A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems

Detalhes bibliográficos
Autor(a) principal: Almeida, Bruna
Data de Publicação: 2023
Outros Autores: 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/10362/161251
Resumo: Almeida, B., & Cabral, P. (2023). A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems. Remote Sensing Applications: Society and Environment, 33(January 2024), [101121]. https://doi.org/10.1016/j.rsase.2023.101121 --- 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]. The authors gratefully acknowledge the financial support of the FCT, through the MagIC Research (Centro de Investigação em Gestão de Informação - UIDB/04152/2020). We are grateful for the constructive remarks from two anonymous reviewers.
id RCAP_16d732232bf373ba81a4cb7da60f165c
oai_identifier_str oai:run.unl.pt:10362/161251
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystemsRemote sensingMachine learningLand use classificationAquatic ecosystemsTerrestrial ecosystemsSpatiotemporal analysisGeography, Planning and DevelopmentComputers in Earth SciencesSDG 6 - Clean Water and SanitationSDG 11 - Sustainable Cities and CommunitiesSDG 13 - Climate ActionSDG 15 - Life on LandSDG 12 - Responsible Consumption and ProductionAlmeida, B., & Cabral, P. (2023). A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems. Remote Sensing Applications: Society and Environment, 33(January 2024), [101121]. https://doi.org/10.1016/j.rsase.2023.101121 --- 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]. The authors gratefully acknowledge the financial support of the FCT, through the MagIC Research (Centro de Investigação em Gestão de Informação - UIDB/04152/2020). We are grateful for the constructive remarks from two anonymous reviewers.Deforestation, environmental pollution, and the overexploitation of resources, in addition to the Earth's natural cycles, are scaling up the impacts of climate change in the provision of Ecosystem Services (ES). Green-Blue Ecosystems (GBE) are impacted by climatic conditions, topography, and water presence. Data-driven modelling techniques may effectively capture the effects of seasonal variations while modelling natural ecosystems. This research proposes a hybrid modelling approach that combines Deep Learning and traditional Machine Learning, Sensitivity Analysis and Feature Importance Evaluation (FIE) to investigate seasonality effects on mapping GBE. The models, built using satellite imagery from the Spring and Summer seasons of the Mediterranean climate zone, included spectral indices, topography (DEM), and groundwater depth (GD). The model that best suited the analysis was selected using sensitivity tests and hyperparameter optimization. The study shows that land cover classes of transitional woodland shrubs, inland marshes, cultivated land parcels, and watercourses are better classified in the Spring, with an accuracy of 0.814. FIE indicates that spectral indices are the most important predictors for detecting green ecosystems in both seasons. Additionally, DEM and GD are the most relevant predictors to classify watercourses in the Summer. An analytical examination of the input data and hyperparameter settings facilitates understanding of models' behaviour while improving models' prediction. This research provides an advanced understanding of the effects of seasonal variations on the status of GBE and enhances understanding of modelling ES in areas with a growing need for changes in land use and high water supply demand.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNAlmeida, BrunaCabral, Pedro2023-12-13T23:04:41Z2024-012024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/161251eng2352-9385PURE: 78167401https://doi.org/10.1016/j.rsase.2023.101121info: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:RCAAP2024-03-11T05:44:06Zoai:run.unl.pt:10362/161251Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:58:27.062902Repositório 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 A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
title A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
spellingShingle A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
Almeida, Bruna
Remote sensing
Machine learning
Land use classification
Aquatic ecosystems
Terrestrial ecosystems
Spatiotemporal analysis
Geography, Planning and Development
Computers in Earth Sciences
SDG 6 - Clean Water and Sanitation
SDG 11 - Sustainable Cities and Communities
SDG 13 - Climate Action
SDG 15 - Life on Land
SDG 12 - Responsible Consumption and Production
title_short A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
title_full A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
title_fullStr A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
title_full_unstemmed A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
title_sort A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems
author Almeida, Bruna
author_facet Almeida, Bruna
Cabral, Pedro
author_role author
author2 Cabral, Pedro
author2_role 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
Cabral, Pedro
dc.subject.por.fl_str_mv Remote sensing
Machine learning
Land use classification
Aquatic ecosystems
Terrestrial ecosystems
Spatiotemporal analysis
Geography, Planning and Development
Computers in Earth Sciences
SDG 6 - Clean Water and Sanitation
SDG 11 - Sustainable Cities and Communities
SDG 13 - Climate Action
SDG 15 - Life on Land
SDG 12 - Responsible Consumption and Production
topic Remote sensing
Machine learning
Land use classification
Aquatic ecosystems
Terrestrial ecosystems
Spatiotemporal analysis
Geography, Planning and Development
Computers in Earth Sciences
SDG 6 - Clean Water and Sanitation
SDG 11 - Sustainable Cities and Communities
SDG 13 - Climate Action
SDG 15 - Life on Land
SDG 12 - Responsible Consumption and Production
description Almeida, B., & Cabral, P. (2023). A hybrid modelling approach for detecting seasonal variations in inland green-blue ecosystems. Remote Sensing Applications: Society and Environment, 33(January 2024), [101121]. https://doi.org/10.1016/j.rsase.2023.101121 --- 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]. The authors gratefully acknowledge the financial support of the FCT, through the MagIC Research (Centro de Investigação em Gestão de Informação - UIDB/04152/2020). We are grateful for the constructive remarks from two anonymous reviewers.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-13T23:04:41Z
2024-01
2024-01-01T00:00:00Z
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/10362/161251
url http://hdl.handle.net/10362/161251
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2352-9385
PURE: 78167401
https://doi.org/10.1016/j.rsase.2023.101121
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15
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
repository.mail.fl_str_mv
_version_ 1799138165214150656

Registros relacionados