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

dc.contributor.authorda Silva Quinaia, Thiago Luiz
dc.contributor.authordo Valle Junior, Renato Farias [UNESP]
dc.contributor.authorde Miranda Coelho, Victor Peçanha
dc.contributor.authorda Cunha, Rafael Carvalho
dc.contributor.authorValera, Carlos Alberto [UNESP]
dc.contributor.authorSanches Fernandes, Luís Filipe [UNESP]
dc.contributor.authorPacheco, Fernando António Leal [UNESP]
dc.contributor.institutionUberaba Campus
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionRegional Coordination of the Environmental Justice Prosecutor's Office of the Paranaíba and Lower Rio Grande River Basins
dc.contributor.institutionUniversity of Trás-os-Montes e Alto Douro
dc.date.accessioned2022-04-28T19:44:20Z
dc.date.available2022-04-28T19:44:20Z
dc.date.issued2021-10-01
dc.description.abstractInadequate 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.en
dc.description.affiliationGeoprocessing Laboratory Federal Institute of Triângulo Mineiro Uberaba Campus
dc.description.affiliationPOLUS—Grupo de Política de Uso do Solo Universidade Estadual Paulista (UNESP)
dc.description.affiliationRegional Coordination of the Environmental Justice Prosecutor's Office of the Paranaíba and Lower Rio Grande River Basins
dc.description.affiliationCenter for Research and Agro-environmental and Biological Technologies University of Trás-os-Montes e Alto Douro
dc.description.affiliationCenter of Chemistry of Vila Real University of Trás-os-Montes e Alto Douro
dc.description.affiliationUnespPOLUS—Grupo de Política de Uso do Solo Universidade Estadual Paulista (UNESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação para a Ciência e a Tecnologia
dc.description.sponsorshipIdCNPq: 307921/2018-2
dc.description.sponsorshipIdFundação para a Ciência e a Tecnologia: UIDB/00616/2020
dc.description.sponsorshipIdFundação para a Ciência e a Tecnologia: UIDB/04033/2020
dc.format.extent4693-4707
dc.identifierhttp://dx.doi.org/10.1002/ldr.4071
dc.identifier.citationLand Degradation and Development, v. 32, n. 16, p. 4693-4707, 2021.
dc.identifier.doi10.1002/ldr.4071
dc.identifier.issn1099-145X
dc.identifier.issn1085-3278
dc.identifier.scopus2-s2.0-85114710050
dc.identifier.urihttp://hdl.handle.net/11449/222388
dc.language.isoeng
dc.relation.ispartofLand Degradation and Development
dc.sourceScopus
dc.subjectGoogle Earth engine
dc.subjectNDVI
dc.subjectpasture degradation
dc.subjectremote sensing
dc.subjecttotal brightness quotient
dc.subjectunmanned aerial vehicles
dc.titleApplication of an improved vegetation index based on the visible spectrum in the diagnosis of degraded pastures: Implications for developmenten
dc.typeArtigo
dspace.entity.typePublication
unesp.author.orcid0000-0003-0774-5788[2]
unesp.author.orcid0000-0003-0024-3304[3]
unesp.author.orcid0000-0002-9486-7160[6]
unesp.author.orcid0000-0002-2399-5261[7]

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