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Satellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysis

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dc.contributor.authorALMEIDA, BRUNA
dc.contributor.authorDavid, João
dc.contributor.authorS. Campos, Felipe
dc.contributor.authorCabral, Pedro
dc.date.accessioned2024-07-04T14:50:07Z
dc.date.available2024-07-04T14:50:07Z
dc.date.issued2024
dc.description.abstractSatellite-based Machine Learning (ML) modelling has emerged as a powerful tool to understand and quantify spatial relationships between landscape dynamics, biophysical variables and natural stocks. Ecosystem Services indicators (ESi) provide qualitative and quantitative information aiding the assessment of ecosystems’ status. Through a systematic meta-analysis following the PRISMA guidelines, studies from one decade (2012–2022) were analyzed and synthesized. The results indicated that Random Forest emerged as the most frequently utilized ML algorithm, while Landsat missions stood out as the primary source of Satellite Earth Observation (SEO) data. Nonetheless, authors favoured Sentinel-2 due to its superior spatial, spectral, and temporal resolution. While 30% of the examined studies focused on modelling proxies of climate regulation services, assessments of natural stocks such as biomass, water, food production, and raw materials were also frequently applied. Meta-analysis illustrated the utilization of classification and regression tasks in estimating measurements of ecosystems' extent and conditions and findings underscored the connections between established methods and their replication. This study offers current perspectives on existing satellite-based approaches, contributing to the ongoing efforts to employ ML and artificial intelligence for unveiling the potential of SEO data and technologies in modelling ESi.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlmeida, B., David, J., Campos, F. S., & Cabral, P. (2024). Satellite-based machine learning modelling of ecosystem services indicators: a review and meta-analysis. "Applied Geography", 165, 103249. DOI:10.1016/j.apgeog.2024.103249pt_PT
dc.identifier.doi10.1016/j.apgeog.2024.103249pt_PT
dc.identifier.eissn1873-7730
dc.identifier.issn0143-6228
dc.identifier.urihttp://hdl.handle.net/10400.3/7093
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0143622824000547pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectRemote Sensingpt_PT
dc.subjectNatural Capitalpt_PT
dc.subjectBiodiversitypt_PT
dc.subjectData Fusionpt_PT
dc.subjectMLOpspt_PT
dc.subjectEnvironmental Modellingpt_PT
dc.titleSatellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysispt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleApplied Geographypt_PT
oaire.citation.volume165pt_PT
person.familyNameGomes Almeida
person.familyNameDavid
person.familyNameS. Campos
person.familyNameCabral
person.givenNameBruna Aparecida
person.givenNameJoão
person.givenNameFelipe
person.givenNamePedro
person.identifier1011985
person.identifierB-2616-2010
person.identifier.ciencia-id0512-DE7A-D9FE
person.identifier.ciencia-idBA16-9D4F-8A9F
person.identifier.ciencia-id681A-06F0-3E52
person.identifier.ciencia-idC314-19F4-4DCC
person.identifier.orcid0000-0002-3349-1470
person.identifier.orcid0000-0001-7415-0202
person.identifier.orcid0000-0001-8622-6008
person.identifier.ridA-9821-2018
person.identifier.scopus-author-id57223093094
person.identifier.scopus-author-id48361058100
person.identifier.scopus-author-id56221630400
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication087d0e00-e6e1-4769-8299-595d33451e31
relation.isAuthorOfPublication3801feb1-16ed-460b-b9b5-245af69c1391
relation.isAuthorOfPublicationa147a107-65ca-4f37-9ffb-b45c48806c75
relation.isAuthorOfPublication5f5b6ee6-a5c9-4e56-9583-5f1f64041b96
relation.isAuthorOfPublication.latestForDiscovery087d0e00-e6e1-4769-8299-595d33451e31

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