Satellite-based Machine Learning modelling of Ecosystem Services indicators

Detalhes bibliográficos
Autor(a) principal: Almeida, Bruna
Data de Publicação: 2024
Outros Autores: David, João, Campos, Felipe Siqueira e, Cabral, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/164985
Resumo: Almeida, B., David, J., Campos, F. S. E., & Cabral, P. (2024). Satellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysis. Applied Geography, 165, 1-17. Article 103249. https://doi.org/10.1016/j.apgeog.2024.103249 --- This study was supported by the research project MaSOT – Mapping Ecosystem Services from Earth Observations, funded by the Portuguese Science Foundation - FCT [EXPL/CTA-AMB/0165/2021], and the European Union-NextGenerationEU. The authors gratefully acknowledge the financial support of the FCT, through the MagIC research center (Centro de Investigação em Gestão de Informação - UIDB/04152/2020). João David was financially supported by the Portuguese Foundation for Science and Technology (FCT) under Grant [2021.06482.BD]. We are grateful for the constructive remarks from anonymous reviewers.
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spelling Satellite-based Machine Learning modelling of Ecosystem Services indicatorsA review and meta-analysisRemote sensingNatural capitalBiodiversityData fusionMLOpsEnvironmental modellingSDG 3 - Good Health and Well-beingSDG 9 - Industry, Innovation, and InfrastructureSDG 11 - Sustainable Cities and CommunitiesSDG 16 - Peace, Justice and Strong InstitutionsSDG 17 - Partnerships for the GoalsSDG 2 - Zero HungerSDG 13 - Climate ActionAlmeida, B., David, J., Campos, F. S. E., & Cabral, P. (2024). Satellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysis. Applied Geography, 165, 1-17. Article 103249. https://doi.org/10.1016/j.apgeog.2024.103249 --- This study was supported by the research project MaSOT – Mapping Ecosystem Services from Earth Observations, funded by the Portuguese Science Foundation - FCT [EXPL/CTA-AMB/0165/2021], and the European Union-NextGenerationEU. The authors gratefully acknowledge the financial support of the FCT, through the MagIC research center (Centro de Investigação em Gestão de Informação - UIDB/04152/2020). João David was financially supported by the Portuguese Foundation for Science and Technology (FCT) under Grant [2021.06482.BD]. We are grateful for the constructive remarks from anonymous reviewers.Satellite-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.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNAlmeida, BrunaDavid, JoãoCampos, Felipe Siqueira eCabral, Pedro2024-03-15T00:24:36Z2024-04-012024-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/164985eng0143-6228PURE: 85508403https://doi.org/10.1016/j.apgeog.2024.103249info: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:RCAAP2024-03-18T01:48:50Zoai:run.unl.pt:10362/164985Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:02:06.897341Repositório 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 Satellite-based Machine Learning modelling of Ecosystem Services indicators
A review and meta-analysis
title Satellite-based Machine Learning modelling of Ecosystem Services indicators
spellingShingle Satellite-based Machine Learning modelling of Ecosystem Services indicators
Almeida, Bruna
Remote sensing
Natural capital
Biodiversity
Data fusion
MLOps
Environmental modelling
SDG 3 - Good Health and Well-being
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
SDG 16 - Peace, Justice and Strong Institutions
SDG 17 - Partnerships for the Goals
SDG 2 - Zero Hunger
SDG 13 - Climate Action
title_short Satellite-based Machine Learning modelling of Ecosystem Services indicators
title_full Satellite-based Machine Learning modelling of Ecosystem Services indicators
title_fullStr Satellite-based Machine Learning modelling of Ecosystem Services indicators
title_full_unstemmed Satellite-based Machine Learning modelling of Ecosystem Services indicators
title_sort Satellite-based Machine Learning modelling of Ecosystem Services indicators
author Almeida, Bruna
author_facet Almeida, Bruna
David, João
Campos, Felipe Siqueira e
Cabral, Pedro
author_role author
author2 David, João
Campos, Felipe Siqueira e
Cabral, Pedro
author2_role author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Almeida, Bruna
David, João
Campos, Felipe Siqueira e
Cabral, Pedro
dc.subject.por.fl_str_mv Remote sensing
Natural capital
Biodiversity
Data fusion
MLOps
Environmental modelling
SDG 3 - Good Health and Well-being
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
SDG 16 - Peace, Justice and Strong Institutions
SDG 17 - Partnerships for the Goals
SDG 2 - Zero Hunger
SDG 13 - Climate Action
topic Remote sensing
Natural capital
Biodiversity
Data fusion
MLOps
Environmental modelling
SDG 3 - Good Health and Well-being
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
SDG 16 - Peace, Justice and Strong Institutions
SDG 17 - Partnerships for the Goals
SDG 2 - Zero Hunger
SDG 13 - Climate Action
description Almeida, B., David, J., Campos, F. S. E., & Cabral, P. (2024). Satellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysis. Applied Geography, 165, 1-17. Article 103249. https://doi.org/10.1016/j.apgeog.2024.103249 --- This study was supported by the research project MaSOT – Mapping Ecosystem Services from Earth Observations, funded by the Portuguese Science Foundation - FCT [EXPL/CTA-AMB/0165/2021], and the European Union-NextGenerationEU. The authors gratefully acknowledge the financial support of the FCT, through the MagIC research center (Centro de Investigação em Gestão de Informação - UIDB/04152/2020). João David was financially supported by the Portuguese Foundation for Science and Technology (FCT) under Grant [2021.06482.BD]. We are grateful for the constructive remarks from anonymous reviewers.
publishDate 2024
dc.date.none.fl_str_mv 2024-03-15T00:24:36Z
2024-04-01
2024-04-01T00:00:00Z
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/10362/164985
url http://hdl.handle.net/10362/164985
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0143-6228
PURE: 85508403
https://doi.org/10.1016/j.apgeog.2024.103249
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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application/pdf
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
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