Remote sensing to detect nests of the leaf-cutting ant atta sexdens (Hymenoptera: Formicidae) in teak plantations
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , |
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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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_ |
1807835155286458368 |