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Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images

2018Journal article Open access
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dc.contributor.authorMacedo, Fabrício L.
dc.contributor.authorSousa, Adélia M. O.
dc.contributor.authorGonçalves, Ana Cristina
dc.contributor.authorMarques da Silva, José R.
dc.contributor.authorMesquita, Paulo A.
dc.contributor.authorRodrigues, Ricardo A. F.
dc.date.accessioned2023-12-28T12:24:46Z
dc.date.available2023-12-28T12:24:46Z
dc.date.issued2018
dc.description.abstractThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMacedo, 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.pt_PT
dc.identifier.doi10.1080/22797254.2018.1521250pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5450
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherTaylor and Francispt_PT
dc.relationInstitute of Mediterranean Agricultural and Environmental Sciences
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAbove-ground biomasspt_PT
dc.subjectHigh spatial resolutionpt_PT
dc.subjectVegetation indicespt_PT
dc.subjectLinear regressionpt_PT
dc.subject.pt_PT
dc.subjectEscola Superior de Tecnologias e Gestãopt_PT
dc.titleAbove-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite imagespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstitute of Mediterranean Agricultural and Environmental Sciences
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FAGR%2F00115%2F2013/PT
oaire.citation.endPage944pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage932pt_PT
oaire.citation.titleEuropean Journal of Remote Sensingpt_PT
oaire.citation.volume51pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMacedo
person.givenNameFabrício Lopes de
person.identifier2439118
person.identifier.ciencia-idBD1F-4778-60D1
person.identifier.orcid0000-0002-8025-6422
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0cae1387-e03a-4cd4-bd47-c48edc60ad27
relation.isAuthorOfPublication.latestForDiscovery0cae1387-e03a-4cd4-bd47-c48edc60ad27
relation.isProjectOfPublicationb1fab795-ed48-4ba3-aba8-33e93c6c104e
relation.isProjectOfPublication.latestForDiscoveryb1fab795-ed48-4ba3-aba8-33e93c6c104e

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