Data-Driven Modelling of Freshwater Ecosystems: A Multiscale Framework Based on Global Geospatial Data

Bruna Almeida, Pedro Cabral

2023

Abstract

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.

Paper Citation

in Harvard Style

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 - Volume 1: GISTAM, ISBN 978-989-758-649-1, SciTePress, pages 104-111. DOI: 10.5220/0012037800003473

in Bibtex Style

@conference{gistam23,
author={Bruna Almeida and Pedro Cabral},
title={Data-Driven Modelling of Freshwater Ecosystems: A Multiscale Framework Based on Global Geospatial Data},
booktitle={Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2023},
pages={104-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012037800003473},
isbn={978-989-758-649-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Data-Driven Modelling of Freshwater Ecosystems: A Multiscale Framework Based on Global Geospatial Data
SN - 978-989-758-649-1
AU - Almeida B.
AU - Cabral P.
PY - 2023
SP - 104
EP - 111
DO - 10.5220/0012037800003473
PB - SciTePress