy and implement high-resolution surveys at farm scales using low-cost unmanned aerial vehicles (UAVs). The pasture imagery can be translated into maps of degraded pasture using the popular NDVI as diagnostic parameter, but their generation using a UAV requires a high-cost NIR sensor, while the struggle is to use low-cost UAVs equipped with RGB cameras. The first step to recognize degraded pastures using RGB cameras is to define a suitable vegetation index. Thus, the purpose of this study was to present the total brightness quotient of red (TBQR), green (TBQG), and blue (TBQB) bands. The test to the index resorted to LANDSAT-8 satellite images captured over the environmental protection area of Uberaba River basin (Minas Gerais, Brazil) in the 2017–2019 period. The images were not captured by a UAV because the equipment was not then available. The results were promising given the large detection accuracy (88.63%) of the TBQG and the high (0.965) correlation between TBQG and NDVI. Besides, the TBQ-based areas of degraded pasture (17,486.3–25,180.1 hectares) were larger than the NDVI counterparts (12,066.9 hectares). This is an additional reason to oversight degraded pastures based on the TBQs, as they seek for improved environmental compliance and economic development |
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Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for developmentGoogle Earth engineNDVIInadequate pasture management causes land degradation through reduction of grass, increased presence of invasive plants or pests, compaction, erosion, and nutrient deficiency. The recognition of pasture degradation is therefore essential. Remote sensing satellite systems allow us to do so at regional-to global scales. A struggle is in progress nowadays is to improve detection accuracy and implement high-resolution surveys at farm scales using low-cost unmanned aerial vehicles (UAVs). The pasture imagery can be translated into maps of degraded pasture using the popular NDVI as diagnostic parameter, but their generation using a UAV requires a high-cost NIR sensor, while the struggle is to use low-cost UAVs equipped with RGB cameras. The first step to recognize degraded pastures using RGB cameras is to define a suitable vegetation index. Thus, the purpose of this study was to present the total brightness quotient of red (TBQR), green (TBQG), and blue (TBQB) bands. The test to the index resorted to LANDSAT-8 satellite images captured over the environmental protection area of Uberaba River basin (Minas Gerais, Brazil) in the 2017–2019 period. The images were not captured by a UAV because the equipment was not then available. The results were promising given the large detection accuracy (88.63%) of the TBQG and the high (0.965) correlation between TBQG and NDVI. Besides, the TBQ-based areas of degraded pasture (17,486.3–25,180.1 hectares) were larger than the NDVI counterparts (12,066.9 hectares). This is an additional reason to oversight degraded pastures based on the TBQs, as they seek for improved environmental compliance and economic development2021-12-09T16:20:04Z2021-08-12T00:00:00Z2021-08-12info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10348/10857engDOI: 10.1002/ldr.4071Quinaia, Thiago Luiz SilvaValle Junior, Renato FariasCoelho, Victor Peçanha MirandaCunha, Rafael CarvalhoValera, Carlos AlbertoFernandes, Luís Filipe SanchesPacheco, Fernando António Lealinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-24T05:07:10Zoai:repositorio.utad.pt:10348/10857Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-03-24T05:07:10Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development |
title |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development |
spellingShingle |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development Quinaia, Thiago Luiz Silva Google Earth engine NDVI |
title_short |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development |
title_full |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development |
title_fullStr |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development |
title_full_unstemmed |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development |
title_sort |
Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development |
author |
Quinaia, Thiago Luiz Silva |
author_facet |
Quinaia, Thiago Luiz Silva Valle Junior, Renato Farias Coelho, Victor Peçanha Miranda Cunha, Rafael Carvalho Valera, Carlos Alberto Fernandes, Luís Filipe Sanches Pacheco, Fernando António Leal |
author_role |
author |
author2 |
Valle Junior, Renato Farias Coelho, Victor Peçanha Miranda Cunha, Rafael Carvalho Valera, Carlos Alberto Fernandes, Luís Filipe Sanches Pacheco, Fernando António Leal |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Quinaia, Thiago Luiz Silva Valle Junior, Renato Farias Coelho, Victor Peçanha Miranda Cunha, Rafael Carvalho Valera, Carlos Alberto Fernandes, Luís Filipe Sanches Pacheco, Fernando António Leal |
dc.subject.por.fl_str_mv |
Google Earth engine NDVI |
topic |
Google Earth engine NDVI |
description |
Inadequate pasture management causes land degradation through reduction of grass, increased presence of invasive plants or pests, compaction, erosion, and nutrient deficiency. The recognition of pasture degradation is therefore essential. Remote sensing satellite systems allow us to do so at regional-to global scales. A struggle is in progress nowadays is to improve detection accuracy and implement high-resolution surveys at farm scales using low-cost unmanned aerial vehicles (UAVs). The pasture imagery can be translated into maps of degraded pasture using the popular NDVI as diagnostic parameter, but their generation using a UAV requires a high-cost NIR sensor, while the struggle is to use low-cost UAVs equipped with RGB cameras. The first step to recognize degraded pastures using RGB cameras is to define a suitable vegetation index. Thus, the purpose of this study was to present the total brightness quotient of red (TBQR), green (TBQG), and blue (TBQB) bands. The test to the index resorted to LANDSAT-8 satellite images captured over the environmental protection area of Uberaba River basin (Minas Gerais, Brazil) in the 2017–2019 period. The images were not captured by a UAV because the equipment was not then available. The results were promising given the large detection accuracy (88.63%) of the TBQG and the high (0.965) correlation between TBQG and NDVI. Besides, the TBQ-based areas of degraded pasture (17,486.3–25,180.1 hectares) were larger than the NDVI counterparts (12,066.9 hectares). This is an additional reason to oversight degraded pastures based on the TBQs, as they seek for improved environmental compliance and economic development |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-09T16:20:04Z 2021-08-12T00:00:00Z 2021-08-12 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10348/10857 |
url |
http://hdl.handle.net/10348/10857 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
DOI: 10.1002/ldr.4071 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817543198669537280 |