This paper presents a case study on the use of existing Python libraries for remote sensing and vegetation index calculations, demonstrating how open-source technologies enable accessible, efficient, and reproducible workflows for environmental monitoring. Leveraging free satellite imagery from the Copernicus program (Sentinel-2) and other public repositories, the study develops a fully open workflow implemented in Jupyter Notebooks, integrating data collection, preprocessing, and analysis in a transparent manner. The proposed framework employs a range of Python libraries including rasterio, geopandas, xarray, numpy, and matplotlib for raster manipulation and visualization, alongside earthengine-api and sentinelsat for automated data retrieval. Additional tools such as scikit-image, pyresample, and spectral are used for image correction, resampling, and classification. Vegetation indices such as NDVI, EVI, SAVI, and NDWI are computed to assess vegetation health, spatial variability, and temporal changes across selected regions. The study highlights the advantages of the Python ecosystem in enabling reproducible, scalable, and cost-effective remote sensing analyses without reliance on proprietary software. The integration of open data, open-source libraries, and interactive notebooks supports FAIR data principles (Findable, Accessible, Interoperable, Reusable), encouraging transparency and collaboration in environmental research. The results confirm that Python-based tools provide a powerful foundation for vegetation monitoring, sustainable land management, and long-term environmental change detection. Keywords: Python, remote sensing, vegetation indices, NDVI, EVI, SAVI, NDWI, Sentinel-2, Copernicus, open-source, Jupyter Notebooks, environmental monitoring, free satellite data, FAIR data principles.

spatial database, PostgreSQL, PostGIS, QGIS, QField, urban green areas, database normalization, spatial triggers, centroid calculation, spatial automation, area computation.

geodesy engineering

Presentation: poster