Application of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for development

Detalhes bibliográficos
Autor(a) principal: Quinaia, Thiago Luiz Silva
Data de Publicação: 2021
Outros Autores: Valle Junior, Renato Farias, Coelho, Victor Peçanha Miranda, Cunha, Rafael Carvalho, Valera, Carlos Alberto, Fernandes, Luís Filipe Sanches, Pacheco, Fernando António Leal
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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spelling 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:RCAAP2024-02-02T13:00:38Zoai:repositorio.utad.pt:10348/10857Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:07:10.944415Repositório 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
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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
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dc.relation.none.fl_str_mv DOI: 10.1002/ldr.4071
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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