Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data
Main Author: | |
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Publication Date: | 2019 |
Other Authors: | , , , , , , |
Format: | Article |
Language: | eng |
Source: | Floresta e Ambiente |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000103 |
Summary: | ABSTRACT The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation. |
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Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Dataatlantic forestremote sensingforest inventorymeasurementABSTRACT The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation.Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000103Floresta e Ambiente v.26 n.spe1 2019reponame:Floresta e Ambienteinstname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJ10.1590/2179-8087.037918info:eu-repo/semantics/openAccessGonçalves,Anny Francielly AtaideFernandes,Márcia Rodrigues de MouraSilva,Jeferson Pereira MartinsSilva,Gilson Fernandes daAlmeida,André Quintão deCordeiro,Natielle GomesSilva,Lucas Duarte Caldas daScolforo,José Roberto Soareseng2019-03-21T00:00:00Zoai:scielo:S2179-80872019005000103Revistahttps://www.floram.org/PUBhttps://old.scielo.br/oai/scielo-oai.phpfloramjournal@gmail.com||floram@ufrrj.br||2179-80871415-0980opendoar:2019-03-21T00:00Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data |
title |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data |
spellingShingle |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data Gonçalves,Anny Francielly Ataide atlantic forest remote sensing forest inventory measurement |
title_short |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data |
title_full |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data |
title_fullStr |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data |
title_full_unstemmed |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data |
title_sort |
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data |
author |
Gonçalves,Anny Francielly Ataide |
author_facet |
Gonçalves,Anny Francielly Ataide Fernandes,Márcia Rodrigues de Moura Silva,Jeferson Pereira Martins Silva,Gilson Fernandes da Almeida,André Quintão de Cordeiro,Natielle Gomes Silva,Lucas Duarte Caldas da Scolforo,José Roberto Soares |
author_role |
author |
author2 |
Fernandes,Márcia Rodrigues de Moura Silva,Jeferson Pereira Martins Silva,Gilson Fernandes da Almeida,André Quintão de Cordeiro,Natielle Gomes Silva,Lucas Duarte Caldas da Scolforo,José Roberto Soares |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Gonçalves,Anny Francielly Ataide Fernandes,Márcia Rodrigues de Moura Silva,Jeferson Pereira Martins Silva,Gilson Fernandes da Almeida,André Quintão de Cordeiro,Natielle Gomes Silva,Lucas Duarte Caldas da Scolforo,José Roberto Soares |
dc.subject.por.fl_str_mv |
atlantic forest remote sensing forest inventory measurement |
topic |
atlantic forest remote sensing forest inventory measurement |
description |
ABSTRACT The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
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=S2179-80872019005000103 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000103 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2179-8087.037918 |
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.26 n.spe1 2019 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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1750128143222439936 |