A hybrid modelling approach for detecting seasonal variations in inland Green-Blue Ecosystems

ALMEIDA, BRUNA; Cabral, Pedro

Publication

A hybrid modelling approach for detecting seasonal variations in inland Green-Blue Ecosystems

2024Journal article

dc.contributor.author	ALMEIDA, BRUNA
dc.contributor.author	Cabral, Pedro
dc.date.accessioned	2024-07-04T11:15:48Z
dc.date.available	2024-07-04T11:15:48Z
dc.date.issued	2024
dc.description.abstract	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.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Almeida, B., & Cabral, P. (2024). A hybrid modelling approach for detecting seasonal variations in inland Green-Blue Ecosystems. "Remote Sensing Applications: Society and Environment", 33, 101121. DOI:10.1016/j.rsase.2023.101121	pt_PT
dc.identifier.doi	10.1016/j.rsase.2023.101121	pt_PT
dc.identifier.uri	http://hdl.handle.net/10400.3/7080
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Elsevier	pt_PT
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S2352938523002033	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Remote Sensing	pt_PT
dc.subject	Machine Learning	pt_PT
dc.subject	Land Use Classification	pt_PT
dc.subject	Aquatic Ecosystems	pt_PT
dc.subject	Terrestrial Ecosystems	pt_PT
dc.subject	Spatiotemporal Analysis	pt_PT
dc.title	A hybrid modelling approach for detecting seasonal variations in inland Green-Blue Ecosystems	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.title	Remote Sensing Applications: Society and Environment	pt_PT
oaire.citation.volume	33	pt_PT
person.familyName	Gomes Almeida
person.familyName	Cabral
person.givenName	Bruna Aparecida
person.givenName	Pedro
person.identifier	B-2616-2010
person.identifier.ciencia-id	0512-DE7A-D9FE
person.identifier.ciencia-id	C314-19F4-4DCC
person.identifier.orcid	0000-0002-3349-1470
person.identifier.orcid	0000-0001-8622-6008
person.identifier.scopus-author-id	57223093094
person.identifier.scopus-author-id	56221630400
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
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