Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/173177
Título: A Hybrid Modelling Approach for Detecting Seasonal Variations in Inland Green-Blue Ecosystems [poster]
Autor: Almeida, Bruna
Cabral, Pedro
Palavras-chave: Remote Sensing
Machine Learning
Geographic Information Systems
Aquatic Ecosystems
Terrestrial Ecosystems
Climate Change
SDG 6 - Clean Water and Sanitation
SDG 11 - Sustainable Cities and Communities
SDG 12 - Responsible Consumption and Production
SDG 13 - Climate Action
SDG 15 - Life on Land
Data: 3-Jul-2024
Resumo: 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. In the context of climate change, Portugal is recognized as a hotspot among the most vulnerable European countries. Recent studies have shown evidence of climatic changes, such as the long periods of drought recorded in 1990, 2004/2005 and2012. The more frequent occurrence of these events is increasing the severity of seasonality effects on GBE and compromising the provision of services such as freshwater supply, and consequently crop and wood production, and carbon storage and sequestration.
Descrição: Almeida, B., & Cabral, P. (2024). A Hybrid Modelling Approach for Detecting Seasonal Variations in Inland Green-Blue Ecosystems [poster]. 1. Poster session presented at Encontro Ciência 2024, Porto, Portugal. --- 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 by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS)
Peer review: no
URI: http://hdl.handle.net/10362/173177
Aparece nas colecções:NIMS: MagIC - Documentos de conferências nacionais

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