Ground-mounted photovoltaic systems are expanding rapidly to meet decarbonisation targets, but their growth raises concerns about land take, farmland conversion, and biodiversity impacts. Addressing the lack of tools to monitor local land-use change across Italian municipalities, this study presents an open-access application developed on Google Earth Engine. Through an interactive interface, users can select an Italian municipality, define the year and compositing method for Sentinel-2 imagery, draw training and validation polygons, and choose among three classifiers to generate land-cover maps. The tool automatically evaluates classification accuracy, filters pixels, and converts the photovoltaic class into vector polygons. Users can then select the dataset (CORINE Land Cover + Backbone or EUCropMap) and reference year to reconstruct previous land cover and agricultural use. All results, including classified maps, photovoltaic polygons, summary tables, and charts, can be exported. Developed for Montalto di Castro (Lazio), the workflow achieved 91.05% overall accuracy and mapped 762.14 ha of installations, covering 4.02% of the entire municipal territory. Results show that most installations replaced herbaceous farmland. The workflow was successfully tested in the municipality of Guillena (Andalucía, Spain), confirming its adaptability. The application offers a practical, scalable solution for quantifying photovoltaic expansion and supporting spatial planning in Italy and across Europe.

A google earth engine app to map ground-mounted photovoltaic system and track land use change in Italy

Marta Cotti Piccinelli
Primo
;
Marco Ciolfi;Carlo Calfapietra
Co-ultimo
;
Chiara Baldacchini
Co-ultimo
2026

Abstract

Ground-mounted photovoltaic systems are expanding rapidly to meet decarbonisation targets, but their growth raises concerns about land take, farmland conversion, and biodiversity impacts. Addressing the lack of tools to monitor local land-use change across Italian municipalities, this study presents an open-access application developed on Google Earth Engine. Through an interactive interface, users can select an Italian municipality, define the year and compositing method for Sentinel-2 imagery, draw training and validation polygons, and choose among three classifiers to generate land-cover maps. The tool automatically evaluates classification accuracy, filters pixels, and converts the photovoltaic class into vector polygons. Users can then select the dataset (CORINE Land Cover + Backbone or EUCropMap) and reference year to reconstruct previous land cover and agricultural use. All results, including classified maps, photovoltaic polygons, summary tables, and charts, can be exported. Developed for Montalto di Castro (Lazio), the workflow achieved 91.05% overall accuracy and mapped 762.14 ha of installations, covering 4.02% of the entire municipal territory. Results show that most installations replaced herbaceous farmland. The workflow was successfully tested in the municipality of Guillena (Andalucía, Spain), confirming its adaptability. The application offers a practical, scalable solution for quantifying photovoltaic expansion and supporting spatial planning in Italy and across Europe.
2026
Istituto di Ricerca sugli Ecosistemi Terrestri - IRET
Google earth engine
ground-mounted photovoltaic
land use change
spatial planning
supervised classification
user-friendly tool
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/599182
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