Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands

Detalhes bibliográficos
Autor(a) principal: Oliveira,Ivy Mayara Sanches de
Data de Publicação: 2018
Outros Autores: Silveira,Eduarda Martiniano de Oliveira, Paiva,Lara de, Acerbi Júnior,Fausto Weimar, Mello,José Marcio de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Floresta e Ambiente
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872018000300127
Resumo: ABSTRACT Brazil has many rural properties with unmanaged eucalyptus stands. These plantations are heterogeneous, presenting different tree sizes, advanced ages, and large wood volumes that can be quantified using forest inventories. The prediction error of dendrometric variables, mainly in highly heterogeneous areas, can be associated with inadequate forest inventory procedures, i.e. low intensity of sampling plots. However, a larger number of plots increases the cost of inventorying. Therefore, a promising alternative is forest stratification into homogeneous sub areas. Accordingly, the aim of this study was to analyze the reduction of volume estimate errors by post-stratification procedures. We used the normalized difference vegetation index (NDVI) derived from Landsat 8 and Spot 6 images and geostatistical techniques, such as kriging the volume (V) and diameter at breast height (DBH). The most precise method to estimate the total volume was the stratified random sampling (STS), based on geostatistical interpolation, using the DBH (error lower than 10%).
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spelling Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Standsforest inventorykrigingNDVIABSTRACT Brazil has many rural properties with unmanaged eucalyptus stands. These plantations are heterogeneous, presenting different tree sizes, advanced ages, and large wood volumes that can be quantified using forest inventories. The prediction error of dendrometric variables, mainly in highly heterogeneous areas, can be associated with inadequate forest inventory procedures, i.e. low intensity of sampling plots. However, a larger number of plots increases the cost of inventorying. Therefore, a promising alternative is forest stratification into homogeneous sub areas. Accordingly, the aim of this study was to analyze the reduction of volume estimate errors by post-stratification procedures. We used the normalized difference vegetation index (NDVI) derived from Landsat 8 and Spot 6 images and geostatistical techniques, such as kriging the volume (V) and diameter at breast height (DBH). The most precise method to estimate the total volume was the stratified random sampling (STS), based on geostatistical interpolation, using the DBH (error lower than 10%).Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872018000300127Floresta e Ambiente v.25 n.3 2018reponame:Floresta e Ambienteinstname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJ10.1590/2179-8087.058616info:eu-repo/semantics/openAccessOliveira,Ivy Mayara Sanches deSilveira,Eduarda Martiniano de OliveiraPaiva,Lara deAcerbi Júnior,Fausto WeimarMello,José Marcio deeng2018-07-27T00:00:00Zoai:scielo:S2179-80872018000300127Revistahttps://www.floram.org/PUBhttps://old.scielo.br/oai/scielo-oai.phpfloramjournal@gmail.com||floram@ufrrj.br||2179-80871415-0980opendoar:2018-07-27T00:00Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
title Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
spellingShingle Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
Oliveira,Ivy Mayara Sanches de
forest inventory
kriging
NDVI
title_short Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
title_full Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
title_fullStr Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
title_full_unstemmed Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
title_sort Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
author Oliveira,Ivy Mayara Sanches de
author_facet Oliveira,Ivy Mayara Sanches de
Silveira,Eduarda Martiniano de Oliveira
Paiva,Lara de
Acerbi Júnior,Fausto Weimar
Mello,José Marcio de
author_role author
author2 Silveira,Eduarda Martiniano de Oliveira
Paiva,Lara de
Acerbi Júnior,Fausto Weimar
Mello,José Marcio de
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Oliveira,Ivy Mayara Sanches de
Silveira,Eduarda Martiniano de Oliveira
Paiva,Lara de
Acerbi Júnior,Fausto Weimar
Mello,José Marcio de
dc.subject.por.fl_str_mv forest inventory
kriging
NDVI
topic forest inventory
kriging
NDVI
description ABSTRACT Brazil has many rural properties with unmanaged eucalyptus stands. These plantations are heterogeneous, presenting different tree sizes, advanced ages, and large wood volumes that can be quantified using forest inventories. The prediction error of dendrometric variables, mainly in highly heterogeneous areas, can be associated with inadequate forest inventory procedures, i.e. low intensity of sampling plots. However, a larger number of plots increases the cost of inventorying. Therefore, a promising alternative is forest stratification into homogeneous sub areas. Accordingly, the aim of this study was to analyze the reduction of volume estimate errors by post-stratification procedures. We used the normalized difference vegetation index (NDVI) derived from Landsat 8 and Spot 6 images and geostatistical techniques, such as kriging the volume (V) and diameter at breast height (DBH). The most precise method to estimate the total volume was the stratified random sampling (STS), based on geostatistical interpolation, using the DBH (error lower than 10%).
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872018000300127
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872018000300127
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2179-8087.058616
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro
publisher.none.fl_str_mv Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro
dc.source.none.fl_str_mv Floresta e Ambiente v.25 n.3 2018
reponame:Floresta e Ambiente
instname:Universidade Federal do Rio de Janeiro (UFRJ)
instacron:UFRJ
instname_str Universidade Federal do Rio de Janeiro (UFRJ)
instacron_str UFRJ
institution UFRJ
reponame_str Floresta e Ambiente
collection Floresta e Ambiente
repository.name.fl_str_mv Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)
repository.mail.fl_str_mv floramjournal@gmail.com||floram@ufrrj.br||
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