Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations

Detalhes bibliográficos
Autor(a) principal: Santos, Isabel Carolina de Lima
Data de Publicação: 2019
Outros Autores: Santos, Alexandre dos, Oumar, Zakariyyaa, Soares, Marcus Alvarenga, Silva, Július César Cerqueira, Zanetti, Ronald, Zanuncio, José Cola
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/41489
Resumo: Leaf-cutting ants of the genus Atta are an important insect pest in forest plantations in many countries of South America. The objective of this work was to evaluate the potential for using Landsat-8 images, with medium spatial resolution and distributed free of charge, to detect leaf-cutting ant nests in Tectona grandis plantations in Brazil, using partial least squares discriminant analysis (PLS-DA). The regression model adjusted by PLS-DA selected three principal components with a cross-validation error of 0.275 to map and predict the presence of leaf-cutting ant nests in these plantations. The most important bands and vegetation indices were selected using the main variables in the projection (VIP) and predicted pixels with the presence or absence of leaf-cutting ant nests with an accuracy of 72.3% on an independent validation data set. The study indicates that Landsat-8 OLI images have the potential to detect and map leaf-cutting ant nests in T. grandis plantations.
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spelling Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantationsLandsat-8 OLILeaf-cutting antsMedium resolution imagesMultivariate analysisTectona grandisSensoriamento remotoFormigas cortadeirasImagens de média resoluçãoAnálise multivariadaTecaLeaf-cutting ants of the genus Atta are an important insect pest in forest plantations in many countries of South America. The objective of this work was to evaluate the potential for using Landsat-8 images, with medium spatial resolution and distributed free of charge, to detect leaf-cutting ant nests in Tectona grandis plantations in Brazil, using partial least squares discriminant analysis (PLS-DA). The regression model adjusted by PLS-DA selected three principal components with a cross-validation error of 0.275 to map and predict the presence of leaf-cutting ant nests in these plantations. The most important bands and vegetation indices were selected using the main variables in the projection (VIP) and predicted pixels with the presence or absence of leaf-cutting ant nests with an accuracy of 72.3% on an independent validation data set. The study indicates that Landsat-8 OLI images have the potential to detect and map leaf-cutting ant nests in T. grandis plantations.MDPI Journals2020-06-19T16:40:36Z2020-06-19T16:40:36Z2019-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSANTOS, I. C. de L. et al. Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations. Remote Sensing, [S.I.], v. 11, n. 14, 2019. doi: 10.3390/rs11141641http://repositorio.ufla.br/jspui/handle/1/41489Remote Sensingreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSantos, Isabel Carolina de LimaSantos, Alexandre dosOumar, ZakariyyaaSoares, Marcus AlvarengaSilva, Július César CerqueiraZanetti, RonaldZanuncio, José Colaeng2023-05-09T12:02:22Zoai:localhost:1/41489Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-09T12:02:22Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
title Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
spellingShingle Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
Santos, Isabel Carolina de Lima
Landsat-8 OLI
Leaf-cutting ants
Medium resolution images
Multivariate analysis
Tectona grandis
Sensoriamento remoto
Formigas cortadeiras
Imagens de média resolução
Análise multivariada
Teca
title_short Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
title_full Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
title_fullStr Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
title_full_unstemmed Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
title_sort Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
author Santos, Isabel Carolina de Lima
author_facet Santos, Isabel Carolina de Lima
Santos, Alexandre dos
Oumar, Zakariyyaa
Soares, Marcus Alvarenga
Silva, Július César Cerqueira
Zanetti, Ronald
Zanuncio, José Cola
author_role author
author2 Santos, Alexandre dos
Oumar, Zakariyyaa
Soares, Marcus Alvarenga
Silva, Július César Cerqueira
Zanetti, Ronald
Zanuncio, José Cola
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Santos, Isabel Carolina de Lima
Santos, Alexandre dos
Oumar, Zakariyyaa
Soares, Marcus Alvarenga
Silva, Július César Cerqueira
Zanetti, Ronald
Zanuncio, José Cola
dc.subject.por.fl_str_mv Landsat-8 OLI
Leaf-cutting ants
Medium resolution images
Multivariate analysis
Tectona grandis
Sensoriamento remoto
Formigas cortadeiras
Imagens de média resolução
Análise multivariada
Teca
topic Landsat-8 OLI
Leaf-cutting ants
Medium resolution images
Multivariate analysis
Tectona grandis
Sensoriamento remoto
Formigas cortadeiras
Imagens de média resolução
Análise multivariada
Teca
description Leaf-cutting ants of the genus Atta are an important insect pest in forest plantations in many countries of South America. The objective of this work was to evaluate the potential for using Landsat-8 images, with medium spatial resolution and distributed free of charge, to detect leaf-cutting ant nests in Tectona grandis plantations in Brazil, using partial least squares discriminant analysis (PLS-DA). The regression model adjusted by PLS-DA selected three principal components with a cross-validation error of 0.275 to map and predict the presence of leaf-cutting ant nests in these plantations. The most important bands and vegetation indices were selected using the main variables in the projection (VIP) and predicted pixels with the presence or absence of leaf-cutting ant nests with an accuracy of 72.3% on an independent validation data set. The study indicates that Landsat-8 OLI images have the potential to detect and map leaf-cutting ant nests in T. grandis plantations.
publishDate 2019
dc.date.none.fl_str_mv 2019-07
2020-06-19T16:40:36Z
2020-06-19T16:40:36Z
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 SANTOS, I. C. de L. et al. Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations. Remote Sensing, [S.I.], v. 11, n. 14, 2019. doi: 10.3390/rs11141641
http://repositorio.ufla.br/jspui/handle/1/41489
identifier_str_mv SANTOS, I. C. de L. et al. Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations. Remote Sensing, [S.I.], v. 11, n. 14, 2019. doi: 10.3390/rs11141641
url http://repositorio.ufla.br/jspui/handle/1/41489
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI Journals
publisher.none.fl_str_mv MDPI Journals
dc.source.none.fl_str_mv Remote Sensing
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
_version_ 1784550161637179392

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