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/10174/23599 https://doi.org/10.1080/22797254.2018.1521250 |
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 classification 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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Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite imagesabove-ground biomasshigh spatial resolutionvegetation indiceslinear regressionThe 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 classification 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.2018-10-12T16:30:07Z2018-10-122018-10-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/23599http://hdl.handle.net/10174/23599https://doi.org/10.1080/22797254.2018.1521250engMacedo F. L., Sousa A.M.O., Gonçalves A.C., Marques da Silva J.R., Mesquita P.A e Rodrigues, R. A. (2018). Above ground biomass estimation for Quercus rotundifolia using vegetation indices derived from very high spatial resolution satellite images. European Journal of Remote Sensing. 51(1): 933-944.ICAAMfabriciolmacedo@hotmail.comasousa@uevora.ptacag@uevora.ptjmsilva@uevora.ptpmesquita@uevora.ptricardo@agr.feis.unesp.br214Macedo, FabrícioSousa, AdéliaGonçalves, Ana CristinaMarques da Silva, JoséMesquita, PauloRodrigues, Ricardoinfo: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:RCAAP2024-01-03T19:15:52Zoai:dspace.uevora.pt:10174/23599Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:14:25.149678Repositório 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 above-ground biomass high spatial resolution vegetation indices linear regression |
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 |
author_facet |
Macedo, Fabrício Sousa, Adélia Gonçalves, Ana Cristina Marques da Silva, José Mesquita, Paulo Rodrigues, Ricardo |
author_role |
author |
author2 |
Sousa, Adélia Gonçalves, Ana Cristina Marques da Silva, José Mesquita, Paulo Rodrigues, Ricardo |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Macedo, Fabrício Sousa, Adélia Gonçalves, Ana Cristina Marques da Silva, José Mesquita, Paulo Rodrigues, Ricardo |
dc.subject.por.fl_str_mv |
above-ground biomass high spatial resolution vegetation indices linear regression |
topic |
above-ground biomass high spatial resolution vegetation indices linear regression |
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 classification 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 |
2018-10-12T16:30:07Z 2018-10-12 2018-10-03T00:00:00Z |
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/10174/23599 http://hdl.handle.net/10174/23599 https://doi.org/10.1080/22797254.2018.1521250 |
url |
http://hdl.handle.net/10174/23599 https://doi.org/10.1080/22797254.2018.1521250 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Macedo F. L., Sousa A.M.O., Gonçalves A.C., Marques da Silva J.R., Mesquita P.A e Rodrigues, R. A. (2018). Above ground biomass estimation for Quercus rotundifolia using vegetation indices derived from very high spatial resolution satellite images. European Journal of Remote Sensing. 51(1): 933-944. ICAAM fabriciolmacedo@hotmail.com asousa@uevora.pt acag@uevora.pt jmsilva@uevora.pt pmesquita@uevora.pt ricardo@agr.feis.unesp.br 214 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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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