Sustainable Urban Development Requires Detailed Understanding of Green Infrastructure Patterns

In a recent study published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, researchers from Transilvania University have assessed the patterns of green infrastructure in urban and peri-urban areas of two Romanian cities, Brasov and Oradea. The study used morphological spatial pattern analysis (MSPA) and satellite imagery to evaluate the green infrastructure patterns in these cities, highlighting the importance of understanding GI for sustainable urban development. The researchers found that maintaining a high percentage of green infrastructure contributes to well-being by regulating microclimatic parameters, reducing air pollution, and supporting public health.

Key Takeaways:

• The study assessed the patterns of green infrastructure in urban and peri-urban areas of Brasov and Oradea, Romania, using morphological spatial pattern analysis (MSPA) and satellite imagery.
• The researchers found that maintaining a high percentage of green infrastructure contributes to well-being by regulating microclimatic parameters, reducing air pollution, and supporting public health.
• The study used data from multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) satellite imagery, alongside vegetation indices (NDVI, EVI, NDBI, SAVI, and NDWI) to evaluate the green infrastructure patterns.
• The classification of land use/land cover (LULC) was performed using the gradient tree boosting machine learning algorithm, achieving overall accuracies of 96.02% for Brasov and 95.67% for Oradea.
• The study found that Brasov had a moderately equitable green infrastructure distribution (0.325) in urban and peri-urban areas, while Oradea displayed a more unequal distribution (0.519).
• The researchers identified core areas as the largest spatial extent in both cities, with Brasov covering 73.1% (urban) and 84.1% (urban and peri-urban), while Oradea covered 59.0% (urban) and 66.5% (urban and peri-urban).
• The study found that the green infrastructure edge pattern in Oradea was more complex, indicating higher fragmentation.

Statistics:

• The study achieved overall accuracies of 96.02% for Brasov and 95.67% for Oradea in the classification of land use/land cover (LULC) using the gradient tree boosting machine learning algorithm.
• The green infrastructure distribution in Brasov was found to be moderately equitable (0.325) in urban and peri-urban areas.
• The green infrastructure distribution in Oradea was found to be more unequal (0.519).
• The core areas in Brasov covered 73.1% (urban) and 84.1% (urban and peri-urban) of the total area.
• The core areas in Oradea covered 59.0% (urban) and 66.5% (urban and peri-urban) of the total area.

Sources:

• Assessment of Urban and Peri-Urban Green Infrastructure Patterns Using Morphological Spatial Pattern Analysis and Satellite Imagery: Case Studies of Brasov and Oradea, Romania. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025,18():25087-25109.
• Transilvania University, Remote Sensing, Machine Learning, Pattern Analysis, Emerging Technologies.