Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.13/5450 |
Resumo: | The estimation of vegetation parameters, such as above-ground biomass, with high accuracy using remote sensing data, represents a promising approach. The present study develops models to estimate and map above-ground biomass of Mediterranean Quercus rotundifolia stands using one QuickBird satellite image in pan-sharpened mode, with four multispectral bands (blue, green, red and near infrared) and a spatial resolution of 0.70 m. The satellite image was orthorectified, geometrically and radiometrically corrected. Object-oriented classi fication methods and multi-resolution segmentation were used to derive a vegetation mask per forest species. Data from forest inventory (24 plots) and vegetation indices (NDVI, EVI, SR and SAVI) derived from high spatial resolution satellite images were used for an area of 133 km2 , in southern Portugal. The statistical analysis included correlation, variance analysis and linear regression. The linear regression models fitted included the arithmetic mean and the median values of the vegetation indices per inventory plot as explanatory variables. The overall results of the fitted models show a trend of better performance for those with the median value of the vegetation index as the explanatory variable. The best fitted model (R2 = 75.3) was associated with the Simple Ratio (SR) median value as an explanatory variable. A Quercus rotundifolia above-ground biomass map was produced. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite imagesAbove-ground biomassHigh spatial resolutionVegetation indicesLinear regression.Escola Superior de Tecnologias e GestãoThe estimation of vegetation parameters, such as above-ground biomass, with high accuracy using remote sensing data, represents a promising approach. The present study develops models to estimate and map above-ground biomass of Mediterranean Quercus rotundifolia stands using one QuickBird satellite image in pan-sharpened mode, with four multispectral bands (blue, green, red and near infrared) and a spatial resolution of 0.70 m. The satellite image was orthorectified, geometrically and radiometrically corrected. Object-oriented classi fication methods and multi-resolution segmentation were used to derive a vegetation mask per forest species. Data from forest inventory (24 plots) and vegetation indices (NDVI, EVI, SR and SAVI) derived from high spatial resolution satellite images were used for an area of 133 km2 , in southern Portugal. The statistical analysis included correlation, variance analysis and linear regression. The linear regression models fitted included the arithmetic mean and the median values of the vegetation indices per inventory plot as explanatory variables. The overall results of the fitted models show a trend of better performance for those with the median value of the vegetation index as the explanatory variable. The best fitted model (R2 = 75.3) was associated with the Simple Ratio (SR) median value as an explanatory variable. A Quercus rotundifolia above-ground biomass map was produced.Taylor and FrancisDigitUMaMacedo, Fabrício L.Sousa, Adélia M. O.Gonçalves, Ana CristinaMarques da Silva, José R.Mesquita, Paulo A.Rodrigues, Ricardo A. F.2023-12-28T12:24:46Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.13/5450engMacedo, F. L., Sousa, A. M., Gonçalves, A. C., Marques da Silva, J. R., Mesquita, P. A., & Rodrigues, R. A. (2018). Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images. European Journal of Remote Sensing, 51(1), 932-944.10.1080/22797254.2018.1521250info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-31T03:30:33Zoai:digituma.uma.pt:10400.13/5450Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:57:00.165559Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images |
title |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images |
spellingShingle |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images Macedo, Fabrício L. Above-ground biomass High spatial resolution Vegetation indices Linear regression . Escola Superior de Tecnologias e Gestão |
title_short |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images |
title_full |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images |
title_fullStr |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images |
title_full_unstemmed |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images |
title_sort |
Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images |
author |
Macedo, Fabrício L. |
author_facet |
Macedo, Fabrício L. Sousa, Adélia M. O. Gonçalves, Ana Cristina Marques da Silva, José R. Mesquita, Paulo A. Rodrigues, Ricardo A. F. |
author_role |
author |
author2 |
Sousa, Adélia M. O. Gonçalves, Ana Cristina Marques da Silva, José R. Mesquita, Paulo A. Rodrigues, Ricardo A. F. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
DigitUMa |
dc.contributor.author.fl_str_mv |
Macedo, Fabrício L. Sousa, Adélia M. O. Gonçalves, Ana Cristina Marques da Silva, José R. Mesquita, Paulo A. Rodrigues, Ricardo A. F. |
dc.subject.por.fl_str_mv |
Above-ground biomass High spatial resolution Vegetation indices Linear regression . Escola Superior de Tecnologias e Gestão |
topic |
Above-ground biomass High spatial resolution Vegetation indices Linear regression . Escola Superior de Tecnologias e Gestão |
description |
The estimation of vegetation parameters, such as above-ground biomass, with high accuracy using remote sensing data, represents a promising approach. The present study develops models to estimate and map above-ground biomass of Mediterranean Quercus rotundifolia stands using one QuickBird satellite image in pan-sharpened mode, with four multispectral bands (blue, green, red and near infrared) and a spatial resolution of 0.70 m. The satellite image was orthorectified, geometrically and radiometrically corrected. Object-oriented classi fication methods and multi-resolution segmentation were used to derive a vegetation mask per forest species. Data from forest inventory (24 plots) and vegetation indices (NDVI, EVI, SR and SAVI) derived from high spatial resolution satellite images were used for an area of 133 km2 , in southern Portugal. The statistical analysis included correlation, variance analysis and linear regression. The linear regression models fitted included the arithmetic mean and the median values of the vegetation indices per inventory plot as explanatory variables. The overall results of the fitted models show a trend of better performance for those with the median value of the vegetation index as the explanatory variable. The best fitted model (R2 = 75.3) was associated with the Simple Ratio (SR) median value as an explanatory variable. A Quercus rotundifolia above-ground biomass map was produced. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z 2023-12-28T12:24:46Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.13/5450 |
url |
http://hdl.handle.net/10400.13/5450 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Macedo, F. L., Sousa, A. M., Gonçalves, A. C., Marques da Silva, J. R., Mesquita, P. A., & Rodrigues, R. A. (2018). Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images. European Journal of Remote Sensing, 51(1), 932-944. 10.1080/22797254.2018.1521250 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Taylor and Francis |
publisher.none.fl_str_mv |
Taylor and Francis |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136454227525632 |