Evelyn Uuemaa
2024 - 2029 • Consolidator Grant
How has receiving an ERC grant influenced you as a scientist?
The ERC grant is one of the most prestigious grants in the world, and receiving it is a great honour and an important recognition. It has given me more self-confidence and faith in scientific ideas that initially seemed unreachable.
Creating water-smart landscapes
The project addresses the increasing nutrient emissions from agriculture by developing sustainable intensification strategies, focusing on nature-based solutions (NbS) like wetlands and riparian buffer strips to decrease nutrient runoff. Current studies lack specific identification of priority areas (hotspots) or spatially explicit solutions. This project will create an analysis, modelling, and machine learning (ML) framework to identify optimal land management scenarios for NbS. ML will be used to identify landscape predictor variables for nutrient concentrations and their interactions across different spatial scales. A Discrete Global Grid System data cube will manage environmental data, while ML methods will address complex ecosystem responses and reveal new interactions among predictor variables. Spatially explicit NbS allocation scenarios will be developed and evaluated through process-based hydrological modelling.
Result
There is a multitude of satellite data that can be used to monitor and better understand Earth's ecosystems and their conditions. Making this vast amount of satellite data useful requires extensive pre-processing and modelling. The project will develop a novel data cube solution to process satellite data efficiently and use it in machine learning. This solution will then be used to plan wetlands and riparian buffer zones to reduce the environmental impact of nutrient runoff from agriculture. It will determine where these areas would be most effective, as agricultural fertilisers affect surface water. The project has just started, so it is too early to say if the aims will be fulfilled. However, the new data cube technology is already generating significant interest from different organisations that are keen to use it.
Impact
The novel data cube solution allows effective and efficient management of geospatial data, enabling us to integrate datasets of arbitrary size, spatial extent, and resolution to support subsequent analyses, while avoiding areal distortions. This solution can be used in any field that requires the use of remote sensing data, e.g. flood mapping, forest management, etc.
Additionally, the solution will enable farmers to identify areas of their land that are at a higher risk of nutrient runoff and determine where they could potentially plan wetlands and riparian buffer strips to mitigate the said runoff.