MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL

Detalhes bibliográficos
Autor(a) principal: Käfer, Pâmela Suélen
Data de Publicação: 2018
Outros Autores: Rex, Franciel Eduardo, Breunig, Fábio Marcelo, Balbinot, Rafaelo
Tipo de documento: Artigo
Idioma: por
eng
Título da fonte: Boletim de Ciências Geodésicas
Texto Completo: https://revistas.ufpr.br/bcg/article/view/61517
Resumo: Remote sensing data are a key proxy to forest monitoring and management at local, regional and global scales. Considering the hypothesis that NDVI and EVI can be used at least during one decade to monitor Pinus elliottii in Southern Brazil, the objective of this study was to identify saturation time after planting of these vegetation indices in a Pinus elliottii plantation and the most suitable index by adjusting theoretical functions to each one of them. Based on Landsat Surface Reflectance Higher-Level Data Products, 32 scenes were selected between 1984 to 2015. A set of theoretical polynomial, gaussian and logistic mathematical functions were applied to fit the experimental data on vegetation indices. The determination coefficient (R²) and RMSE at 95% probability were also used. Finally, EVI efficiency was tested by changing the L parameter. The logistic model was the one that best explained the data resulting from NDVI and EVI over time. NDVI was more effective than EVI for this forest monitoring, identifying the forest growth pattern until its 18 years of age. EVI may have been saturated after 14 years and the L factor may be set to near to zero to achieve a higher coefficient of determination.
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spelling MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZILMODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZILGeociências; GeodésiaForest production; Remote sensing; Curve fitting; Time seriesGeociências; GeodésiaForest production; Remote sensing; Curve fitting; Time seriesRemote sensing data are a key proxy to forest monitoring and management at local, regional and global scales. Considering the hypothesis that NDVI and EVI can be used at least during one decade to monitor Pinus elliottii in Southern Brazil, the objective of this study was to identify saturation time after planting of these vegetation indices in a Pinus elliottii plantation and the most suitable index by adjusting theoretical functions to each one of them. Based on Landsat Surface Reflectance Higher-Level Data Products, 32 scenes were selected between 1984 to 2015. A set of theoretical polynomial, gaussian and logistic mathematical functions were applied to fit the experimental data on vegetation indices. The determination coefficient (R²) and RMSE at 95% probability were also used. Finally, EVI efficiency was tested by changing the L parameter. The logistic model was the one that best explained the data resulting from NDVI and EVI over time. NDVI was more effective than EVI for this forest monitoring, identifying the forest growth pattern until its 18 years of age. EVI may have been saturated after 14 years and the L factor may be set to near to zero to achieve a higher coefficient of determination.Remote sensing data are a key proxy to forest monitoring and management at local, regional and global scales. Considering the hypothesis that NDVI and EVI can be used at least during one decade to monitor Pinus elliottii in Southern Brazil, the objective of this study was to identify saturation time after planting of these vegetation indices in a Pinus elliottii plantation and the most suitable index by adjusting theoretical functions to each one of them. Based on Landsat Surface Reflectance Higher-Level Data Products, 32 scenes were selected between 1984 to 2015. A set of theoretical polynomial, gaussian and logistic mathematical functions were applied to fit the experimental data on vegetation indices. The determination coefficient (R²) and RMSE at 95% probability were also used. Finally, EVI efficiency was tested by changing the L parameter. The logistic model was the one that best explained the data resulting from NDVI and EVI over time. NDVI was more effective than EVI for this forest monitoring, identifying the forest growth pattern until its 18 years of age. EVI may have been saturated after 14 years and the L factor may be set to near to zero to achieve a higher coefficient of determination.Boletim de Ciências GeodésicasBulletin of Geodetic SciencesCNPqCNPqKäfer, Pâmela SuélenRex, Franciel EduardoBreunig, Fábio MarceloBalbinot, Rafaelo2018-09-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://revistas.ufpr.br/bcg/article/view/61517Boletim de Ciências Geodésicas; Vol 24, No 3 (2018)Bulletin of Geodetic Sciences; Vol 24, No 3 (2018)1982-21701413-4853reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRporenghttps://revistas.ufpr.br/bcg/article/view/61517/36075https://revistas.ufpr.br/bcg/article/view/61517/36076Copyright (c) 2018 Pâmela Suélen Käfer, Franciel Eduardo Rex, Fábio Marcelo Breunig, Rafaelo Balbinothttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccess2018-09-13T18:54:59Zoai:revistas.ufpr.br:article/61517Revistahttps://revistas.ufpr.br/bcgPUBhttps://revistas.ufpr.br/bcg/oaiqdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br1982-21701413-4853opendoar:2018-09-13T18:54:59Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)false
dc.title.none.fl_str_mv MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
title MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
spellingShingle MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
Käfer, Pâmela Suélen
Geociências; Geodésia
Forest production; Remote sensing; Curve fitting; Time series
Geociências; Geodésia
Forest production; Remote sensing; Curve fitting; Time series
title_short MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
title_full MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
title_fullStr MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
title_full_unstemmed MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
title_sort MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL
author Käfer, Pâmela Suélen
author_facet Käfer, Pâmela Suélen
Rex, Franciel Eduardo
Breunig, Fábio Marcelo
Balbinot, Rafaelo
author_role author
author2 Rex, Franciel Eduardo
Breunig, Fábio Marcelo
Balbinot, Rafaelo
author2_role author
author
author
dc.contributor.none.fl_str_mv CNPq
CNPq
dc.contributor.author.fl_str_mv Käfer, Pâmela Suélen
Rex, Franciel Eduardo
Breunig, Fábio Marcelo
Balbinot, Rafaelo
dc.subject.por.fl_str_mv Geociências; Geodésia
Forest production; Remote sensing; Curve fitting; Time series
Geociências; Geodésia
Forest production; Remote sensing; Curve fitting; Time series
topic Geociências; Geodésia
Forest production; Remote sensing; Curve fitting; Time series
Geociências; Geodésia
Forest production; Remote sensing; Curve fitting; Time series
description Remote sensing data are a key proxy to forest monitoring and management at local, regional and global scales. Considering the hypothesis that NDVI and EVI can be used at least during one decade to monitor Pinus elliottii in Southern Brazil, the objective of this study was to identify saturation time after planting of these vegetation indices in a Pinus elliottii plantation and the most suitable index by adjusting theoretical functions to each one of them. Based on Landsat Surface Reflectance Higher-Level Data Products, 32 scenes were selected between 1984 to 2015. A set of theoretical polynomial, gaussian and logistic mathematical functions were applied to fit the experimental data on vegetation indices. The determination coefficient (R²) and RMSE at 95% probability were also used. Finally, EVI efficiency was tested by changing the L parameter. The logistic model was the one that best explained the data resulting from NDVI and EVI over time. NDVI was more effective than EVI for this forest monitoring, identifying the forest growth pattern until its 18 years of age. EVI may have been saturated after 14 years and the L factor may be set to near to zero to achieve a higher coefficient of determination.
publishDate 2018
dc.date.none.fl_str_mv 2018-09-13
dc.type.none.fl_str_mv

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://revistas.ufpr.br/bcg/article/view/61517
url https://revistas.ufpr.br/bcg/article/view/61517
dc.language.iso.fl_str_mv por
eng
language por
eng
dc.relation.none.fl_str_mv https://revistas.ufpr.br/bcg/article/view/61517/36075
https://revistas.ufpr.br/bcg/article/view/61517/36076
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Boletim de Ciências Geodésicas
Bulletin of Geodetic Sciences
publisher.none.fl_str_mv Boletim de Ciências Geodésicas
Bulletin of Geodetic Sciences
dc.source.none.fl_str_mv Boletim de Ciências Geodésicas; Vol 24, No 3 (2018)
Bulletin of Geodetic Sciences; Vol 24, No 3 (2018)
1982-2170
1413-4853
reponame:Boletim de Ciências Geodésicas
instname:Universidade Federal do Paraná (UFPR)
instacron:UFPR
instname_str Universidade Federal do Paraná (UFPR)
instacron_str UFPR
institution UFPR
reponame_str Boletim de Ciências Geodésicas
collection Boletim de Ciências Geodésicas
repository.name.fl_str_mv Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)
repository.mail.fl_str_mv qdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br
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