Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations

Detalhes bibliográficos
Autor(a) principal: Silva, André Gracioso Peres
Data de Publicação: 2015
Outros Autores: Görgens, Eric Bastos, Campoe, Otávio Camargo, Alvares, Clayton Alcarde, Stape, José Luiz, Rodriguez, Luiz Carlos Estraviz
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/106684
Resumo: This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R2 = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj.R2 = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively).
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spelling Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R2 = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj.R2 = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively). Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2015-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/10668410.1590/0103-9016-2015-0070Scientia Agricola; v. 72 n. 6 (2015); 504-512Scientia Agricola; Vol. 72 Núm. 6 (2015); 504-512Scientia Agricola; Vol. 72 No. 6 (2015); 504-5121678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/106684/105301Copyright (c) 2015 Scientia Agricolainfo:eu-repo/semantics/openAccessSilva, André Gracioso PeresGörgens, Eric BastosCampoe, Otávio CamargoAlvares, Clayton AlcardeStape, José LuizRodriguez, Luiz Carlos Estraviz2015-10-27T16:19:02Zoai:revistas.usp.br:article/106684Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2015-10-27T16:19:02Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
title Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
spellingShingle Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
Silva, André Gracioso Peres
title_short Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
title_full Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
title_fullStr Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
title_full_unstemmed Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
title_sort Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
author Silva, André Gracioso Peres
author_facet Silva, André Gracioso Peres
Görgens, Eric Bastos
Campoe, Otávio Camargo
Alvares, Clayton Alcarde
Stape, José Luiz
Rodriguez, Luiz Carlos Estraviz
author_role author
author2 Görgens, Eric Bastos
Campoe, Otávio Camargo
Alvares, Clayton Alcarde
Stape, José Luiz
Rodriguez, Luiz Carlos Estraviz
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Silva, André Gracioso Peres
Görgens, Eric Bastos
Campoe, Otávio Camargo
Alvares, Clayton Alcarde
Stape, José Luiz
Rodriguez, Luiz Carlos Estraviz
description This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R2 = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj.R2 = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively).
publishDate 2015
dc.date.none.fl_str_mv 2015-12-01
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/106684
10.1590/0103-9016-2015-0070
url https://www.revistas.usp.br/sa/article/view/106684
identifier_str_mv 10.1590/0103-9016-2015-0070
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/106684/105301
dc.rights.driver.fl_str_mv Copyright (c) 2015 Scientia Agricola
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2015 Scientia Agricola
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. 72 n. 6 (2015); 504-512
Scientia Agricola; Vol. 72 Núm. 6 (2015); 504-512
Scientia Agricola; Vol. 72 No. 6 (2015); 504-512
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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