Creating Water-Smart Landscapes
By 2050 the world’s population will reach 9-10 billion, which means that food production must increase significantly. Therefore there is a need for sustainable intensification by increasing yields while simultaneously decreasing the environmental impacts. The aim of the project is to develop an integrated geospatial data management and analysis, modelling, and machine learning framework for finding spatially optimal land management together with implementing nature-based solutions such as wetlands and riparian buffer zones for reducing agricultural nutrient runoff from catchments at different scales. Moreover, the project will identify the landscape predictor variables at different spatial scales for nutrient concentrations and their cross-scale interactions using machine learning.
Additionally, we will address the challenges of large geospatial data processing by integrating all data into a datacube and by using proven parallelisation, and high-performance computing environments.
More information regarding the project
Funding agency: Estonian Research Council
Principal investigator: Evelyn Uuemaa, 1.01.2023−31.12.2027
