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Spatially explicit assessment of carbon storage and sequestration in forest ecosystems

2025-04-19Artigo de investigação Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências Biológicas
datacite.subject.sdg15:Proteger a Vida Terrestre
dc.contributor.authoralmeida, bruna
dc.contributor.authorMonteiro, Luís
dc.contributor.authorTiengo, Rafaela
dc.contributor.authorFreire Gil, Artur José
dc.contributor.authorCabral, Pedro
dc.date.accessioned2026-01-27T15:02:32Z
dc.date.available2026-01-27T15:02:32Z
dc.date.issued2025-04-19
dc.description.abstractABSTRACT: Forests play an important role in the global carbon cycle, making accurate assessments of carbon dynamics essential for effective forest management and climate change mitigation strategies. This research examines the spatiotemporal patterns of carbon storage and sequestration (CSS) in forests' aboveground biomass using satellite data, machine learning (Support Vector Machines), carbon modelling and spatial statistics. The methodology follows a two-step classification process: (i) binary forest classification and (ii) forest type classification, mapping seven forest types within two main categories - Broadleaves (Quercus suber, Quercus ilex, Eucalyptus sp., and other species) and Coniferous (Pinus pinaster, Pinus pinea, and other species). We analyzed the relationship between forest type and CSS at the Nomenclature of Territorial Units for Statistics (NUTS) III level and identified spatial clusters, outliers, and hot and cold spots of carbon sequestration at the municipal level across mainland Portugal. The broadleaved category demonstrated the highest classification accuracy in both years, decreasing slightly from 90.3 % in 2018 to 89 % in 2022, while the Coniferous group had the lowest accuracy, declining from 84.1 % in 2018 to 83.6 % in 2022. Anselin's Local Moran's I identified clusters of carbon sequestration, while the Getis-Ord Gi analysis confirmed these findings, revealing statistically significant hotspots of carbon sequestration in the northern and central regions and cold spots in the southern region. By providing insights at the sub-regional and municipal levels, this study offers a robust framework to support sustainable forest management and climate change mitigation strategies. Moreover, it can assist decision-makers in prioritizing natural capital, and developing nature-based solutions to tackle climate change and biodiversity loss.eng
dc.identifier.citationAlmeida, B., Monteiro, L., Tiengo, R., Gil, A., & Cabral, P. (2025). Spatially explicit assessment of carbon storage and sequestration in forest ecosystems. Remote Sensing Applications: Society and Environment, 38, 101544. https://doi.org/10.1016/j.rsase.2025.101544
dc.identifier.doi10.1016/j.rsase.2025.101544
dc.identifier.issn2352-9385
dc.identifier.urihttp://hdl.handle.net/10400.3/8839
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationFundação para a Ciência e a Tecnologia (FCT) - MaSOT – Mapping Ecosystem Services from Earth Observations - EXPL/CTA-AMB/0165/2021
dc.relationFundação para a Ciência e a Tecnologia (FCT) - UIDB/04152/2020 – Centro de Investigação em Gestão de Informação (MagIC) / NOVA IMS
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S2352938525000977?via%3Dihub
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectClimate regulation
dc.subjectVegetation dynamics
dc.subjectSustainable development goals
dc.subjectGeographical information systems
dc.subjectMachine learning
dc.titleSpatially explicit assessment of carbon storage and sequestration in forest ecosystemseng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage19
oaire.citation.issue101544
oaire.citation.startPage1
oaire.citation.titleRemote Sensing Applications-Society and Environment
oaire.citation.volume38
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamealmeida
person.familyNameTiengo
person.familyNameFreire Gil
person.familyNameCabral
person.givenNamebruna
person.givenNameRafaela
person.givenNameArtur José
person.givenNamePedro
person.identifier2775097
person.identifierI-7520-2012
person.identifierB-2616-2010
person.identifier.ciencia-id9A15-C528-B4A3
person.identifier.ciencia-id6E1A-0689-D573
person.identifier.ciencia-idC314-19F4-4DCC
person.identifier.orcid0000-0001-7431-5468
person.identifier.orcid0000-0002-9298-0178
person.identifier.orcid0000-0003-4450-8167
person.identifier.orcid0000-0001-8622-6008
person.identifier.scopus-author-id37064609200
person.identifier.scopus-author-id56221630400
relation.isAuthorOfPublication2085a536-19bf-4bb9-8bb2-58e6b55de34b
relation.isAuthorOfPublication37d39e04-4557-4b33-b4e9-dfbb223c82fd
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relation.isAuthorOfPublication5f5b6ee6-a5c9-4e56-9583-5f1f64041b96
relation.isAuthorOfPublication.latestForDiscovery2085a536-19bf-4bb9-8bb2-58e6b55de34b

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