Monitoring the understory in eucalyptus plantations using airborne laser scanning

Detalhes bibliográficos
Autor(a) principal: Melo, Alessandra Morais
Data de Publicação: 2021
Outros Autores: Reis, Cristiano Rodrigues, Martins, Bruno Ferraz, Penido, Tamires Mousslech Andrade, Rodriguez, Luiz Carlos Estraviz, Gorgens, Eric Bastos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/183110
Resumo: In eucalyptus plantations, the presence of understory increases the risk of fires, acts as an obstacle to forest operations, and leads to yield losses due to competition. The objective of this study was to develop an approach to discriminate the presence or absence of understory in eucalyptus plantations based on airborne laser scanning surveys. The bimodal canopy height profile was modeled by two Weibull density functions: one to model the canopy, and other to model the understory. The parameters used as predictor in the logistic model successfully discriminated the presence or absence of understory. The logistic model composed by gcanopy, gunderstory, and gunderstory showed higher values of accuracy (0.96) and kappa (0.92), which means an adequate classification of presence of understory and absence of understory. Weibull parameters could be used as input in the logistic regression to effectively identify the presence and absence of understory in eucalyptus plantation.
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spelling Monitoring the understory in eucalyptus plantations using airborne laser scanningLiDARremote sensingweed controlunderstory vegetationIn eucalyptus plantations, the presence of understory increases the risk of fires, acts as an obstacle to forest operations, and leads to yield losses due to competition. The objective of this study was to develop an approach to discriminate the presence or absence of understory in eucalyptus plantations based on airborne laser scanning surveys. The bimodal canopy height profile was modeled by two Weibull density functions: one to model the canopy, and other to model the understory. The parameters used as predictor in the logistic model successfully discriminated the presence or absence of understory. The logistic model composed by gcanopy, gunderstory, and gunderstory showed higher values of accuracy (0.96) and kappa (0.92), which means an adequate classification of presence of understory and absence of understory. Weibull parameters could be used as input in the logistic regression to effectively identify the presence and absence of understory in eucalyptus plantation.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2021-01-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/18311010.1590/1678-992X-2019-0134Scientia Agricola; v. 78 n. 1 (2021); e20190134Scientia Agricola; Vol. 78 Núm. 1 (2021); e20190134Scientia Agricola; Vol. 78 No. 1 (2021); e201901341678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/183110/169823Copyright (c) 2021 Scientia Agricolahttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessMelo, Alessandra Morais Reis, Cristiano Rodrigues Martins, Bruno Ferraz Penido, Tamires Mousslech Andrade Rodriguez, Luiz Carlos Estraviz Gorgens, Eric Bastos 2021-03-12T19:33:26Zoai:revistas.usp.br:article/183110Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2021-03-12T19:33:26Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Monitoring the understory in eucalyptus plantations using airborne laser scanning
title Monitoring the understory in eucalyptus plantations using airborne laser scanning
spellingShingle Monitoring the understory in eucalyptus plantations using airborne laser scanning
Melo, Alessandra Morais
LiDAR
remote sensing
weed control
understory vegetation
title_short Monitoring the understory in eucalyptus plantations using airborne laser scanning
title_full Monitoring the understory in eucalyptus plantations using airborne laser scanning
title_fullStr Monitoring the understory in eucalyptus plantations using airborne laser scanning
title_full_unstemmed Monitoring the understory in eucalyptus plantations using airborne laser scanning
title_sort Monitoring the understory in eucalyptus plantations using airborne laser scanning
author Melo, Alessandra Morais
author_facet Melo, Alessandra Morais
Reis, Cristiano Rodrigues
Martins, Bruno Ferraz
Penido, Tamires Mousslech Andrade
Rodriguez, Luiz Carlos Estraviz
Gorgens, Eric Bastos
author_role author
author2 Reis, Cristiano Rodrigues
Martins, Bruno Ferraz
Penido, Tamires Mousslech Andrade
Rodriguez, Luiz Carlos Estraviz
Gorgens, Eric Bastos
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Melo, Alessandra Morais
Reis, Cristiano Rodrigues
Martins, Bruno Ferraz
Penido, Tamires Mousslech Andrade
Rodriguez, Luiz Carlos Estraviz
Gorgens, Eric Bastos
dc.subject.por.fl_str_mv LiDAR
remote sensing
weed control
understory vegetation
topic LiDAR
remote sensing
weed control
understory vegetation
description In eucalyptus plantations, the presence of understory increases the risk of fires, acts as an obstacle to forest operations, and leads to yield losses due to competition. The objective of this study was to develop an approach to discriminate the presence or absence of understory in eucalyptus plantations based on airborne laser scanning surveys. The bimodal canopy height profile was modeled by two Weibull density functions: one to model the canopy, and other to model the understory. The parameters used as predictor in the logistic model successfully discriminated the presence or absence of understory. The logistic model composed by gcanopy, gunderstory, and gunderstory showed higher values of accuracy (0.96) and kappa (0.92), which means an adequate classification of presence of understory and absence of understory. Weibull parameters could be used as input in the logistic regression to effectively identify the presence and absence of understory in eucalyptus plantation.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-06
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/sa/article/view/183110
10.1590/1678-992X-2019-0134
url https://www.revistas.usp.br/sa/article/view/183110
identifier_str_mv 10.1590/1678-992X-2019-0134
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/183110/169823
dc.rights.driver.fl_str_mv Copyright (c) 2021 Scientia Agricola
http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Scientia Agricola
http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
dc.source.none.fl_str_mv Scientia Agricola; v. 78 n. 1 (2021); e20190134
Scientia Agricola; Vol. 78 Núm. 1 (2021); e20190134
Scientia Agricola; Vol. 78 No. 1 (2021); e20190134
1678-992X
0103-9016
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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