Data-Driven Modelling of Freshwater Ecosystems

Almeida, Bruna; Cabral, Pedro

http://hdl.handle.net/10362/153871

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Autores

Almeida, Bruna

Cabral, Pedro

Resumo(s)

Freshwater ecosystems are primarily impacted by climate, land use and land cover changes, and over-abstraction. Satellite Earth observation (SEO) data and technologies are key in environmental modelling and support decisions. These technologies combined with machine learning (ML) are a powerful approach for modelling freshwater ecosystems at a multiscale level. The goal of this study is to present a set of reference data and guidelines that can be used to estimate the water and wetness probability index (WWPI) in different spatial and temporal scales. To find the best model’s predictors, sensitivity analyses were carried out in a predictive ML model implemented in a transnational river basin district (Portugal – Spain), the Tagus Basin. Satellite imagery, satellite-derived data, biophysical variables, and landscape characteristics were the explanatory variables evaluated in the sensitivity analyses, and some of them were included in the framework as a reference source of spatial data.

Descrição

Almeida, B. and Cabral, P. (2023). Data-Driven Modelling of Freshwater Ecosystems: A Multiscale Framework Based on Global Geospatial Data. In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-649-1; ISSN 2184-500X, SciTePress, pages 104-111. DOI: 10.5220/0012037800003473---This work 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 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS

Palavras-chave

Remote Sensing Ecosystem Services Water Modelling Machine Learning Geographical Information Systems Computer Graphics and Computer-Aided Design Computer Networks and Communications Computer Science Applications Computer Vision and Pattern Recognition Information Systems Software SDG 6 - Clean Water and Sanitation SDG 11 - Sustainable Cities and Communities SDG 13 - Climate Action SDG 15 - Life on Land