In the heart of the Swiss Alps, a critical study is unfolding that could reshape how we understand and manage land use, with significant implications for the energy sector. Led by L. D’Ambrosio from the Department of Architecture, Built Environment and Construction Engineering at Politecnico di Milano, the research is part of the INTERREG VI-A Italy-Switzerland project WINCA4TI, focusing on the Ticino River Basin. The study, published in ‘The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences’ (translated as ‘The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences’), delves into the dynamics of built-up surfaces, providing a comparative analysis of three key land use and land cover (LU/LC) datasets.
The Ticino River Basin, a vital transboundary watershed, is under increasing pressure from urbanization and climate change. Accurate LU/LC data are essential for tracking environmental changes, modeling nutrient flows, and guiding sustainable land management. D’Ambrosio and his team analyzed three datasets: the national Arealstatistik, the European CORINE Land Cover (CLC), and the Ticino Cantonal Land Cover Cadastre. These datasets vary in spatial resolution, classification systems, and acquisition methodologies, making their comparison crucial for understanding the nuances of built-up surface evolution.
“Our goal was to harmonize these datasets to ensure consistency and reliability in our analysis,” D’Ambrosio explained. “We focused on the ‘built-up surfaces’ category, which is critical for environmental modeling and nutrient management.”
The study revealed significant discrepancies in the absolute values and temporal trends of built-up surfaces, depending on the dataset used. While the Cadastre and Arealstatistik yielded similar estimates when filtered for building-related categories, CORINE reported higher values. This discrepancy highlights the suitability of CORINE for national-scale analysis but also its limited precision in smaller, alpine regions.
For the energy sector, these findings are particularly relevant. Accurate LU/LC data are crucial for planning renewable energy projects, such as hydroelectric power plants, which rely on stable water flows and minimal environmental impact. “Understanding the dynamics of built-up surfaces helps us predict changes in water availability and quality, which are essential for hydroelectric power generation,” D’Ambrosio noted.
The study’s use of open-source tools like QGIS and open data ensures a reproducible and transferable workflow, making it accessible for future research and practical applications. This preliminary work lays the foundation for a broader comparison involving all land use categories, supporting future modeling of diffuse nutrient loads and climate adaptation strategies across the transboundary Ticino basin.
As the energy sector increasingly turns to renewable sources, the need for precise and reliable environmental data becomes ever more critical. This research not only highlights the importance of dataset selection and class harmonization but also paves the way for more accurate environmental modeling and sustainable land management practices. In a world grappling with climate change, such insights are invaluable, offering a glimpse into a future where technology and environmental stewardship go hand in hand.